




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   (						lowerCAmelCase_     :					Any   ,				lowerCAmelCase_     :					Tuple   ,				lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					Dict   ,				self     :					Optional[int]			):
     """simple docstring"""

     from torch import _softmax_backward_data

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



def 							_A   (						lowerCAmelCase_     :					nn.Linear   ,				lowerCAmelCase_     :					torch.LongTensor   ,				lowerCAmelCase_     :					int = 0			):
     """simple docstring"""

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





class  __lowerCamelCase       (    nn.Module    ):




    """simple docstring"""

    def __init__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   )      ->       Any:
         super().__init__()
         lowerCAmelCase__		= nf
         lowerCAmelCase__		= nn.Parameter(torch.empty(__snake_case   ,	__snake_case   )   )
         lowerCAmelCase__		= nn.Parameter(torch.zeros(__snake_case   )   )
         nn.init.normal_(self.weight   ,	std=0.02   )


    def    a      (       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   )      ->       int:
         lowerCAmelCase__		= x.size()[:-1] + (self.nf,)
         lowerCAmelCase__		= torch.addmm(self.bias   ,	x.view(-1   ,	x.size(-1   )   )   ,	self.weight   )
         lowerCAmelCase__		= x.view(__snake_case   )
         return x






def 							_A   (						lowerCAmelCase_     :					ConvaD   ,				lowerCAmelCase_     :					torch.LongTensor   ,				lowerCAmelCase_     :					int = 1			):
     """simple docstring"""

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



def 							_A   (						lowerCAmelCase_     :					Union[nn.Linear, ConvaD]   ,				lowerCAmelCase_     :					torch.LongTensor   ,				lowerCAmelCase_     :					Optional[int] = None			):
     """simple docstring"""

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



def 							_A   (						lowerCAmelCase_     :					Callable[..., torch.Tensor]   ,				lowerCAmelCase_     :					int   ,				lowerCAmelCase_     :					int   ,				*lowerCAmelCase_     :					Any			):
     """simple docstring"""

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

     if chunk_size > 0:
          lowerCAmelCase__		= 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}'			)

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

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

     return forward_fn(*__UpperCamelCase			)



def 							_A   (						lowerCAmelCase_     :					List[int]   ,				lowerCAmelCase_     :					int   ,				lowerCAmelCase_     :					int   ,				lowerCAmelCase_     :					Set[int]			):
     """simple docstring"""

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



def 							_A   (						*lowerCAmelCase_     :					Union[torch.Tensor, List[torch.Tensor]]   ,				lowerCAmelCase_     :					Optional[str] = None			):
     """simple docstring"""

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






def 							_A   (						lowerCAmelCase_     :					torch.Tensor			):
     """simple docstring"""

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






from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


_lowerCamelCase			 =						datasets.utils.logging.get_logger(__name__)



class 			a     (   folder_based_builder.FolderBasedBuilderConfig			):

     '''simple docstring'''





     lowerCAmelCase				:       bool   		=  None
     lowerCAmelCase				:       bool   		=  None



class 			a     (   folder_based_builder.FolderBasedBuilder			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  datasets.Image()
     lowerCAmelCase				:       List[Any]   		=  'image'
     lowerCAmelCase				:       List[Any]   		=  ImageFolderConfig
     lowerCAmelCase				:       List[str]  # definition at the bottom of the script
     lowerCAmelCase				:       Dict   		=  ImageClassification(image_column='image'	,   label_column='label'			)


_lowerCamelCase			 =						[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
_lowerCamelCase			 =						IMAGE_EXTENSIONS





