






import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    EfficientFormerForImageClassification,
					    EfficientFormerForImageClassificationWithTeacher,
					    EfficientFormerModel,
					)
					from transformers.models.efficientformer.modeling_efficientformer import (
					    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
					)


if is_vision_available():
					from PIL import Image

					from transformers import EfficientFormerImageProcessor





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__ = 13							, lowercase__ = 64							, lowercase__ = 2							, lowercase__ = 3							, lowercase__ = 3							, lowercase__ = True							, lowercase__ = True							, lowercase__ = 128							, lowercase__=[16, 32, 64, 128]							, lowercase__ = 7							, lowercase__ = 4							, lowercase__ = 37							, lowercase__ = "gelu"							, lowercase__ = 0.1							, lowercase__ = 0.1							, lowercase__ = 10							, lowercase__ = 0.02							, lowercase__ = 2							, lowercase__ = 1							, lowercase__ = 128							, lowercase__ = [2, 2, 2, 2]							, lowercase__ = 2							, lowercase__ = 2							, )				->				Optional[int]:
				__A    =							parent
				__A    =							batch_size
				__A    =							image_size
				__A    =							patch_size
				__A    =							num_channels
				__A    =							is_training
				__A    =							use_labels
				__A    =							hidden_size
				__A    =							num_hidden_layers
				__A    =							num_attention_heads
				__A    =							intermediate_size
				__A    =							hidden_act
				__A    =							hidden_dropout_prob
				__A    =							attention_probs_dropout_prob
				__A    =							type_sequence_label_size
				__A    =							initializer_range
				__A    =							encoder_stride
				__A    =							num_attention_outputs
				__A    =							embed_dim
				__A    =							embed_dim + 1
				__A    =							resolution
				__A    =							depths
				__A    =							hidden_sizes
				__A    =							dim
				__A    =							mlp_expansion_ratio






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				__A    =							None
				if self.use_labels:
						__A    =							ids_tensor([self.batch_size]							, self.type_sequence_label_size						)

				__A    =							self.get_config()
				return config, pixel_values, labels






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				return EfficientFormerConfig(
				    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=lowercase__							, initializer_range=self.initializer_range							, encoder_stride=self.encoder_stride							, resolution=self.resolution							, depths=self.depths							, hidden_sizes=self.hidden_sizes							, dim=self.dim							, mlp_expansion_ratio=self.mlp_expansion_ratio							, )






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				str:
				__A    =							EfficientFormerModel(config=lowercase__						)
				model.to(lowercase__						)
				model.eval()
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				Dict:
				__A    =							self.type_sequence_label_size
				__A    =							EfficientFormerForImageClassification(lowercase__						)
				model.to(lowercase__						)
				model.eval()
				__A    =							model(lowercase__							, labels=lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)						)

				# test greyscale images
				__A    =							1
				__A    =							EfficientFormerForImageClassification(lowercase__						)
				model.to(lowercase__						)
				model.eval()

				__A    =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)						)






		def 					_lowerCamelCase					(      self						)				->				int:
				__A    =							self.prepare_config_and_inputs()
				(
				    (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   
				)    =							config_and_inputs
				__A    =							{"pixel_values": pixel_values}
				return config, inputs_dict





@require_torch
class        a__      (	lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Dict 					= (
		    (
		        EfficientFormerModel,
		        EfficientFormerForImageClassificationWithTeacher,
		        EfficientFormerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase_							:       str 					= (
		    {
		        'feature-extraction': EfficientFormerModel,
		        'image-classification': (
		            EfficientFormerForImageClassification,
		            EfficientFormerForImageClassificationWithTeacher,
		        ),
		    }
		    if is_torch_available()
		    else {}
		)
		UpperCAmelCase_							:       List[str] 					= False

		UpperCAmelCase_							:       Any 					= False
		UpperCAmelCase_							:       Union[str, Any] 					= False
		UpperCAmelCase_							:       Optional[int] 					= False






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							EfficientFormerModelTester(self						)
				__A    =							ConfigTester(
				    self							, config_class=lowercase__							, has_text_modality=lowercase__							, hidden_size=37						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				self.config_tester.run_common_tests()






		@unittest.skip(reason="EfficientFormer does not use inputs_embeds"						)
		def 					_lowerCamelCase					(      self						)				->				Tuple:
				pass






		@unittest.skip(reason="EfficientFormer does not support input and output embeddings"						)
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				pass






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__A    =							model_class(lowercase__						)
						__A    =							inspect.signature(model.forward						)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__A    =							[*signature.parameters.keys()]

						__A    =							["pixel_values"]
						self.assertListEqual(arg_names[:1]							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				def check_hidden_states_output(lowercase__							, lowercase__							, lowercase__						):
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()

						with torch.no_grad():
								__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)

						__A    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						__A    =							getattr(
						    self.model_tester							, "expected_num_hidden_layers"							, self.model_tester.num_hidden_layers + 1						)
						self.assertEqual(len(lowercase__						)							, lowercase__						)

						if hasattr(self.model_tester							, "encoder_seq_length"						):
								__A    =							self.model_tester.encoder_seq_length
								if hasattr(self.model_tester							, "chunk_length"						) and self.model_tester.chunk_length > 1:
										__A    =							seq_length * self.model_tester.chunk_length
						else:
								__A    =							self.model_tester.seq_length

						self.assertListEqual(
						    list(hidden_states[-1].shape[-2:]						)							, [seq_length, self.model_tester.hidden_size]							, )

						if config.is_encoder_decoder:
								__A    =							outputs.decoder_hidden_states

								self.assertIsInstance(lowercase__							, (list, tuple)						)
								self.assertEqual(len(lowercase__						)							, lowercase__						)
								__A    =							getattr(self.model_tester							, "seq_length"							, lowercase__						)
								__A    =							getattr(self.model_tester							, "decoder_seq_length"							, lowercase__						)

								self.assertListEqual(
								    list(hidden_states[-1].shape[-2:]						)							, [decoder_seq_length, self.model_tester.hidden_size]							, )

				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__A    =							True
						check_hidden_states_output(lowercase__							, lowercase__							, lowercase__						)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__A    =							True

						check_hidden_states_output(lowercase__							, lowercase__							, lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__=False						)				->				List[str]:
				__A    =							super()._prepare_for_class(lowercase__							, lowercase__							, return_labels=lowercase__						)

				if return_labels:
						if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
								del inputs_dict["labels"]

				return inputs_dict






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase__						)






		@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"						)
		def 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase__						)






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

				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()
				__A    =							True

				for model_class in self.all_model_classes:
						# EfficientFormerForImageClassificationWithTeacher supports inference-only
						if (
						    model_class in get_values(lowercase__						)
						    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
						):
								continue
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.train()
						__A    =							self._prepare_for_class(lowercase__							, lowercase__							, return_labels=lowercase__						)
						__A    =							model(**lowercase__						).loss
						loss.backward()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

				__A    =							[
				    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
				    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
				    {"title": "regression", "num_labels": 1, "dtype": torch.float},
				]

				for model_class in self.all_model_classes:
						if (
						    model_class
						    not in [
						        *get_values(lowercase__						),
						    ]
						    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
						):
								continue

						for problem_type in problem_types:
								with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""						):
										__A    =							problem_type["title"]
										__A    =							problem_type["num_labels"]

										__A    =							model_class(lowercase__						)
										model.to(lowercase__						)
										model.train()

										__A    =							self._prepare_for_class(lowercase__							, lowercase__							, return_labels=lowercase__						)

										if problem_type["num_labels"] > 1:
												__A    =							inputs["labels"].unsqueeze(1						).repeat(1							, problem_type["num_labels"]						)

										__A    =							inputs["labels"].to(problem_type["dtype"]						)

										# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
										# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
										# they have the same size." which is a symptom something in wrong for the regression problem.
										# See https://github.com/huggingface/transformers/issues/11780
										with warnings.catch_warnings(record=lowercase__						) as warning_list:
												__A    =							model(**lowercase__						).loss
										for w in warning_list:
												if "Using a target size that is different to the input size" in str(w.message						):
														raise ValueError(
														    F"""Something is going wrong in the regression problem: intercepted {w.message}"""						)

										loss.backward()






		@slow
		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__A    =							EfficientFormerModel.from_pretrained(lowercase__						)
						self.assertIsNotNone(lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				str:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()
				__A    =							True

				__A    =							getattr(self.model_tester							, "seq_length"							, lowercase__						)
				__A    =							getattr(self.model_tester							, "encoder_seq_length"							, lowercase__						)
				__A    =							getattr(self.model_tester							, "key_length"							, lowercase__						)
				__A    =							getattr(self.model_tester							, "chunk_length"							, lowercase__						)

				if chunk_length is not None and hasattr(self.model_tester							, "num_hashes"						):
						__A    =							encoder_seq_length * self.model_tester.num_hashes

				for model_class in self.all_model_classes:
						__A    =							True
						__A    =							False
						__A    =							True
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						with torch.no_grad():
								__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)
						__A    =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
						self.assertEqual(len(lowercase__						)							, self.model_tester.num_attention_outputs						)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						__A    =							True
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						with torch.no_grad():
								__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)
						__A    =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
						self.assertEqual(len(lowercase__						)							, self.model_tester.num_attention_outputs						)

						if chunk_length is not None:
								self.assertListEqual(
								    list(attentions[0].shape[-4:]						)							, [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]							, )
						else:
								self.assertListEqual(
								    list(attentions[0].shape[-3:]						)							, [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]							, )



def      UpperCAmelCase						( ):


		'''simple docstring'''

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





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






		@cached_property
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				return (
				    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"						)
				    if is_vision_available()
				    else None
				)






		@slow
		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"						).to(
				    lowercase__						)

				__A    =							self.default_image_processor
				__A    =							prepare_img()
				__A    =							image_processor(images=lowercase__							, return_tensors="pt"						).to(lowercase__						)

				# forward pass
				with torch.no_grad():
						__A    =							model(**lowercase__						)

				# verify the logits
				__A    =							(1, 1000)
				self.assertEqual(outputs.logits.shape							, lowercase__						)

				__A    =							torch.tensor([-0.0555, 0.4825, -0.0852]						).to(lowercase__						)
				self.assertTrue(torch.allclose(outputs.logits[0][:3]							, lowercase__							, atol=1e-4						)						)






		@slow
		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							EfficientFormerForImageClassificationWithTeacher.from_pretrained(
				    "snap-research/efficientformer-l1-300"						).to(lowercase__						)

				__A    =							self.default_image_processor
				__A    =							prepare_img()
				__A    =							image_processor(images=lowercase__							, return_tensors="pt"						).to(lowercase__						)

				# forward pass
				with torch.no_grad():
						__A    =							model(**lowercase__						)

				# verify the logits
				__A    =							(1, 1000)
				self.assertEqual(outputs.logits.shape							, lowercase__						)

				__A    =							torch.tensor([-0.1312, 0.4353, -1.0499]						).to(lowercase__						)
				self.assertTrue(torch.allclose(outputs.logits[0][:3]							, lowercase__							, atol=1e-4						)						)




'''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	)






