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


UpperCAmelCase__	:       Any			      =      [nn.LayerNorm]

UpperCAmelCase__	:       List[str]			      =      logging.get_logger(__name__)

UpperCAmelCase__	:       Dict			      =      version.parse(version.parse(torch.__version__).base_version)

UpperCAmelCase__	:       List[str]			      =      parsed_torch_version_base >= version.parse("""2.0""")
UpperCAmelCase__	:       List[Any]			      =      parsed_torch_version_base >= version.parse("""1.12""")
UpperCAmelCase__	:       str			      =      parsed_torch_version_base >= version.parse("""1.11""")
UpperCAmelCase__	:       Union[str, Any]			      =      parsed_torch_version_base < version.parse("""1.11""")






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A  ,      self     )       ->  int:
      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:
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				index.to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: List[Any]					       =				layer.weight.index_select(_A  ,      _A     ).clone().detach()
      if layer.bias is not None:
            if dim == 1:
                  SCREAMING_SNAKE_CASE	: Tuple					       =				layer.bias.clone().detach()
            else:
                  SCREAMING_SNAKE_CASE	: Dict					       =				layer.bias[index].clone().detach()
      SCREAMING_SNAKE_CASE	: Tuple					       =				list(layer.weight.size()     )
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				len(_A     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				nn.Linear(new_size[1]  ,      new_size[0]  ,      bias=layer.bias is not None     ).to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: str					       =				False
      new_layer.weight.copy_(W.contiguous()     )
      SCREAMING_SNAKE_CASE	: List[Any]					       =				True
      if layer.bias is not None:
            SCREAMING_SNAKE_CASE	: Dict					       =				False
            new_layer.bias.copy_(b.contiguous()     )
            SCREAMING_SNAKE_CASE	: Tuple					       =				True
      return new_layer







class     a__    (				nn.Module    ):







  """simple docstring"""




  def __init__(     self      :	str			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Union[str, Any]						)   ->Optional[int]:
        """simple docstring"""







        super().__init__()
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				nf
        SCREAMING_SNAKE_CASE	: Dict					       =				nn.Parameter(torch.empty(UpperCAmelCase__			,			UpperCAmelCase__						)						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				nn.Parameter(torch.zeros(UpperCAmelCase__						)						)
        nn.init.normal_(self.weight			,			std=0.02						)




  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Optional[int]						)   ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				x.size()[:-1] + (self.nf,)
        SCREAMING_SNAKE_CASE	: List[str]					       =				torch.addmm(self.bias			,			x.view(-1			,			x.size(-1						)						)			,			self.weight						)
        SCREAMING_SNAKE_CASE	: str					       =				x.view(UpperCAmelCase__						)
        return x






def    __lowercase (       _A  ,      _A  ,      _A = 1     )       ->  ConvaD:
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				index.to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: List[str]					       =				layer.weight.index_select(_A  ,      _A     ).clone().detach()
      if dim == 0:
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				layer.bias.clone().detach()
      else:
            SCREAMING_SNAKE_CASE	: Dict					       =				layer.bias[index].clone().detach()
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				list(layer.weight.size()     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				len(_A     )
      SCREAMING_SNAKE_CASE	: Dict					       =				ConvaD(new_size[1]  ,      new_size[0]     ).to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				False
      new_layer.weight.copy_(W.contiguous()     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				True
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				False
      new_layer.bias.copy_(b.contiguous()     )
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				True
      return new_layer






def    __lowercase (       _A  ,      _A  ,      _A = None     )       ->  Union[nn.Linear, ConvaD]:
      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:
      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
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				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:
            SCREAMING_SNAKE_CASE	: str					       =				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}"     )

            SCREAMING_SNAKE_CASE	: Optional[Any]					       =				input_tensors[0].shape[chunk_dim] // chunk_size

            # chunk input tensor into tuples
            SCREAMING_SNAKE_CASE	: Any					       =				tuple(input_tensor.chunk(_A  ,      dim=_A     ) for input_tensor in input_tensors     )
            # apply forward fn to every tuple
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				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]:
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				torch.ones(_A  ,      _A     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				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
            SCREAMING_SNAKE_CASE	: List[str]					       =				head - sum(1 if h < head else 0 for h in already_pruned_heads     )
            SCREAMING_SNAKE_CASE	: Tuple					       =				0
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				mask.view(-1     ).contiguous().eq(1     )
      SCREAMING_SNAKE_CASE	: torch.LongTensor					       =				torch.arange(len(_A     )     )[mask].long()
      return heads, index






def    __lowercase (       *_A  ,      _A = None     )       ->  Tuple[torch.Tensor, ...]:
      return torch.meshgrid(*_A  ,      indexing=_A     )







def    __lowercase (       _A     )       ->  Tuple[torch.device, int, int]:
      return tensor.device, storage_ptr(_A     ), storage_size(_A     )







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

UpperCAmelCase__	:       Dict			      =      [(1,)]






def    __lowercase (       )       ->  Tuple:
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"






def    __lowercase (       )       ->  Optional[Any]:
      SCREAMING_SNAKE_CASE	: Dict					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"







@require_torch
class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  @slow
  @require_torch_gpu
  def 	_lowercase (     self      :	Any						)   ->Optional[Any]:
        """simple docstring"""







        check_slow_torch_cuda()


  @require_torch_gpu
  @slow
  def 	_lowercase (     self      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        check_slow_torch_cuda()




  @parameterized.expand(UpperCAmelCase__						)
  @slow
  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Dict=None						)   ->Optional[Any]:
        """simple docstring"""







        check_slow()






@slow
@require_torch_gpu
def    __lowercase (       )       ->  Optional[int]:
      check_slow_torch_cuda()






@require_torch_gpu
@slow
def    __lowercase (       )       ->  int:
      check_slow_torch_cuda()






@slow
@pytest.mark.parametrize("""param"""  ,      [1]     )
def    __lowercase (       _A     )       ->  Optional[int]:
      check_slow()







@pytest.mark.parametrize("""param"""  ,      [1]     )
@slow
def    __lowercase (       _A     )       ->  str:
      check_slow()




