






"""simple docstring"""







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     			=  [nn.LayerNorm]

_UpperCamelCase     			=  logging.get_logger(__name__)

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

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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       self							):







      """simple docstring"""






      from torch import _softmax_backward_data

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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case = 0							):







      """simple docstring"""






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







class 					lowerCamelCase__					(       nn.Module ):






    def __init__( self	,A	,A					):
          super().__init__()
          UpperCAmelCase							=       nf
          UpperCAmelCase							=       nn.Parameter(torch.empty(A	,A					)					)
          UpperCAmelCase							=       nn.Parameter(torch.zeros(A					)					)
          nn.init.normal_(self.weight	,std=0.02					)






    def 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       x.size()[:-1] + (self.nf,)
          UpperCAmelCase							=       torch.addmm(self.bias	,x.view(-1	,x.size(-1					)					)	,self.weight					)
          UpperCAmelCase							=       x.view(A					)
          return x







def _a (		_snake_case      ,       _snake_case      ,       _snake_case = 1							):







      """simple docstring"""






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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case = None							):







      """simple docstring"""






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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       *_snake_case							):







      """simple docstring"""






      assert len(_snake_case							) > 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
      UpperCAmelCase							=       len(inspect.signature(_snake_case							).parameters							)
      if num_args_in_forward_chunk_fn != len(_snake_case							):
            raise ValueError(
                F'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_snake_case							)} input '''
                """tensors are given"""							)

      if chunk_size > 0:
            UpperCAmelCase							=       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}'''							)

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

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

      return forward_fn(*_snake_case							)

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       torch.ones(_snake_case      ,       _snake_case							)
      UpperCAmelCase							=       set(_snake_case							) - 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
            UpperCAmelCase							=       head - sum(1 if h < head else 0 for h in already_pruned_heads							)
            UpperCAmelCase							=       0
      UpperCAmelCase							=       mask.view(-1							).contiguous().eq(1							)
      UpperCAmelCase							=       torch.arange(len(_snake_case							)							)[mask].long()
      return heads, index

def _a (		*_snake_case      ,       _snake_case = None							):







      """simple docstring"""






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






def _a (		_snake_case							):







      """simple docstring"""






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











"""simple docstring"""







import sys

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      if not numbers:
            return 0
      if target == 0 and target in numbers:
            return 0
      if not isinstance(_snake_case      ,       (list, tuple)							) or not all(
          isinstance(_snake_case      ,       _snake_case							) for number in numbers							):
            raise ValueError("""numbers must be an iterable of integers"""							)

      UpperCAmelCase							=       UpperCAmelCase							=       UpperCAmelCase							=       0
      UpperCAmelCase							=       sys.maxsize

      while right < len(_snake_case							):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                  UpperCAmelCase							=       min(_snake_case      ,       right - left + 1							)
                  curr_sum -= numbers[left]
                  left += 1
            right += 1

      return 0 if min_len == sys.maxsize else min_len



