# coding=utf-8
# Copyright 2023 the Falcon authors and HuggingFace Inc. team.  All rights reserved.
#
# 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 Falcon model."""

import math
from typing import Optional, Tuple, Union

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

from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_falcon import FalconConfig


logger = logging.get_logger(__name__)

FALCON_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "tiiuae/falcon-40b",
    "tiiuae/falcon-40b-instruct",
    "tiiuae/falcon-7b",
    "tiiuae/falcon-7b-instruct",
    "tiiuae/falcon-rw-7b",
    "tiiuae/falcon-rw-1b",
]
_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b"
_CONFIG_FOR_DOC = "FalconConfig"


# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
class FalconLinear(nn.Linear):
    def forward(self, input: torch.Tensor) -> torch.Tensor:
        hidden_states = input @ self.weight.T
        if self.bias is None:
            return hidden_states
        return hidden_states + self.bias


# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
def rotate_half(x):
    x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


class FalconRotaryEmbedding(nn.Module):
    """Implementation of RotaryEmbedding from GPT-NeoX.
    This implementation is designed to operate on queries and keys that are compatible with `[batch_size,
    n_heads_per_partition, seq_len, head_dim]` (e.g. MinGPTAttention format).
    """

    def __init__(self, head_dim: int, base=10000):
        super().__init__()
        inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.head_dim = head_dim
        self.seq_len_cached = -1
        self.cos_cached: torch.Tensor | None = None
        self.sin_cached: torch.Tensor | None = None

    def cos_sin(self, seq_len: int, past_key_values_length: int, device="cpu", dtype=torch.bfloat16) -> torch.Tensor:
        total_length = seq_len + past_key_values_length
        if total_length > self.seq_len_cached:
            self.seq_len_cached = total_length
            t = torch.arange(total_length, device=device, dtype=self.inv_freq.dtype)
            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
            emb = torch.cat((freqs, freqs), dim=-1).to(device)

            if dtype in [torch.float16, torch.bfloat16]:
                emb = emb.float()

            self.cos_cached = emb.cos()[None, :, :]
            self.sin_cached = emb.sin()[None, :, :]

            self.cos_cached = self.cos_cached.type(dtype)
            self.sin_cached = self.sin_cached.type(dtype)

        return (
            self.cos_cached[:, past_key_values_length : seq_len + past_key_values_length],
            self.sin_cached[:, past_key_values_length : seq_len + past_key_values_length],
        )

    def forward(self, query, key, past_key_values_length=0):
        batch, seq_len, head_dim = query.shape
        cos, sin = self.cos_sin(seq_len, past_key_values_length, query.device, query.dtype)
        return (query * cos) + (rotate_half(query) * sin), (key * cos) + (rotate_half(key) * sin)


def _make_causal_mask(
    input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
) -> torch.BoolTensor:
    """
    Make causal mask used for self-attention. This mask does not take the existing attention mask into account - it
    just blocks tokens from attending forwards in the sequence. The output shape will be `[batch_size, 1,
    target_length, target_length+past_key_values_length]`.
    """
    batch_size, target_length = input_ids_shape

    mask = torch.triu(torch.ones((target_length, target_length), dtype=torch.bool, device=device), diagonal=1)
    # If past_key_values_length is 0 this is an empty tensor and the concatenation is a no-op.
    # This code style is an unfortunate consequence of getting your TF engineer to port models; doing it this
    # way avoids a data-dependent conditional, which will help me when I have to port this to XLA later.
    past_mask = torch.zeros((target_length, past_key_values_length), dtype=torch.bool, device=device)
    mask = torch.cat([past_mask, mask], dim=-1)
    expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
    return expanded_mask


def _expand_mask(mask: torch.Tensor, past_key_values_length: int) -> torch.BoolTensor:
    """
    Expands attention_mask from `[batch_size, seq_length]` to `[batch_size, 1, seq_length, seq_length + past_length]`.
    """
    batch_size, total_length = mask.shape
    seq_length = total_length - past_key_values_length if past_key_values_length is not None else total_length

    expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
    return expanded_mask.expand(batch_size, 1, seq_length, total_length)


def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.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].bfloat16() * arange_tensor
    return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)


# Copied from transformers.models.bloom.modeling_bloom.dropout_add
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
    """
    Dropout add function
    Args:
        x (`torch.tensor`, *required*):
            input tensor
        residual (`torch.tensor`, *required*):
            residual tensor
        prob (`float`, *required*):
            dropout probability
        training (`bool`, *required*):
            training mode
    """
    out = F.dropout(x, p=prob, training=training)
    out = residual + out
    return out


class FalconAttention(nn.Module):
    def __init__(self, config: FalconConfig):
        super().__init__()

        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        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})."
            )

        self.maybe_rotary = FalconRotaryEmbedding(config.head_dim) if config.rotary else lambda q, k, t: (q, k)

        # Layer-wise attention scaling
        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
        self.beta = self.inv_norm_factor
        if config.new_decoder_architecture:
            qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
        elif config.multi_query:
            qkv_out_dim = self.hidden_size + 2 * self.head_dim
        else:
            qkv_out_dim = 3 * self.hidden_size
        self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias)
        self.new_decoder_architecture = config.new_decoder_architecture
        self.multi_query = config.multi_query
        self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias)
        self.attention_dropout = nn.Dropout(config.attention_dropout)
        self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1

    def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv`
        Args:
            fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
        Returns:
            query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
            value: [batch_size, seq_length, num_heads, head_dim]
        """
        if self.new_decoder_architecture:
            batch, seq_len, _ = fused_qkv.shape
            qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim)
            query = qkv[:, :, :, :-2]
            key = qkv[:, :, :, [-2]]
            value = qkv[:, :, :, [-1]]
            key = torch.broadcast_to(key, query.shape)
            value = torch.broadcast_to(value, query.shape)

            query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
            return query, key, value
        elif not self.multi_query:
            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)
            return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
        else:
            batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
            fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
            return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]

    # Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
        """
        Merge heads together over the last dimenstion
        Args:
            x (`torch.tensor`, *required*): [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,
        alibi: Optional[torch.Tensor],
        attention_mask: torch.Tensor,
        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
    ):
        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
        num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
        # 3 x [batch_size, seq_length, num_heads, head_dim]
        (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)

        batch_size, query_length, _, _ = query_layer.shape

        query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, query_length, self.head_dim)
        key_layer = key_layer.transpose(1, 2).reshape(
            batch_size * num_kv_heads,
            query_length,
            self.head_dim,
        )
        value_layer = value_layer.transpose(1, 2).reshape(batch_size * num_kv_heads, query_length, self.head_dim)

        past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
        query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)

        if layer_past is not None:
            past_key, past_value = layer_past
            # concatenate along seq_length dimension:
            #  - key: [batch_size * self.num_heads, kv_length, head_dim]
            #  - value: [batch_size * self.num_heads, kv_length, head_dim]
            key_layer = torch.cat((past_key, key_layer), dim=1)
            value_layer = torch.cat((past_value, value_layer), dim=1)

        _, kv_length, _ = key_layer.shape
        if use_cache:
            present = (key_layer, value_layer)
        else:
            present = None

        attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)

        query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
        key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
        value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)

        if alibi is None:
            if output_attentions:
                # F.scaled_dot_product_attention doesn't return the attention weights, so we have
                # to do it by hand if we want them
                attention_scores = query_layer_ @ key_layer_.transpose(-1, -2)
                attention_scores /= math.sqrt(self.head_dim)

                attention_scores = F.softmax(
                    attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype
                )
                attn_output = attention_scores @ value_layer_
            else:
                attn_output = F.scaled_dot_product_attention(
                    query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False
                )
                attention_scores = None

            attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
            attn_output = attn_output.permute(0, 2, 1, 3)
            attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)

            output_tensor = self.dense(attn_output)

            if output_attentions:
                return output_tensor, present, attention_scores
            else:
                return output_tensor, present

        else:
            matmul_result = query_layer_ @ key_layer_.transpose(-1, -2)

            # change view to [batch_size, num_heads, q_length, kv_length]
            attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)

            # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
            input_dtype = attention_scores.dtype
            # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
            if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
                attention_scores = attention_scores.to(torch.float32)
            # Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by
            # adding (alibi * self.inv_norm_factor) to attention_mask_float. I think this would be mathematically
            # equivalent and more performant, but there might be a numerical difference. If you're reading this
            # and you'd like to experiment and maybe file a PR, feel free!
            attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
            attention_logits *= self.inv_norm_factor
            attention_probs = F.softmax(attention_logits + attention_mask_float, dim=-1, dtype=hidden_states.dtype)
            # [batch_size, num_heads, q_length, kv_length]
            attention_probs = self.attention_dropout(attention_probs)

            if head_mask is not None:
                attention_probs = attention_probs * head_mask

            # change view [batch_size, num_heads, q_length, kv_length]
            attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)

            # matmul: [batch_size * num_heads, q_length, head_dim]
            context_layer = (attention_probs_reshaped @ value_layer_).flatten(0, 1)

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

            output_tensor = self.dense(context_layer)

            if output_attentions:
                return output_tensor, present, attention_probs
            else:
                return output_tensor, present


class FalconMLP(nn.Module):
    def __init__(self, config: FalconConfig):
        super().__init__()
        hidden_size = config.hidden_size

        self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias)
        self.act = nn.GELU()
        self.dense_4h_to_h = FalconLinear(4 * hidden_size, hidden_size, bias=config.bias)
        self.hidden_dropout = config.hidden_dropout

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.act(self.dense_h_to_4h(x))
        x = self.dense_4h_to_h(x)
        return x


class FalconDecoderLayer(nn.Module):
    def __init__(self, config: FalconConfig):
        super().__init__()
        hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.self_attention = FalconAttention(config)
        self.mlp = FalconMLP(config)
        self.hidden_dropout = config.hidden_dropout
        self.config = config

        if config.new_decoder_architecture:
            # The layer norm before self-attention
            self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
            # The layer norm before the MLP
            self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        else:
            self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
            if not config.parallel_attn:
                self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

    def forward(
        self,
        hidden_states: torch.Tensor,
        alibi: Optional[torch.Tensor],
        attention_mask: torch.Tensor,
        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
    ):
        residual = hidden_states

        if self.config.new_decoder_architecture:
            attention_layernorm_out = self.ln_attn(hidden_states)
            mlp_layernorm_out = self.ln_mlp(hidden_states)
        else:
            attention_layernorm_out = self.input_layernorm(hidden_states)

        # Self attention.
        attn_outputs = self.self_attention(
            attention_layernorm_out,
            layer_past=layer_past,
            attention_mask=attention_mask,
            alibi=alibi,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )

        attention_output = attn_outputs[0]

        if not self.config.new_decoder_architecture:
            if self.config.parallel_attn:
                mlp_layernorm_out = attention_layernorm_out
            else:
                residual = dropout_add(
                    attention_output, residual, self.config.attention_dropout, training=self.training
                )
                mlp_layernorm_out = self.post_attention_layernorm(residual)

        outputs = attn_outputs[1:]

        # MLP.
        mlp_output = self.mlp(mlp_layernorm_out)

        if self.config.new_decoder_architecture or self.config.parallel_attn:
            mlp_output += attention_output

        output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)

        if use_cache:
            outputs = (output,) + outputs
        else:
            outputs = (output,) + outputs[1:]

        return outputs  # hidden_states, present, attentions


FALCON_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config ([`FalconConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

FALCON_INPUTS_DOCSTRING = r"""
    Args:
        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[0][0].shape[2]`
            (`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)
        past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`):
            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
            `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
            their past given to this model should not be passed as `input_ids` as they have already been computed.
            Each element of `past_key_values` is a tuple (past_key, past_value):
            - past_key: [batch_size * num_heads, head_dim, kv_length]
            - past_value: [batch_size * num_heads, kv_length, head_dim]
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
            `past_key_values`).
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""


class FalconPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = FalconConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["FalconDecoderLayer"]

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module: nn.Module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear) or isinstance(module, FalconLinear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    # Copied from transformers.models.bloom.modeling_bloom.BloomPreTrainedModel._set_gradient_checkpointing with BloomModel->FalconModel
    def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
        if isinstance(module, FalconModel):
            module.gradient_checkpointing = value

    @staticmethod
    def _convert_cache_to_standard_format(
        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
        """
        Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
        num_heads, ...]))
        """
        batch_size_times_num_heads, kv_length, head_dim = past_key_value[0][0].shape
        # [batch_size * self.num_heads, kv_length, head_dim] -> [batch_size, num_heads, kv_length, head_dim]
        # Note that don't want to use self.num_attention_heads because the number of heads may vary depending
        # on whether we use multi_query attention.
        num_heads = batch_size_times_num_heads // batch_size
        return tuple(
            (
                layer_past[0].view(batch_size, num_heads, kv_length, head_dim),
                layer_past[1].view(batch_size, num_heads, kv_length, head_dim),
            )
            for layer_past in past_key_value
        )

    @staticmethod
    def _convert_to_rw_cache(
        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
        batch_size, num_heads, kv_length, head_dim = past_key_value[0][0].shape
        batch_size_times_num_heads = batch_size * num_heads
        # [batch_size, num_heads, kv_length, head_dim] -> [batch_size * num_heads, kv_length, head_dim]
        return tuple(
            (
                layer_past[0].view(batch_size_times_num_heads, kv_length, head_dim),
                layer_past[1].view(batch_size_times_num_heads, kv_length, head_dim),
            )
            for layer_past in past_key_value
        )


@add_start_docstrings(
    "The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.",
    FALCON_START_DOCSTRING,
)
class FalconModel(FalconPreTrainedModel):
    def __init__(self, config: FalconConfig):
        super().__init__(config)