
import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_A  					= [nn.LayerNorm]

_A  					= logging.get_logger(__name__)

_A  					= version.parse(version.parse(torch.__version__).base_version)

_A  					= parsed_torch_version_base >= version.parse("2.0")
_A  					= parsed_torch_version_base >= version.parse("1.12")
_A  					= parsed_torch_version_base >= version.parse("1.11")
_A  					= parsed_torch_version_base < version.parse("1.11")



def 						lowercase_			(   A__ ,		A__ ,		A__ ,		A__ ,		self		)							->							List[str]:
    """simple docstring"""

    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(A__ ,		A__ ,		parent.dim ,		self		)
    else:
        return _softmax_backward_data(A__ ,		A__ ,		parent.dim ,		self.dtype		)



def 						lowercase_			(   A__ ,		A__ ,		A__ = 0		)							->							nn.Linear:
    """simple docstring"""

    snake_case      =					index.to(layer.weight.device		)
    snake_case      =					layer.weight.index_select(A__ ,		A__		).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            snake_case      =					layer.bias.clone().detach()
        else:
            snake_case      =					layer.bias[index].clone().detach()
    snake_case      =					list(layer.weight.size()		)
    snake_case      =					len(A__		)
    snake_case      =					nn.Linear(new_size[1] ,		new_size[0] ,		bias=layer.bias is not None		).to(layer.weight.device		)
    snake_case      =					False
    new_layer.weight.copy_(W.contiguous()		)
    snake_case      =					True
    if layer.bias is not None:
        snake_case      =					False
        new_layer.bias.copy_(b.contiguous()		)
        snake_case      =					True
    return new_layer







class      lowerCamelCase		(  nn.Module  ):





     def __init__(self      :    Optional[Any]				,    _A      :    Union[str, Any]				,    _A      :    str   )						->						Optional[Any]:
         super().__init__()
         snake_case      =					nf
         snake_case      =					nn.Parameter(torch.empty(_A				,    _A   )   )
         snake_case      =					nn.Parameter(torch.zeros(_A   )   )
         nn.init.normal_(self.weight				,    std=0.02   )





     def 		UpperCAmelCase(self      :    List[str]				,    _A      :    Optional[int]   )						->						Union[str, Any]:
         snake_case      =					x.size()[:-1] + (self.nf,)
         snake_case      =					torch.addmm(self.bias				,    x.view(-1				,    x.size(-1   )   )				,    self.weight   )
         snake_case      =					x.view(_A   )
         return x



def 						lowercase_			(   A__ ,		A__ ,		A__ = 1		)							->							ConvaD:
    """simple docstring"""

    snake_case      =					index.to(layer.weight.device		)
    snake_case      =					layer.weight.index_select(A__ ,		A__		).clone().detach()
    if dim == 0:
        snake_case      =					layer.bias.clone().detach()
    else:
        snake_case      =					layer.bias[index].clone().detach()
    snake_case      =					list(layer.weight.size()		)
    snake_case      =					len(A__		)
    snake_case      =					ConvaD(new_size[1] ,		new_size[0]		).to(layer.weight.device		)
    snake_case      =					False
    new_layer.weight.copy_(W.contiguous()		)
    snake_case      =					True
    snake_case      =					False
    new_layer.bias.copy_(b.contiguous()		)
    snake_case      =					True
    return new_layer



def 						lowercase_			(   A__ ,		A__ ,		A__ = None		)							->							Union[nn.Linear, ConvaD]:
    """simple docstring"""

    if isinstance(A__ ,		nn.Linear		):
        return prune_linear_layer(A__ ,		A__ ,		dim=0 if dim is None else dim		)
    elif isinstance(A__ ,		A__		):
        return prune_convad_layer(A__ ,		A__ ,		dim=1 if dim is None else dim		)
    else:
        raise ValueError(F'Can\'t prune layer of class {layer.__class__}'		)



def 						lowercase_			(   A__ ,		A__ ,		A__ ,		*A__		)							->							torch.Tensor:
    """simple docstring"""

    assert len(A__		) > 0, F'{input_tensors} has to be a tuple/list of tensors'

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    snake_case      =					len(inspect.signature(A__		).parameters		)
    if num_args_in_forward_chunk_fn != len(A__		):
        raise ValueError(
            F'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(A__		)} input '
            "tensors are given"		)

    if chunk_size > 0:
        snake_case      =					input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    F'All input tenors have to be of the same shape: {tensor_shape}, '
                    F'found shape {input_tensor.shape[chunk_dim]}'		)

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                F'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
                F'size {chunk_size}'		)

        snake_case      =					input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        snake_case      =					tuple(input_tensor.chunk(A__ ,		dim=A__		) for input_tensor in input_tensors		)
        # apply forward fn to every tuple
        snake_case      =					tuple(forward_fn(*A__		) for input_tensors_chunk in zip(*A__		)		)
        # concatenate output at same dimension
        return torch.cat(A__ ,		dim=A__		)

    return forward_fn(*A__		)



def 						lowercase_			(   A__ ,		A__ ,		A__ ,		A__		)							->							Tuple[Set[int], torch.LongTensor]:
    """simple docstring"""

    snake_case      =					torch.ones(A__ ,		A__		)
    snake_case      =					set(A__		) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        snake_case      =					head - sum(1 if h < head else 0 for h in already_pruned_heads		)
        snake_case      =					0
    snake_case      =					mask.view(-1		).contiguous().eq(1		)
    snake_case      =					torch.arange(len(A__		)		)[mask].long()
    return heads, index



def 						lowercase_			(   *A__ ,		A__ = None		)							->							Tuple[torch.Tensor, ...]:
    """simple docstring"""

    return torch.meshgrid(*A__ ,		indexing=A__		)





def 						lowercase_			(   A__		)							->							Tuple[torch.device, int, int]:
    """simple docstring"""

    return tensor.device, storage_ptr(A__		), storage_size(A__		)



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A  					= logging.get_logger(__name__)

_A  					= {
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"bigbird_pegasus"
     UpperCAmelCase__				:							Tuple           =		["past_key_values"]
     UpperCAmelCase__				:							Optional[Any]           =		{
         "num_attention_heads": "encoder_attention_heads",
         "hidden_size": "d_model",
         "attention_probs_dropout_prob": "attention_dropout",
     }





     def __init__(self      :    Any				,    _A      :    List[str]=9_6_1_0_3				,    _A      :    List[Any]=4_0_9_6				,    _A      :    str=1_6				,    _A      :    Tuple=4_0_9_6				,    _A      :    List[str]=1_6				,    _A      :    Optional[int]=1_6				,    _A      :    List[Any]=4_0_9_6				,    _A      :    Dict=1_6				,    _A      :    Tuple=0.0				,    _A      :    List[str]=0.0				,    _A      :    Dict=True				,    _A      :    int=True				,    _A      :    List[Any]="gelu_new"				,    _A      :    int=1_0_2_4				,    _A      :    int=0.1				,    _A      :    List[Any]=0.0				,    _A      :    Optional[int]=0.0				,    _A      :    Any=0.02				,    _A      :    Dict=2				,    _A      :    List[Any]=0.0				,    _A      :    Any=True				,    _A      :    str=0				,    _A      :    Dict=2				,    _A      :    Any=1				,    _A      :    List[str]="block_sparse"				,    _A      :    int=6_4				,    _A      :    Optional[int]=3				,    _A      :    List[Any]=False				,    **_A      :    Tuple				,    )						->						Any:
         snake_case      =					vocab_size
         snake_case      =					max_position_embeddings
         snake_case      =					d_model
         snake_case      =					encoder_ffn_dim
         snake_case      =					encoder_layers
         snake_case      =					encoder_attention_heads
         snake_case      =					decoder_ffn_dim
         snake_case      =					decoder_layers
         snake_case      =					decoder_attention_heads
         snake_case      =					dropout
         snake_case      =					attention_dropout
         snake_case      =					activation_dropout
         snake_case      =					activation_function
         snake_case      =					init_std
         snake_case      =					encoder_layerdrop
         snake_case      =					decoder_layerdrop
         snake_case      =					classifier_dropout
         snake_case      =					use_cache
         snake_case      =					encoder_layers
         snake_case      =					scale_embedding  # scale factor will be sqrt(d_model) if True

         # extra config
         snake_case      =					attention_type
         snake_case      =					block_size
         snake_case      =					num_random_blocks
         snake_case      =					use_bias

         super().__init__(
             pad_token_id=_A				,    bos_token_id=_A				,    eos_token_id=_A				,    is_encoder_decoder=_A				,    decoder_start_token_id=_A				,    **_A				,    )







class      lowerCamelCase		(  A_  ):





     @property
     def 		UpperCAmelCase(self      :    Any   )						->						Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]   )

             if self.use_past:
                 snake_case      =					{0: "batch"}
                 snake_case      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                 snake_case      =					{0: "batch", 1: "decoder_sequence"}
                 snake_case      =					{0: "batch", 1: "decoder_sequence"}

             if self.use_past:
                 self.fill_with_past_key_values_(_A				,    direction="inputs"   )
         elif self.task == "causal-lm":
             # TODO: figure this case out.
             snake_case      =					OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]   )
             if self.use_past:
                 snake_case     ,							snake_case      =					self.num_layers
                 for i in range(_A   ):
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
         else:
             snake_case      =					OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                     ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                     ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                 ]   )

         return common_inputs





     @property
     def 		UpperCAmelCase(self      :    str   )						->						Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					super().outputs
         else:
             snake_case      =					super(_A				,    self   ).outputs
             if self.use_past:
                 snake_case     ,							snake_case      =					self.num_layers
                 for i in range(_A   ):
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
                     snake_case      =					{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs





     def 		UpperCAmelCase(self      :    Any				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         snake_case      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A				,    _A				,    _A				,    _A				,    _A   )

         # Generate decoder inputs
         snake_case      =					seq_length if not self.use_past else 1
         snake_case      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A				,    _A				,    _A				,    _A				,    _A   )
         snake_case      =					{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         snake_case      =					dict(**_A				,    **_A   )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
             else:
                 import torch
             snake_case     ,							snake_case      =					common_inputs["input_ids"].shape
             snake_case      =					common_inputs["decoder_input_ids"].shape[1]
             snake_case     ,							snake_case      =					self.num_attention_heads
             snake_case      =					(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             snake_case      =					decoder_seq_length + 3
             snake_case      =					(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             snake_case      =					torch.cat(
                 [common_inputs["decoder_attention_mask"], torch.ones(_A				,    _A   )]				,    dim=1   )

             snake_case      =					[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             snake_case     ,							snake_case      =					self.num_layers
             snake_case      =					min(_A				,    _A   )
             snake_case      =					max(_A				,    _A   ) - min_num_layers
             snake_case      =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

             for _ in range(_A   ):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_A   ),
                         torch.zeros(_A   ),
                         torch.zeros(_A   ),
                         torch.zeros(_A   ),
                     )   )
             # TODO: test this.
             snake_case      =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
             for _ in range(_A				,    _A   ):
                 common_inputs["past_key_values"].append((torch.zeros(_A   ), torch.zeros(_A   ))   )
         return common_inputs





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         snake_case      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _A				,    _A				,    _A				,    _A				,    _A   )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
             else:
                 import torch
             snake_case     ,							snake_case      =					common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             snake_case      =					seqlen + 2
             snake_case     ,							snake_case      =					self.num_layers
             snake_case     ,							snake_case      =					self.num_attention_heads
             snake_case      =					(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             snake_case      =					common_inputs["attention_mask"].dtype
             snake_case      =					torch.cat(
                 [common_inputs["attention_mask"], torch.ones(_A				,    _A				,    dtype=_A   )]				,    dim=1   )
             snake_case      =					[
                 (torch.zeros(_A   ), torch.zeros(_A   )) for _ in range(_A   )
             ]
         return common_inputs





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         snake_case      =					compute_effective_axis_dimension(
             _A				,    fixed_dimension=OnnxConfig.default_fixed_batch				,    num_token_to_add=0   )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         snake_case      =					tokenizer.num_special_tokens_to_add(_A   )
         snake_case      =					compute_effective_axis_dimension(
             _A				,    fixed_dimension=OnnxConfig.default_fixed_sequence				,    num_token_to_add=_A   )

         # Generate dummy inputs according to compute batch and sequence
         snake_case      =					[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
         snake_case      =					dict(tokenizer(_A				,    return_tensors=_A   )   )
         return common_inputs





     def 		UpperCAmelCase(self      :    Any				,    _A      :    PreTrainedTokenizer				,    _A      :    int = -1				,    _A      :    int = -1				,    _A      :    bool = False				,    _A      :    Optional[TensorType] = None				,    )						->						Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _A				,    batch_size=_A				,    seq_length=_A				,    is_pair=_A				,    framework=_A   )

         elif self.task == "causal-lm":
             snake_case      =					self._generate_dummy_inputs_for_causal_lm(
                 _A				,    batch_size=_A				,    seq_length=_A				,    is_pair=_A				,    framework=_A   )
         else:
             snake_case      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _A				,    batch_size=_A				,    seq_length=_A				,    is_pair=_A				,    framework=_A   )

         return common_inputs





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    str				,    _A      :    Union[str, Any]				,    _A      :    Tuple				,    _A      :    List[Any]   )						->						Any:
         if self.task in ["default", "seq2seq-lm"]:
             snake_case      =					super()._flatten_past_key_values_(_A				,    _A				,    _A				,    _A   )
         else:
             snake_case      =					super(_A				,    self   )._flatten_past_key_values_(
                 _A				,    _A				,    _A				,    _A   )

