



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {'vocab_file': 'sentencepiece.model'}

__SCREAMING_SNAKE_CASE           =  {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

__SCREAMING_SNAKE_CASE           =  {
    'google/rembert': 256,
}

class 	lowerCamelCase_    ( lowerCAmelCase__						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Any=False	,	__lowerCamelCase      :			str=True	,	__lowerCamelCase      :			Tuple=True	,	__lowerCamelCase      :			str="[CLS]"	,	__lowerCamelCase      :			Tuple="[SEP]"	,	__lowerCamelCase      :			List[Any]="[UNK]"	,	__lowerCamelCase      :			Optional[int]="[SEP]"	,	__lowerCamelCase      :			Any="[PAD]"	,	__lowerCamelCase      :			Tuple="[CLS]"	,	__lowerCamelCase      :			List[str]="[MASK]"	,	**__lowerCamelCase      :			Tuple	,	)      ->				str:
									super().__init__(
									    do_lower_case=_SCREAMING_SNAKE_CASE	,	remove_space=_SCREAMING_SNAKE_CASE	,	keep_accents=_SCREAMING_SNAKE_CASE	,	bos_token=_SCREAMING_SNAKE_CASE	,	eos_token=_SCREAMING_SNAKE_CASE	,	unk_token=_SCREAMING_SNAKE_CASE	,	sep_token=_SCREAMING_SNAKE_CASE	,	pad_token=_SCREAMING_SNAKE_CASE	,	cls_token=_SCREAMING_SNAKE_CASE	,	mask_token=_SCREAMING_SNAKE_CASE	,	**_SCREAMING_SNAKE_CASE	,	)

									A							:					Optional[Any]              =						do_lower_case
									A							:					List[str]              =						remove_space
									A							:					int              =						keep_accents
									A							:					Optional[int]              =						vocab_file

									A							:					Tuple              =						spm.SentencePieceProcessor()
									self.sp_model.Load(_SCREAMING_SNAKE_CASE )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									return len(self.sp_model )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[int]:
									A							:					Union[str, Any]              =						{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
									vocab.update(self.added_tokens_encoder )
									return vocab







						def __getstate__(	self      :			Any )      ->				Optional[Any]:
									A							:					Tuple              =						self.__dict__.copy()
									A							:					Optional[Any]              =						None
									return state







						def __setstate__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str] )      ->				Optional[Any]:
									A							:					Tuple              =						d
									A							:					Optional[int]              =						spm.SentencePieceProcessor()
									self.sp_model.Load(self.vocab_file )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any]=False )      ->				Union[str, Any]:
									A							:					int              =						self.sp_model.EncodeAsPieces(_SCREAMING_SNAKE_CASE )
									return pieces







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Any )      ->				Optional[int]:
									return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			Union[str, Any] )      ->				Union[str, Any]:
									return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			int )      ->				List[str]:
									A							:					Union[str, Any]              =						self.sp_model.decode_pieces(_SCREAMING_SNAKE_CASE )
									return out_string







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[str] = None )      ->				List[int]:
									A							:					int              =						[self.sep_token_id]
									A							:					int              =						[self.cls_token_id]
									if token_ids_a is None:
												return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Any = None	,	__lowerCamelCase      :			List[str] = False )      ->				List[int]:

									if already_has_special_tokens:
												if token_ids_a is not None:
															raise ValueError(
															    "You should not supply a second sequence if the provided sequence of "
															    "ids is already formatted with special tokens for the model." )
												return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
												return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[str] = None )      ->				List[int]:
									A							:					Optional[int]              =						[self.sep_token_id]
									A							:					Union[str, Any]              =						[self.cls_token_id]

									if token_ids_a is None:
												return len(cls + token_ids_a + sep ) * [0]
									return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			List[Any] = None )      ->				Tuple[str]:
									if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
												logger.error("Vocabulary path ({}) should be a directory".format(_SCREAMING_SNAKE_CASE ) )
												return
									A							:					Union[str, Any]              =						os.path.join(
									    _SCREAMING_SNAKE_CASE	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

									if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
												copyfile(self.vocab_file	,	_SCREAMING_SNAKE_CASE )

									return (out_vocab_file,)




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    DeiTForImageClassification,
							    DeiTForImageClassificationWithTeacher,
							    DeiTForMaskedImageModeling,
							    DeiTModel,
							)
							from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			Tuple	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Tuple=13	,	__lowerCamelCase      :			List[str]=30	,	__lowerCamelCase      :			Tuple=2	,	__lowerCamelCase      :			Any=3	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			Optional[int]=True	,	__lowerCamelCase      :			int=32	,	__lowerCamelCase      :			List[str]=5	,	__lowerCamelCase      :			Any=4	,	__lowerCamelCase      :			Optional[int]=37	,	__lowerCamelCase      :			List[Any]="gelu"	,	__lowerCamelCase      :			int=0.1	,	__lowerCamelCase      :			Union[str, Any]=0.1	,	__lowerCamelCase      :			Optional[Any]=10	,	__lowerCamelCase      :			Dict=0.02	,	__lowerCamelCase      :			Tuple=3	,	__lowerCamelCase      :			Union[str, Any]=None	,	__lowerCamelCase      :			List[Any]=2	,	)      ->				str:
									A							:					List[Any]              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					Any              =						image_size
									A							:					Optional[Any]              =						patch_size
									A							:					Optional[Any]              =						num_channels
									A							:					Tuple              =						is_training
									A							:					Optional[Any]              =						use_labels
									A							:					Union[str, Any]              =						hidden_size
									A							:					Tuple              =						num_hidden_layers
									A							:					Union[str, Any]              =						num_attention_heads
									A							:					Union[str, Any]              =						intermediate_size
									A							:					Any              =						hidden_act
									A							:					Tuple              =						hidden_dropout_prob
									A							:					Dict              =						attention_probs_dropout_prob
									A							:					Any              =						type_sequence_label_size
									A							:					Tuple              =						initializer_range
									A							:					List[Any]              =						scope
									A							:					Optional[int]              =						encoder_stride

									# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
									A							:					List[str]              =						(image_size // patch_size) ** 2
									A							:					List[str]              =						num_patches + 2







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				int:
									A							:					Union[str, Any]              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									A							:					List[Any]              =						None
									if self.use_labels:
												A							:					List[str]              =						ids_tensor([self.batch_size]	,	self.type_sequence_label_size )

									A							:					Dict              =						self.get_config()

									return config, pixel_values, labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Dict:
									return DeiTConfig(
									    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=__lowerCamelCase	,	initializer_range=self.initializer_range	,	encoder_stride=self.encoder_stride	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[str] )      ->				int:
									A							:					Optional[int]              =						DeiTModel(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					List[Any]              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Any )      ->				Any:
									A							:					List[Any]              =						DeiTForMaskedImageModeling(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Optional[int]              =						model(__lowerCamelCase )
									self.parent.assertEqual(
									    result.reconstruction.shape	,	(self.batch_size, self.num_channels, self.image_size, self.image_size) )

									# test greyscale images
									A							:					List[str]              =						1
									A							:					Optional[int]              =						DeiTForMaskedImageModeling(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()

									A							:					Dict              =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
									A							:					Optional[int]              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.reconstruction.shape	,	(self.batch_size, 1, self.image_size, self.image_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			int )      ->				Dict:
									A							:					str              =						self.type_sequence_label_size
									A							:					List[str]              =						DeiTForImageClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Tuple              =						model(__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.type_sequence_label_size) )

									# test greyscale images
									A							:					Any              =						1
									A							:					str              =						DeiTForImageClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()

									A							:					Tuple              =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
									A							:					Tuple              =						model(__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.type_sequence_label_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[int]:
									A							:					Dict              =						self.prepare_config_and_inputs()
									(
									    (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , 
									)							:					Tuple              =						config_and_inputs
									A							:					int              =						{"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (
						    (
						        DeiTModel,
						        DeiTForImageClassification,
						        DeiTForImageClassificationWithTeacher,
						        DeiTForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						a__   =  (
						    {
						        "feature-extraction": DeiTModel,
						        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[int]:
									A							:					str              =						DeiTModelTester(self )
									A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase	,	hidden_size=37 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Dict:
									self.config_tester.run_common_tests()







						@unittest.skip(reason="DeiT does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				int:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A       , A							:					Dict              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Dict              =						model_class(__lowerCamelCase )
												self.assertIsInstance(model.get_input_embeddings()	,	(nn.Module) )
												A							:					Any              =						model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__lowerCamelCase	,	nn.Linear ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												A							:					Optional[int]              =						inspect.signature(model.forward )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A							:					Any              =						[*signature.parameters.keys()]

												A							:					Any              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									A							:					Any              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Dict:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]=False )      ->				str:
									A							:					Union[str, Any]              =						super()._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )

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

									return inputs_dict







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

									A       , A							:					Optional[int]              =						self.model_tester.prepare_config_and_inputs_for_common()
									A							:					Dict              =						True

									for model_class in self.all_model_classes:
												# DeiTForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(__lowerCamelCase )
												    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
												):
															continue
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												model.to(__lowerCamelCase )
												model.train()
												A							:					Union[str, Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Dict              =						model(**__lowerCamelCase ).loss
												loss.backward()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
												return

									A							:					Tuple              =						False
									A							:					Any              =						True

									for model_class in self.all_model_classes:
												if model_class in get_values(__lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
															continue
												# DeiTForImageClassificationWithTeacher supports inference-only
												if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
															continue
												A							:					List[str]              =						model_class(__lowerCamelCase )
												model.gradient_checkpointing_enable()
												model.to(__lowerCamelCase )
												model.train()
												A							:					Dict              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Tuple              =						model(**__lowerCamelCase ).loss
												loss.backward()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

									A							:					int              =						[
									    {"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(__lowerCamelCase ),
												        *get_values(__lowerCamelCase ),
												    ]
												    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
												):
															continue

												for problem_type in problem_types:
															with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}""" ):
																		A							:					Tuple              =						problem_type["title"]
																		A							:					Optional[Any]              =						problem_type["num_labels"]

																		A							:					List[str]              =						model_class(__lowerCamelCase )
																		model.to(__lowerCamelCase )
																		model.train()

																		A							:					List[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )

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

																		A							:					int              =						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=__lowerCamelCase ) as warning_list:
																					A							:					Optional[Any]              =						model(**__lowerCamelCase ).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 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Optional[Any]              =						DeiTModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Optional[int]              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image






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






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									return (
									    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
									    if is_vision_available()
									    else None
									)







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[int]:
									A							:					Optional[int]              =						DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
									    __lowerCamelCase )

									A							:					List[Any]              =						self.default_image_processor
									A							:					List[Any]              =						prepare_img()
									A							:					Optional[Any]              =						image_processor(images=__lowerCamelCase	,	return_tensors="pt" ).to(__lowerCamelCase )

									# forward pass
									with torch.no_grad():
												A							:					List[str]              =						model(**__lowerCamelCase )

									# verify the logits
									A							:					str              =						torch.Size((1, 10_00) )
									self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

									A							:					List[str]              =						torch.tensor([-1.0266, 0.1912, -1.2861] ).to(__lowerCamelCase )

									self.assertTrue(torch.allclose(outputs.logits[0, :3]	,	__lowerCamelCase	,	atol=1e-4 ) )







						@slow
						@require_accelerate
						@require_torch_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					str              =						DeiTModel.from_pretrained(
									    "facebook/deit-base-distilled-patch16-224"	,	torch_dtype=torch.floataa	,	device_map="auto" )
									A							:					Dict              =						self.default_image_processor

									A							:					Optional[int]              =						prepare_img()
									A							:					Union[str, Any]              =						image_processor(images=__lowerCamelCase	,	return_tensors="pt" )
									A							:					Union[str, Any]              =						inputs.pixel_values.to(__lowerCamelCase )

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
												A							:					List[str]              =						model(__lowerCamelCase )