




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    snake_case   (				A		,  A		,  A		,  A		,  self     )				->				Any:




	'''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    snake_case   (				A		,  A		,  A = 0     )				->				nn.Linear:




	'''simple docstring'''


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


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						List[str]				  =				nf
						lowerCamelCase_				:						Any				  =				nn.Parameter(torch.empty(__lowercase					,			__lowercase   )   )
						lowerCamelCase_				:						List[Any]				  =				nn.Parameter(torch.zeros(__lowercase   )   )
						nn.init.normal_(self.weight					,			std=0.02   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




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






def    snake_case   (				A		,  A		,  A = 1     )				->				ConvaD:




	'''simple docstring'''


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






def    snake_case   (				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    snake_case   (				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
	lowerCamelCase_				:						str				  =				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:
		lowerCamelCase_				:						List[Any]				  =				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_				:						str				  =				input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		lowerCamelCase_				:						List[Any]				  =				tuple(input_tensor.chunk(A		,  dim=A     ) for input_tensor in input_tensors     )
		# apply forward fn to every tuple
		lowerCamelCase_				:						List[Any]				  =				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    snake_case   (				A		,  A		,  A		,  A     )				->				Tuple[Set[int], torch.LongTensor]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				torch.ones(A		,  A     )
	lowerCamelCase_				:						List[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
		lowerCamelCase_				:						Tuple				  =				head - sum(1 if h < head else 0 for h in already_pruned_heads     )
		lowerCamelCase_				:						Optional[Any]				  =				0
	lowerCamelCase_				:						Union[str, Any]				  =				mask.view(-1     ).contiguous().eq(1     )
	lowerCamelCase_				:						torch.LongTensor				  =				torch.arange(len(A     )     )[mask].long()
	return heads, index






def    snake_case   (				*A		,  A = None     )				->				Tuple[torch.Tensor, ...]:




	'''simple docstring'''


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






def    snake_case   (				A     )				->				Tuple[torch.device, int, int]:




	'''simple docstring'''


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





'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DeiTImageProcessor

class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Any   ,		__A		:  Tuple=7   ,		__A		:  Dict=3   ,		__A		:  List[str]=18   ,		__A		:  Optional[int]=30   ,		__A		:  int=400   ,		__A		:  Union[str, Any]=True   ,		__A		:  Tuple=None   ,		__A		:  int=True   ,		__A		:  int=None   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=[0.5, 0.5, 0.5]   ,		__A		:  List[Any]=[0.5, 0.5, 0.5]   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   size if size is not None else {"""height""": 20, """width""": 20}
								lowerCAmelCase__  =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   min_resolution
								lowerCAmelCase__  =   max_resolution
								lowerCAmelCase__  =   do_resize
								lowerCAmelCase__  =   size
								lowerCAmelCase__  =   do_center_crop
								lowerCAmelCase__  =   crop_size
								lowerCAmelCase__  =   do_normalize
								lowerCAmelCase__  =   image_mean
								lowerCAmelCase__  =   image_std




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





@require_torch
@require_vision
class      lowerCamelCase__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	DeiTImageProcessor if is_vision_available() else None
	A__		=	True




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   DeiTImageProcessingTester(self					)




	@property
	def      lowercase__							(  self		:  Any					)     ->			int:




								'''simple docstring'''





								return self.image_processor_tester.prepare_image_processor_dict()




	def      lowercase__							(  self		:  str					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(__A   ,		"""do_resize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""size"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_mean"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_std"""					)					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size   ,		{"""height""": 20, """width""": 20}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 18, """width""": 18}					)

								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		crop_size=84					)
								self.assertEqual(image_processor.size   ,		{"""height""": 42, """width""": 42}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 84, """width""": 84}					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			List[str]:




								'''simple docstring'''





								pass




	def      lowercase__							(  self		:  Dict					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		Image.Image					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		numpify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		np.ndarray					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[str]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		torchify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		torch.Tensor					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)





