



"""simple docstring"""
import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_MAPPING,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						)
						from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				str		,	snake_case:				Union[str, Any]		,	snake_case:				Optional[int]=100		,	snake_case:				Tuple=13		,	snake_case:				Optional[Any]=30		,	snake_case:				Dict=2		,	snake_case:				int=3		,	snake_case:				Union[str, Any]=True		,	snake_case:				List[str]=True		,	snake_case:				List[Any]=32		,	snake_case:				Optional[Any]=4		,	snake_case:				str=4		,	snake_case:				Any=37		,	snake_case:				str="gelu"		,	snake_case:				Union[str, Any]=0.1		,	snake_case:				int=0.1		,	snake_case:				Optional[int]=10		,	snake_case:				Any=0.0_2		,	snake_case:				str=3		,	snake_case:				List[str]=None		,	snake_case:				int=[0, 1, 2, 3]		,	)  ->      Union[str, Any]:
											snake_case_					:Tuple		=     parent
											snake_case_					:Union[str, Any]		=     100
											snake_case_					:Union[str, Any]		=     batch_size
											snake_case_					:Union[str, Any]		=     image_size
											snake_case_					:Tuple		=     patch_size
											snake_case_					:Any		=     num_channels
											snake_case_					:str		=     is_training
											snake_case_					:Dict		=     use_labels
											snake_case_					:Any		=     hidden_size
											snake_case_					:Optional[Any]		=     num_hidden_layers
											snake_case_					:Optional[Any]		=     num_attention_heads
											snake_case_					:str		=     intermediate_size
											snake_case_					:List[str]		=     hidden_act
											snake_case_					:Any		=     hidden_dropout_prob
											snake_case_					:List[str]		=     attention_probs_dropout_prob
											snake_case_					:int		=     type_sequence_label_size
											snake_case_					:Optional[int]		=     initializer_range
											snake_case_					:Optional[Any]		=     scope
											snake_case_					:Any		=     out_indices
											snake_case_					:Dict		=     num_labels

											# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											snake_case_					:Tuple		=     (image_size // patch_size) ** 2
											snake_case_					:Tuple		=     num_patches + 1






					def   lowerCAmelCase_							(			self:				str		)  ->      List[Any]:
											snake_case_					:str		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

											snake_case_					:Union[str, Any]		=     None
											snake_case_					:Optional[Any]		=     None
											if self.use_labels:
																	snake_case_					:Tuple		=     ids_tensor([self.batch_size]		,	self.type_sequence_label_size		)
																	snake_case_					:List[Any]		=     ids_tensor([self.batch_size, self.image_size, self.image_size]		,	self.num_labels		)

											snake_case_					:Tuple		=     self.get_config()

											return config, pixel_values, labels, pixel_labels






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Dict:
											return DataaVecVisionConfig(
											    vocab_size=self.vocab_size		,	image_size=self.image_size		,	patch_size=self.patch_size		,	num_channels=self.num_channels		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	is_decoder=snake_case		,	initializer_range=self.initializer_range		,	out_indices=self.out_indices		,	)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Optional[int]		,	snake_case:				int		,	snake_case:				str		,	snake_case:				Dict		)  ->      int:
											snake_case_					:Union[str, Any]		=     DataaVecVisionModel(config=snake_case		)
											model.to(snake_case		)
											model.eval()
											snake_case_					:Optional[int]		=     model(snake_case		)
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											snake_case_					:Dict		=     (self.image_size // self.patch_size) ** 2
											self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, num_patches + 1, self.hidden_size)		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				Tuple		,	snake_case:				Union[str, Any]		,	snake_case:				Tuple		,	snake_case:				List[Any]		)  ->      Tuple:
											snake_case_					:Union[str, Any]		=     self.type_sequence_label_size
											snake_case_					:int		=     DataaVecVisionForImageClassification(snake_case		)
											model.to(snake_case		)
											model.eval()
											snake_case_					:Any		=     model(snake_case		,	labels=snake_case		)
											self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)		)






					def   lowerCAmelCase_							(			self:				List[Any]		,	snake_case:				Any		,	snake_case:				Tuple		,	snake_case:				List[Any]		,	snake_case:				Optional[int]		)  ->      Tuple:
											snake_case_					:Tuple		=     self.num_labels
											snake_case_					:List[str]		=     DataaVecVisionForSemanticSegmentation(snake_case		)
											model.to(snake_case		)
											model.eval()
											snake_case_					:Tuple		=     model(snake_case		)
											self.parent.assertEqual(
											    result.logits.shape		,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)
											snake_case_					:List[str]		=     model(snake_case		,	labels=snake_case		)
											self.parent.assertEqual(
											    result.logits.shape		,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Union[str, Any]:
											snake_case_					:List[str]		=     self.prepare_config_and_inputs()
											snake_case_,			snake_case_,			snake_case_,			snake_case_					:List[str]		=     config_and_inputs
											snake_case_					:List[str]		=     {"""pixel_values""": pixel_values}
											return config, inputs_dict

@require_torch
class 							lowerCamelCase    (     _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase							):






					'''simple docstring'''





					_A						:     List[str]		=	(
					    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					_A						:     Union[str, Any]		=	(
					    {
					        """feature-extraction""": DataaVecVisionModel,
					        """image-classification""": DataaVecVisionForImageClassification,
					        """image-segmentation""": DataaVecVisionForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					_A						:     List[Any]		=	False
					_A						:     int		=	False
					_A						:     List[str]		=	False






					def   lowerCAmelCase_							(			self:				Any		)  ->      Any:
											snake_case_					:Union[str, Any]		=     DataaVecVisionModelTester(self		)
											snake_case_					:Union[str, Any]		=     ConfigTester(
											    self		,	config_class=snake_case		,	has_text_modality=snake_case		,	hidden_size=37		)






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Dict:
											self.config_tester.run_common_tests()






					def   lowerCAmelCase_							(			self:				int		)  ->      Union[str, Any]:
											# Data2VecVision does not use inputs_embeds
											pass






					@require_torch_multi_gpu
					@unittest.skip(
					    reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""		)
					def   lowerCAmelCase_							(			self:				Any		)  ->      Optional[Any]:
											pass






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[int]:
											snake_case_,			snake_case_					:Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_					:Dict		=     model_class(snake_case		)
																	self.assertIsInstance(model.get_input_embeddings()		,	(nn.Module)		)
																	snake_case_					:Optional[int]		=     model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case		,	nn.Linear		)		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Dict:
											snake_case_,			snake_case_					:str		=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_					:int		=     model_class(snake_case		)
																	snake_case_					:List[str]		=     inspect.signature(model.forward		)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	snake_case_					:Optional[int]		=     [*signature.parameters.keys()]

																	snake_case_					:Union[str, Any]		=     ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]		,	snake_case		)






					def   lowerCAmelCase_							(			self:				int		)  ->      List[Any]:
											snake_case_					:int		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Any:
											snake_case_					:Tuple		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_segmentation(*snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      List[str]:
											if not self.model_tester.is_training:
																	return

											snake_case_,			snake_case_					:List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:Optional[Any]		=     True

											for model_class in self.all_model_classes:
																	if model_class in [*get_values(snake_case		)]:
																							continue

																	snake_case_					:List[str]		=     model_class(snake_case		)
																	model.to(snake_case		)
																	model.train()
																	snake_case_					:Optional[Any]		=     self._prepare_for_class(snake_case		,	snake_case		,	return_labels=snake_case		)
																	snake_case_					:int		=     model(**snake_case		).loss
																	loss.backward()






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Tuple:
											snake_case_,			snake_case_					:List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																	return

											snake_case_					:List[Any]		=     False
											snake_case_					:Optional[int]		=     True

											for model_class in self.all_model_classes:
																	if model_class in [*get_values(snake_case		)] or not model_class.supports_gradient_checkpointing:
																							continue
																	# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
																	# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																	elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																							snake_case_,			snake_case_,			snake_case_,			snake_case_					:Tuple		=     inputs_dict["""pixel_values"""].shape
																							snake_case_					:List[Any]		=     torch.zeros(
																							    [self.model_tester.batch_size, height, width]		,	device=snake_case		).long()
																	snake_case_					:Dict		=     model_class(snake_case		)
																	model.gradient_checkpointing_enable()
																	model.to(snake_case		)
																	model.train()
																	snake_case_					:Any		=     self._prepare_for_class(snake_case		,	snake_case		,	return_labels=snake_case		)
																	snake_case_					:List[str]		=     model(**snake_case		).loss
																	loss.backward()






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Any:
											snake_case_,			snake_case_					:Dict		=     self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_					:Union[str, Any]		=     _config_zero_init(snake_case		)
											for model_class in self.all_model_classes:
																	snake_case_					:List[Any]		=     model_class(config=snake_case		)
																	for name, param in model.named_parameters():
																							# we skip lambda parameters as these require special initial values
																							# determined by config.layer_scale_init_value
																							if "lambda" in name:
																													continue
																							if param.requires_grad:
																													self.assertIn(
																													    ((param.data.mean() * 1E9).round() / 1E9).item()		,	[0.0, 1.0]		,	msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,	)






					def   lowerCAmelCase_							(			self:				List[str]		,	snake_case:				int		,	snake_case:				int		,	snake_case:				int		,	snake_case:				List[str]=2E-4		,	snake_case:				Any="outputs"		,	snake_case:				Tuple=None		)  ->      int:
											# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
											super().check_pt_tf_outputs(snake_case		,	snake_case		,	snake_case		,	snake_case		,	snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Optional[Any]:
											snake_case_					:str		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case		)






					@slow
					def   lowerCAmelCase_							(			self:				Dict		)  ->      str:
											for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_					:Tuple		=     DataaVecVisionModel.from_pretrained(snake_case		)
																	self.assertIsNotNone(snake_case		)

def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Dict		=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
						return image

@require_torch
@require_vision
class 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





					@cached_property
					def   lowerCAmelCase_							(			self:				List[str]		)  ->      List[Any]:
											return (
											    BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""		) if is_vision_available() else None
											)






					@slow
					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Dict:
											snake_case_					:Optional[int]		=     DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""		).to(
											    snake_case		)

											snake_case_					:Union[str, Any]		=     self.default_image_processor
											snake_case_					:Union[str, Any]		=     prepare_img()
											snake_case_					:Dict		=     image_processor(images=snake_case		,	return_tensors="""pt"""		).to(snake_case		)

											# forward pass
											with torch.no_grad():
																	snake_case_					:str		=     model(**snake_case		)
											snake_case_					:Union[str, Any]		=     outputs.logits

											# verify the logits
											snake_case_					:Union[str, Any]		=     torch.Size((1, 1_000)		)
											self.assertEqual(logits.shape		,	snake_case		)

											snake_case_					:int		=     torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]		).to(snake_case		)

											self.assertTrue(torch.allclose(logits[0, :3]		,	snake_case		,	atol=1E-4		)		)

											snake_case_					:List[str]		=     [model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
											self.assertEqual(logits[0].topk(2		).indices.cpu().tolist()		,	snake_case		)



'''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


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



class        lowercase    (	nn.Module			):


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





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[int]:
								UpperCAmelCase_		:   Dict													=       x.size()[:-1] + (self.nf,)
								UpperCAmelCase_		:   Optional[Any]													=       torch.addmm(self.bias     ,      x.view(-1     ,      x.size(-1))     ,      self.weight)
								UpperCAmelCase_		:   List[str]													=       x.view(_snake_case)
								return x







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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 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 ,UpperCamelCase ,UpperCamelCase ,*UpperCamelCase	)     ->     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_		:   str													=       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_		:   Optional[int]													=       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_		:   Any													=       input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		UpperCAmelCase_		:   int													=       tuple(input_tensor.chunk(UpperCamelCase ,dim=UpperCamelCase	) for input_tensor in input_tensors	)
		# apply forward fn to every tuple
		UpperCAmelCase_		:   List[str]													=       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 ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Tuple[Set[int], torch.LongTensor]:
	UpperCAmelCase_		:   List[Any]													=       torch.ones(UpperCamelCase ,UpperCamelCase	)
	UpperCAmelCase_		:   Optional[int]													=       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_		:   Any													=       head - sum(1 if h < head else 0 for h in already_pruned_heads	)
		UpperCAmelCase_		:   List[Any]													=       0
	UpperCAmelCase_		:   str													=       mask.view(-1	).contiguous().eq(1	)
	UpperCAmelCase_		:   torch.LongTensor													=       torch.arange(len(UpperCamelCase	)	)[mask].long()
	return heads, index







def     SCREAMING_SNAKE_CASE(	*UpperCamelCase ,UpperCamelCase = None	)     ->     Tuple[torch.Tensor, ...]:
	return torch.meshgrid(*UpperCamelCase ,indexing=UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Tuple[torch.device, int, int]:
	return tensor.device, storage_ptr(UpperCamelCase	), storage_size(UpperCamelCase	)






