
"""simple docstring"""




import json
import os
import tempfile

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


if is_torch_available():
					import numpy as np
					import torch

if is_vision_available():
					from PIL import Image

def __snake_case (      UpperCamelCase						,					UpperCamelCase=False						,					UpperCamelCase=False						,					UpperCamelCase=False    )       ->    Union[str, Any]:


		"""simple docstring"""





		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		a__   		=     []
		for i in range(image_processor_tester.batch_size    ):
				if equal_resolution:
						a__   		=     a__   		=     image_processor_tester.max_resolution
				else:
						# To avoid getting image width/height 0
						a__   		=     image_processor_tester.min_resolution
						if getattr(SCREAMING_SNAKE_CASE_						,					'''size_divisor'''						,					SCREAMING_SNAKE_CASE_    ):
								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
								a__   		=     max(image_processor_tester.size_divisor						,					SCREAMING_SNAKE_CASE_    )
						a__		,	a__   		=     np.random.choice(np.arange(SCREAMING_SNAKE_CASE_						,					image_processor_tester.max_resolution    )						,					2    )
				image_inputs.append(
				    np.random.randint(255						,					size=(image_processor_tester.num_channels, width, height)						,					dtype=np.uinta    )    )

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				a__   		=     [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_						,					0						,					-1    )    ) for image in image_inputs]

		if torchify:
				a__   		=     [torch.from_numpy(SCREAMING_SNAKE_CASE_    ) for image in image_inputs]

		return image_inputs

def __snake_case (      UpperCamelCase						,					UpperCamelCase=10						,					UpperCamelCase=10						,					UpperCamelCase=False						,					UpperCamelCase=False    )       ->    Any:


		"""simple docstring"""





		a__   		=     []
		for i in range(image_processor_tester.num_frames    ):
				video.append(np.random.randint(255						,					size=(image_processor_tester.num_channels, width, height)						,					dtype=np.uinta    )    )

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				a__   		=     [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_						,					0						,					-1    )    ) for frame in video]

		if torchify:
				a__   		=     [torch.from_numpy(SCREAMING_SNAKE_CASE_    ) for frame in video]

		return video

def __snake_case (      UpperCamelCase						,					UpperCamelCase=False						,					UpperCamelCase=False						,					UpperCamelCase=False    )       ->    Tuple:


		"""simple docstring"""





		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		a__   		=     []
		for i in range(image_processor_tester.batch_size    ):
				if equal_resolution:
						a__   		=     a__   		=     image_processor_tester.max_resolution
				else:
						a__		,	a__   		=     np.random.choice(
						    np.arange(image_processor_tester.min_resolution						,					image_processor_tester.max_resolution    )						,					2    )
						a__   		=     prepare_video(
						    image_processor_tester=SCREAMING_SNAKE_CASE_						,					width=SCREAMING_SNAKE_CASE_						,					height=SCREAMING_SNAKE_CASE_						,					numpify=SCREAMING_SNAKE_CASE_						,					torchify=SCREAMING_SNAKE_CASE_						,					)
				video_inputs.append(SCREAMING_SNAKE_CASE_    )

		return video_inputs




class SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







	snake_case__							:		Union[str, Any]         =		None


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			a__   		=     json.loads(image_processor.to_json_string()			)
			for key, value in self.image_processor_dict.items():
					self.assertEqual(obj[key]       ,    lowercase__			)


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

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)

			with tempfile.TemporaryDirectory() as tmpdirname:
					a__   		=     os.path.join(lowercase__       ,    '''image_processor.json'''			)
					image_processor_first.to_json_file(lowercase__			)
					a__   		=     self.image_processing_class.from_json_file(lowercase__			)

			self.assertEqual(image_processor_second.to_dict()       ,    image_processor_first.to_dict()			)


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

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)

			with tempfile.TemporaryDirectory() as tmpdirname:
					a__   		=     image_processor_first.save_pretrained(lowercase__			)[0]
					check_json_file_has_correct_format(lowercase__			)
					a__   		=     self.image_processing_class.from_pretrained(lowercase__			)

			self.assertEqual(image_processor_second.to_dict()       ,    image_processor_first.to_dict()			)


	def    _UpperCamelCase							(  self :List[str]			)			->   str:

			'''simple docstring'''






			a__   		=     self.image_processing_class()
			self.assertIsNotNone(lowercase__			)






	@require_torch
	@require_vision
	def    _UpperCamelCase							(  self :List[Any]			)			->   Optional[Any]:

			'''simple docstring'''






			if self.test_cast_dtype is not None:
					# Initialize image_processor
					a__   		=     self.image_processing_class(**self.image_processor_dict			)

					# create random PyTorch tensors
					a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=lowercase__       ,    torchify=lowercase__			)

					a__   		=     image_processor(lowercase__       ,    return_tensors='''pt'''			)
					# for layoutLM compatiblity
					self.assertEqual(encoding.pixel_values.device       ,    torch.device('''cpu'''			)			)
					self.assertEqual(encoding.pixel_values.dtype       ,    torch.floataa			)

					a__   		=     image_processor(lowercase__       ,    return_tensors='''pt'''			).to(torch.floataa			)
					self.assertEqual(encoding.pixel_values.device       ,    torch.device('''cpu'''			)			)
					self.assertEqual(encoding.pixel_values.dtype       ,    torch.floataa			)

					a__   		=     image_processor(lowercase__       ,    return_tensors='''pt'''			).to('''cpu'''       ,    torch.bfloataa			)
					self.assertEqual(encoding.pixel_values.device       ,    torch.device('''cpu'''			)			)
					self.assertEqual(encoding.pixel_values.dtype       ,    torch.bfloataa			)

					with self.assertRaises(lowercase__			):
							a__   		=     image_processor(lowercase__       ,    return_tensors='''pt'''			).to(torch.bfloataa       ,    '''cpu'''			)

					# Try with text + image feature
					a__   		=     image_processor(lowercase__       ,    return_tensors='''pt'''			)
					encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]			)}			)
					a__   		=     encoding.to(torch.floataa			)

					self.assertEqual(encoding.pixel_values.device       ,    torch.device('''cpu'''			)			)
					self.assertEqual(encoding.pixel_values.dtype       ,    torch.floataa			)
					self.assertEqual(encoding.input_ids.dtype       ,    torch.long			)






"""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


snake_case					  =						[nn.LayerNorm]

snake_case					  =						logging.get_logger(__name__)

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

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	self     ):
					from torch import _softmax_backward_data

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

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




class 	UpperCamelCase      (			nn.Module ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =      nf
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.empty(lowercase__						,      lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.zeros(lowercase__     )     )
									nn.init.normal_(self.weight						,      std=0.02     )




				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      x.size()[:-1] + (self.nf,)
									SCREAMING_SNAKE_CASE           =      torch.addmm(self.bias						,      x.view(-1						,      x.size(-1     )     )						,      self.weight     )
									SCREAMING_SNAKE_CASE           =      x.view(lowercase__     )
									return x






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

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	*SCREAMING_SNAKE_CASE_     ):
					assert len(SCREAMING_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
					SCREAMING_SNAKE_CASE           =      len(inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters     )
					if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(
										    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_     )} input '''
										    'tensors are given'     )

					if chunk_size > 0:
										SCREAMING_SNAKE_CASE           =      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           =      input_tensors[0].shape[chunk_dim] // chunk_size

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

					return forward_fn(*SCREAMING_SNAKE_CASE_     )

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

def     UpperCamelCase_    (						*SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					return torch.meshgrid(*SCREAMING_SNAKE_CASE_,	indexing=SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_     ), storage_size(SCREAMING_SNAKE_CASE_     )
