# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch BLOOM model."""

import math

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F

from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    auto_docstring,
    logging,
)
from .configuration_bloom import BloomConfig


logger = logging.get_logger(__name__)


def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
    """
    Link to paper: https://huggingface.co/papers/2108.12409 Alibi tensor is not causal as the original paper mentions, it
    relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value
    `softmax(l+a) = softmax(l)`. Based on
    https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742
    TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly.

    Args:
    Returns tensor shaped (batch_size * num_heads, 1, max_seq_len)
        attention_mask (`torch.Tensor`):
            Token-wise attention mask, this should be of shape (batch_size, max_seq_len).
        num_heads (`int`):
            number of heads
        dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`):
            dtype of the output tensor
    """
    batch_size, seq_length = attention_mask.shape
    closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
    base = torch.tensor(
        2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
    )
    powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
    slopes = torch.pow(base, powers)

    if closest_power_of_2 != num_heads:
        extra_base = torch.tensor(
            2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
        )
        num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
        extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
        slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)

    # Note: alibi will added to the attention bias that will be applied to the query, key product of attention
    # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
    # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
    # => the query_length dimension will then be broadcasted correctly
    # This is more or less identical to T5's relative position bias:
    # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
    arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
    alibi = slopes[..., None] * arange_tensor
    return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)


def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
    """
    Dropout add function

    Args:
        x (`torch.tensor`):
            input tensor
        residual (`torch.tensor`):
            residual tensor
        prob (`float`):
            dropout probability
        training (`bool`):
            training mode
    """
    out = F.dropout(x, p=prob, training=training)
    out = residual + out
    return out


def bloom_gelu_forward(x: torch.Tensor) -> torch.Tensor:
    """
    Custom bias GELU function. Adapted from Megatron-DeepSpeed code. Here we use a simple implementation (inference) to
    make the model jitable.

    Args:
        x (`torch.tensor`):
            input hidden states
    """
    return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))


def bloom_gelu_back(g: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
    """
    gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) +
    0.3989423 * x * torch.exp(-0.5 * x * x)

    Args:
        g (`torch.tensor`):
            gradient output tensor
        x (`torch.tensor`):
            input tensor
    """
    x = x[0]  # x is a tuple of 1 element, needs to unpack it first
    tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
    # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
    ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out)
    return ff * g


class GeLUFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, input: torch.Tensor) -> torch.Tensor:
        ctx.save_for_backward(input)
        return bloom_gelu_forward(input)

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
        input = ctx.saved_tensors
        tmp = bloom_gelu_back(grad_output, input)
        return tmp


class BloomGelu(nn.Module):
    """
    Partly copied from Megatron-DeepSpeed code and adapted for our needs
    """

    def __init__(self):
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return GeLUFunction.apply(x)


class BloomAttention(nn.Module):
    def __init__(self, config: BloomConfig, layer_idx: int | None = None):
        super().__init__()

        self.pretraining_tp = config.pretraining_tp
        self.slow_but_exact = config.slow_but_exact

        self.hidden_size = config.hidden_size
        self.num_heads = config.n_head
        self.head_dim = self.hidden_size // self.num_heads
        self.split_size = self.hidden_size
        self.hidden_dropout = config.hidden_dropout

        if self.head_dim * self.num_heads != self.hidden_size:
            raise ValueError(
                f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
                f" {self.num_heads})."
            )

        # Layer-wise attention scaling
        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
        self.beta = 1.0
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=True)
        self.dense = nn.Linear(self.hidden_size, self.hidden_size)
        self.attention_dropout = nn.Dropout(config.attention_dropout)

    def _reshape(self, fused_qkv: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Split the last dimension into (num_heads, head_dim) and reshapes to (bs, heads, len, dim) shape
        without making any copies, results share same memory storage as `fused_qkv`

        Args:
            fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim]

        Returns:
            query: [batch_size, num_heads, seq_length, head_dim]
            key: [batch_size, num_heads, seq_length, head_dim]
            value: [batch_size, num_heads, seq_length, head_dim]
        """
        batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
        fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
        query_layer = fused_qkv[..., 0, :].transpose(1, 2)
        key_layer = fused_qkv[..., 1, :].transpose(1, 2)
        value_layer = fused_qkv[..., 2, :].transpose(1, 2)
        return query_layer, key_layer, value_layer

    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
        """
        Merge heads together over the last dimension

        Args:
            x (`torch.tensor`): [batch_size * num_heads, seq_length, head_dim]

        Returns:
            torch.tensor: [batch_size, seq_length, num_heads * head_dim]
        """
        # What we want to achieve is:
        # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
        batch_size_and_num_heads, seq_length, _ = x.shape
        batch_size = batch_size_and_num_heads // self.num_heads

        # First view to decompose the batch size
        # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
        x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)

        # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
        x = x.permute(0, 2, 1, 3)

        # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
        return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        residual: torch.Tensor,
        alibi: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_past: Cache | None = None,
        use_cache: bool = False,
        output_attentions: bool = False,
        **kwargs,
    ):
        batch_size, q_length, _ = hidden_states.shape
        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
        # 3 x [batch_size, num_heads, seq_length, head_dim]
        query_layer, key_layer, value_layer = self._reshape(fused_qkv)

        if layer_past is not None:
            key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx)

        # reshape qkv for further computations
        query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
        key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
        value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)

        # [batch_size * num_heads, q_length, kv_length]
        attention_scores = alibi.baddbmm(
            batch1=query_layer,
            batch2=key_layer,
            beta=self.beta,
            alpha=self.inv_norm_factor,
        )

        # change view to [batch_size, num_heads, q_length, kv_length]
        attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
        if attention_mask is not None:
            attn_weights = attn_weights + attention_mask

        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)

        # [batch_size, num_heads, q_length, kv_length]
        attention_probs = self.attention_dropout(attention_probs)

        # change view [batch_size x num_heads, q_length, kv_length]
        attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)

        # matmul: [batch_size * num_heads, q_length, head_dim]
        context_layer = torch.bmm(attention_probs_reshaped, value_layer)

        # change view [batch_size, q_length, num_heads * head_dim]
        context_layer = self._merge_heads(context_layer)

        # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
        if self.pretraining_tp > 1 and self.slow_but_exact:
            slices = self.hidden_size / self.pretraining_tp
            output_tensor = torch.zeros_like(context_layer)
            for i in range(self.pretraining_tp):
                output_tensor = output_tensor + F.linear(
                    context_layer[:, :, int(i * slices) : int((i + 1) * slices)],
                    self.dense.weight[:, int(i * slices) : int((i + 1) * slices)],
                )
        else:
            output_tensor = self.dense(context_layer)

        output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
        return output_tensor, attention_probs


class BloomMLP(nn.Module):
    def __init__(self, config: BloomConfig):
        super().__init__()
        hidden_size = config.hidden_size

        self.pretraining_tp = config.pretraining_tp
        self.slow_but_exact = config.slow_but_exact
        self.dense_h_to_4h = nn.Linear(hidden_size, 4 * hidden_size)
        self.gelu_impl = BloomGelu()
        self.dense_4h_to_h = nn.Linear(4 * hidden_size, hidden_size)
        self.hidden_dropout = config.hidden_dropout

    def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
        hidden_states = self.gelu_impl(self.dense_h_to_4h(hidden_states))

        if self.pretraining_tp > 1 and self.slow_but_exact:
            intermediate_output = torch.zeros_like(residual)
            slices = self.dense_4h_to_h.weight.shape[-1] / self.pretraining_tp
            for i in range(self.pretraining_tp):
                intermediate_output = intermediate_output + F.linear(
                    hidden_states[:, :, int(i * slices) : int((i + 1) * slices)],
                    self.dense_4h_to_h.weight[:, int(i * slices) : int((i + 1) * slices)],
                )
        else:
            intermediate_output = self.dense_4h_to_h(hidden_states)

        output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training)

        return output


class BloomBlock(GradientCheckpointingLayer):
    def __init__(self, config: BloomConfig, layer_idx: int | None = None):
        super().__init__()
        hidden_size = config.hidden_size

        self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.num_heads = config.n_head
        self.self_attention = BloomAttention(config, layer_idx)
        self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        self.mlp = BloomMLP(config)

        self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
        self.hidden_dropout = config.hidden_dropout

    def forward(
        self,
        hidden_states: torch.Tensor,
        alibi: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_past: Cache | None = None,
        use_cache: bool = False,
        output_attentions: bool = False,
        **kwargs,
    ):
        # hidden_states: [batch_size, seq_length, hidden_size]

        # Layer norm at the beginning of the transformer layer.
        layernorm_output = self.input_layernorm(hidden_states)

        # Layer norm post the self attention.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = hidden_states

        # Self attention.
        attention_output, attn_weights = self.self_attention(
            layernorm_output,
            residual,
            layer_past=layer_past,
            attention_mask=attention_mask,
            alibi=alibi,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )

        layernorm_output = self.post_attention_layernorm(attention_output)

        # Get residual
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = attention_output

        # MLP.
        output = self.mlp(layernorm_output, residual)

        return output, attn_weights  # hidden_states, attentions


@auto_docstring
class BloomPreTrainedModel(PreTrainedModel):
    config: BloomConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BloomBlock"]
    _skip_keys_device_placement = "past_key_values"
    _can_compile_fullgraph = True


@auto_docstring
class BloomModel(BloomPreTrainedModel):
    def __init__(self, config: BloomConfig):
        super().__init__(config)

        self.embed_dim = config.hidden_size
        self.num_heads = config.n_head

        # Embedding + LN Embedding
        self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
        self.word_embeddings_layernorm = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

        # Transformer blocks
        self.h = nn.ModuleList([BloomBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])

        # Final Layer Norm
        self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def build_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
        return build_alibi_tensor(attention_mask, num_heads, dtype)

    def get_input_embeddings(self):
        return self.word_embeddings

    def set_input_embeddings(self, new_embeddings: torch.Tensor):
        self.word_embeddings = new_embeddings

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        attention_mask: torch.Tensor | None = None,
        inputs_embeds: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple[torch.Tensor, ...] | BaseModelOutputWithPastAndCrossAttentions:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
            (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.

            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
            )
            use_cache = False

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        batch_size, seq_length, _ = inputs_embeds.shape
        past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
        seq_length_with_past = seq_length + past_length

        hidden_states = self.word_embeddings_layernorm(inputs_embeds)

        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        # Compute alibi tensor: check build_alibi_tensor documentation
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
        else:
            attention_mask = attention_mask.to(hidden_states.device)

        alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
        causal_mask = create_causal_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
        )

        for i, block in enumerate(self.h):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            outputs = block(
                hidden_states,
                layer_past=past_key_values,
                attention_mask=causal_mask,
                use_cache=use_cache,
                output_attentions=output_attentions,
                alibi=alibi,
            )

            hidden_states = outputs[0]
            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[1],)

        # Add last hidden state
        hidden_states = self.ln_f(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attentions] if v is not None
            )

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


@auto_docstring(
    custom_intro="""
    The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    """
)
class BloomForCausalLM(BloomPreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_head.weight": "transformer.word_embeddings.weight"}

    def __init__(self, config: BloomConfig):
        super().__init__(config)
        self.transformer = BloomModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def set_output_embeddings(self, new_embeddings: torch.Tensor):
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        use_cache=True,
        is_first_iteration=False,
        **kwargs,
    ):
        # Overwritten because of the fixed-shape attention mask creation

        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            is_first_iteration=is_first_iteration,
            **kwargs,
        )

        # This part differs from other models because BLOOM needs a 2D mask to construct alibi tensor
        # The only difference is the usage of 2D instead of 4D mask, but the shape will be static
        if isinstance(past_key_values, StaticCache) and attention_mask is not None:
            target_length = past_key_values.get_max_cache_shape()
            batch_size, seq_length = attention_mask.shape
            diff = target_length - seq_length

            new_attn_mask = torch.zeros(batch_size, diff, device=attention_mask.device, dtype=attention_mask.dtype)
            attention_mask = torch.cat([attention_mask, new_attn_mask], dim=-1)
            model_inputs["attention_mask"] = attention_mask

        return model_inputs

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        attention_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs,
    ) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
            (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.

            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(
                logits,
                labels,
                vocab_size=self.config.vocab_size,
                num_items_in_batch=kwargs.get("num_items_in_batch"),
            )

        if not return_dict:
            output = (logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    The Bloom Model transformer with a sequence classification head on top (linear layer).

    [`BloomForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-1) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """
)
class BloomForSequenceClassification(BloomPreTrainedModel):
    def __init__(self, config: BloomConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = BloomModel(config)
        self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        attention_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple[torch.Tensor] | SequenceClassifierOutputWithPast:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
            (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.

            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            last_non_pad_token = -1
        elif input_ids is not None:
            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
        else:
            last_non_pad_token = -1
            logger.warning_once(
                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
            )

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring
class BloomForTokenClassification(BloomPreTrainedModel):
    def __init__(self, config: BloomConfig):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.transformer = BloomModel(config)
        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
            classifier_dropout = config.classifier_dropout
        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
            classifier_dropout = config.hidden_dropout
        else:
            classifier_dropout = 0.1
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        attention_mask: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple[torch.Tensor] | TokenClassifierOutput:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
            (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.

            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)

        loss = None
        if labels is not None:
            # move labels to correct device
            labels = labels.to(logits.device)
            batch_size, seq_length = labels.shape
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
            )

        if not return_dict:
            output = (logits,) + transformer_outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring
class BloomForQuestionAnswering(BloomPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.transformer = BloomModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, 2)

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.FloatTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        start_positions: torch.LongTensor | None = None,
        end_positions: torch.LongTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | QuestionAnsweringModelOutput:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
            (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.

            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = [
    "BloomForCausalLM",
    "BloomModel",
    "BloomPreTrainedModel",
    "BloomForSequenceClassification",
    "BloomForTokenClassification",
    "BloomForQuestionAnswering",
]