




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


_lowerCamelCase			 =						[nn.LayerNorm]

_lowerCamelCase			 =						logging.get_logger(__name__)

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

_lowerCamelCase			 =						parsed_torch_version_base >= version.parse('2.0')
_lowerCamelCase			 =						parsed_torch_version_base >= version.parse('1.12')
_lowerCamelCase			 =						parsed_torch_version_base >= version.parse('1.11')
_lowerCamelCase			 =						parsed_torch_version_base < version.parse('1.11')




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Any       ,     __UpperCamelCase			:						Tuple       ,     __UpperCamelCase			:						Dict       ,     __UpperCamelCase			:						Dict       ,     self			:						Optional[int]      )  ->							Optional[Any]:
    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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						nn.Linear       ,     __UpperCamelCase			:						torch.LongTensor       ,     __UpperCamelCase			:						int = 0      )  ->							nn.Linear:
    UpperCAmelCase_							 =     index.to(layer.weight.device      )
    UpperCAmelCase_							 =     layer.weight.index_select(__UpperCamelCase       ,     __UpperCamelCase      ).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(__UpperCamelCase      )
    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 			a     (   nn.Module			):

     '''simple docstring'''





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




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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						ConvaD       ,     __UpperCamelCase			:						torch.LongTensor       ,     __UpperCamelCase			:						int = 1      )  ->							ConvaD:
    UpperCAmelCase_							 =     index.to(layer.weight.device      )
    UpperCAmelCase_							 =     layer.weight.index_select(__UpperCamelCase       ,     __UpperCamelCase      ).clone().detach()
    if dim == 0:
        UpperCAmelCase_							 =     layer.bias.clone().detach()
    else:
        UpperCAmelCase_							 =     layer.bias[index].clone().detach()
    UpperCAmelCase_							 =     list(layer.weight.size()      )
    UpperCAmelCase_							 =     len(__UpperCamelCase      )
    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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Union[nn.Linear, ConvaD]       ,     __UpperCamelCase			:						torch.LongTensor       ,     __UpperCamelCase			:						Optional[int] = None      )  ->							Union[nn.Linear, ConvaD]:
    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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Callable[..., torch.Tensor]       ,     __UpperCamelCase			:						int       ,     __UpperCamelCase			:						int       ,     *__UpperCamelCase			:						Any      )  ->							torch.Tensor:
    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
    UpperCAmelCase_							 =     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:
        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(__UpperCamelCase       ,     dim=__UpperCamelCase      ) for input_tensor in input_tensors      )
        # apply forward fn to every tuple
        UpperCAmelCase_							 =     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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						List[int]       ,     __UpperCamelCase			:						int       ,     __UpperCamelCase			:						int       ,     __UpperCamelCase			:						Set[int]      )  ->							Tuple[Set[int], torch.LongTensor]:
    UpperCAmelCase_							 =     torch.ones(__UpperCamelCase       ,     __UpperCamelCase      )
    UpperCAmelCase_							 =     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
        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(__UpperCamelCase      )      )[mask].long()
    return heads, index




def 					SCREAMING_SNAKE_CASE	(       *__UpperCamelCase			:						Union[torch.Tensor, List[torch.Tensor]]       ,     __UpperCamelCase			:						Optional[str] = None      )  ->							Tuple[torch.Tensor, ...]:
    return torch.meshgrid(*__UpperCamelCase       ,     indexing=__UpperCamelCase      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						torch.Tensor      )  ->							Tuple[torch.device, int, int]:
    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





