






"""simple docstring"""



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def 						lowercase    (				__snake_case				: int		,					__snake_case				: Dict		,					__snake_case				: List[str]		,					__snake_case				: Optional[int]		,					__snake_case				: str=True		,					__snake_case				: int="pt"	):
		lowercase_ : int							       =   {'''add_prefix_space''': True} if isinstance(__snake_case		,					__snake_case	) and not line.startswith(''' '''	) else {}
		lowercase_ : str							       =   padding_side
		return tokenizer(
		    [line]		,					max_length=__snake_case		,					padding='''max_length''' if pad_to_max_length else None		,					truncation=__snake_case		,					return_tensors=__snake_case		,					add_special_tokens=__snake_case		,					**__snake_case		,					)



def 						lowercase    (				__snake_case				: Dict		,					__snake_case				: Dict		,					__snake_case				: int=None		,					):
		lowercase_ : Optional[Any]							       =   input_ids.ne(__snake_case	).any(dim=0	)
		if attention_mask is None:
				return input_ids[:, keep_column_mask]
		else:
				return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class 					_UpperCAmelCase       ( _A							):




					def __init__(				self     :						Any    ,						A     :						str    ,						A     :						Optional[Any]    ,						A     :						int    ,						A     :						Optional[Any]    ,						A     :						Optional[int]="train"    ,						A     :						Dict=None    ,						A     :						int=None    ,						A     :						Union[str, Any]=None    ,						A     :						List[str]=""    ,						)  ->  List[str]:
							super().__init__()
							lowercase_ : Any							       =   Path(A   ).joinpath(type_path + '''.source'''   )
							lowercase_ : int							       =   Path(A   ).joinpath(type_path + '''.target'''   )
							lowercase_ : Optional[Any]							       =   self.get_char_lens(self.src_file   )
							lowercase_ : str							       =   max_source_length
							lowercase_ : Any							       =   max_target_length
							assert min(self.src_lens   ) > 0, F'''found empty line in {self.src_file}'''
							lowercase_ : Union[str, Any]							       =   tokenizer
							lowercase_ : Optional[int]							       =   prefix
							if n_obs is not None:
									lowercase_ : Union[str, Any]							       =   self.src_lens[:n_obs]
							lowercase_ : str							       =   src_lang
							lowercase_ : Any							       =   tgt_lang




					def __len__(				self     :						List[Any]   )  ->  str:
							return len(self.src_lens   )




					def __getitem__(				self     :						Any    ,						A     :						List[str]   )  ->  Dict[str, torch.Tensor]:
							lowercase_ : List[str]							       =   index + 1  # linecache starts at 1
							lowercase_ : int							       =   self.prefix + linecache.getline(str(self.src_file   )    ,						A   ).rstrip('''\n'''   )
							lowercase_ : int							       =   linecache.getline(str(self.tgt_file   )    ,						A   ).rstrip('''\n'''   )
							assert source_line, F'''empty source line for index {index}'''
							assert tgt_line, F'''empty tgt line for index {index}'''

							# Need to add eos token manually for T5
							if isinstance(self.tokenizer    ,						A   ):
									source_line += self.tokenizer.eos_token
									tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							lowercase_ : Tuple							       =   (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer    ,						A   ) else self.tokenizer
							)
							lowercase_ : Any							       =   self.tokenizer.generator if isinstance(self.tokenizer    ,						A   ) else self.tokenizer

							lowercase_ : str							       =   encode_line(A    ,						A    ,						self.max_source_length    ,						'''right'''   )
							lowercase_ : Optional[Any]							       =   encode_line(A    ,						A    ,						self.max_target_length    ,						'''right'''   )

							lowercase_ : Any							       =   source_inputs['''input_ids'''].squeeze()
							lowercase_ : List[Any]							       =   target_inputs['''input_ids'''].squeeze()
							lowercase_ : List[str]							       =   source_inputs['''attention_mask'''].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}




					@staticmethod
					def   A  (				A     :						Tuple   )  ->  List[str]:
							return [len(A   ) for x in Path(A   ).open().readlines()]






					def   A  (				self     :						Dict    ,						A     :						Union[str, Any]   )  ->  Dict[str, torch.Tensor]:
							lowercase_ : List[Any]							       =   torch.stack([x['''input_ids'''] for x in batch]   )
							lowercase_ : str							       =   torch.stack([x['''attention_mask'''] for x in batch]   )
							lowercase_ : int							       =   torch.stack([x['''decoder_input_ids'''] for x in batch]   )
							lowercase_ : str							       =   (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer    ,						A   )
							    else self.tokenizer.pad_token_id
							)
							lowercase_ : Optional[Any]							       =   (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer    ,						A   )
							    else self.tokenizer.pad_token_id
							)
							lowercase_ : int							       =   trim_batch(A    ,						A   )
							lowercase_       ,					lowercase_ : List[Any]							       =   trim_batch(A    ,						A    ,						attention_mask=A   )
							lowercase_ : List[str]							       =   {
							    '''input_ids''': source_ids,
							    '''attention_mask''': source_mask,
							    '''decoder_input_ids''': y,
							}
							return batch
__A		:       Tuple  				=		getLogger(__name__)



def 						lowercase    (				__snake_case				: List[List]	):
		return list(itertools.chain.from_iterable(__snake_case	)	)



def 						lowercase    (				__snake_case				: str	):
		lowercase_ : str							       =   get_git_info()
		save_json(__snake_case		,					os.path.join(__snake_case		,					'''git_log.json'''	)	)



def 						lowercase    (				__snake_case				: int		,					__snake_case				: List[Any]		,					__snake_case				: Any=4		,					**__snake_case				: Dict	):
		with open(__snake_case		,					'''w'''	) as f:
				json.dump(__snake_case		,					__snake_case		,					indent=__snake_case		,					**__snake_case	)



def 						lowercase    (				__snake_case				: Tuple	):
		with open(__snake_case	) as f:
				return json.load(__snake_case	)



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   git.Repo(search_parent_directories=__snake_case	)
		lowercase_ : Optional[Any]							       =   {
		    '''repo_id''': str(__snake_case	),
		    '''repo_sha''': str(repo.head.object.hexsha	),
		    '''repo_branch''': str(repo.active_branch	),
		    '''hostname''': str(socket.gethostname()	),
		}
		return repo_infos



def 						lowercase    (				__snake_case				: Callable		,					__snake_case				: Iterable	):
		return list(map(__snake_case		,					__snake_case	)	)



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: List[str]	):
		with open(__snake_case		,					'''wb'''	) as f:
				return pickle.dump(__snake_case		,					__snake_case	)



def 						lowercase    (				__snake_case				: Any	):

		def remove_articles(__snake_case				: List[Any]	):
				return re.sub(r'''\b(a|an|the)\b'''		,					''' '''		,					__snake_case	)

		def white_space_fix(__snake_case				: List[str]	):
				return " ".join(text.split()	)

		def remove_punc(__snake_case				: Optional[int]	):
				lowercase_ : str							       =   set(string.punctuation	)
				return "".join(ch for ch in text if ch not in exclude	)

		def lower(__snake_case				: Tuple	):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(__snake_case	)	)	)	)



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Dict	):
		lowercase_ : Optional[Any]							       =   normalize_answer(__snake_case	).split()
		lowercase_ : List[Any]							       =   normalize_answer(__snake_case	).split()
		lowercase_ : str							       =   Counter(__snake_case	) & Counter(__snake_case	)
		lowercase_ : Union[str, Any]							       =   sum(common.values()	)
		if num_same == 0:
				return 0
		lowercase_ : Dict							       =   1.0 * num_same / len(__snake_case	)
		lowercase_ : str							       =   1.0 * num_same / len(__snake_case	)
		lowercase_ : Union[str, Any]							       =   (2 * precision * recall) / (precision + recall)
		return fa



def 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: Any	):
		return normalize_answer(__snake_case	) == normalize_answer(__snake_case	)



def 						lowercase    (				__snake_case				: List[str]		,					__snake_case				: List[str]	):
		assert len(__snake_case	) == len(__snake_case	)
		lowercase_ : Union[str, Any]							       =   0
		for hypo, pred in zip(__snake_case		,					__snake_case	):
				em += exact_match_score(__snake_case		,					__snake_case	)
		if len(__snake_case	) > 0:
				em /= len(__snake_case	)
		return {"em": em}



def 						lowercase    (				__snake_case				: List[str]	):
		return model_prefix.startswith('''rag'''	)






def 						lowercase    (				__snake_case				: Optional[int]		,					__snake_case				: str		,					__snake_case				: Union[str, Any]	):
		lowercase_ : Union[str, Any]							       =   {p: p for p in extra_params}
		# T5 models don't have `dropout` param, they have `dropout_rate` instead
		lowercase_ : List[str]							       =   '''dropout_rate'''
		for p in extra_params:
				if getattr(__snake_case		,					__snake_case		,					__snake_case	):
						if not hasattr(__snake_case		,					__snake_case	) and not hasattr(__snake_case		,					equivalent_param[p]	):
								logger.info('''config doesn\'t have a `{}` attribute'''.format(__snake_case	)	)
								delattr(__snake_case		,					__snake_case	)
								continue
						lowercase_ : Tuple							       =   p if hasattr(__snake_case		,					__snake_case	) else equivalent_param[p]
						setattr(__snake_case		,					__snake_case		,					getattr(__snake_case		,					__snake_case	)	)
						delattr(__snake_case		,					__snake_case	)
		return hparams, config





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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,
	    LevitForImageClassification,
	    LevitForImageClassificationWithTeacher,
	    LevitModel,
	)
	from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.config_class(**self.inputs_dict       )
		self.parent.assertTrue(hasattr(snake_case		,    'hidden_sizes'       )       )
		self.parent.assertTrue(hasattr(snake_case		,    'num_attention_heads'       )       )







class        A_   :




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=13		,    snake_case=64		,    snake_case=3		,    snake_case=3		,    snake_case=2		,    snake_case=1		,    snake_case=16		,    snake_case=[128, 256, 384]		,    snake_case=[4, 6, 8]		,    snake_case=[2, 3, 4]		,    snake_case=[16, 16, 16]		,    snake_case=0		,    snake_case=[2, 2, 2]		,    snake_case=[2, 2, 2]		,    snake_case=0.02		,    snake_case=True		,    snake_case=True		,    snake_case=2		,    ):
		lowercase       =    parent
		lowercase       =    batch_size
		lowercase       =    image_size
		lowercase       =    num_channels
		lowercase       =    kernel_size
		lowercase       =    stride
		lowercase       =    padding
		lowercase       =    hidden_sizes
		lowercase       =    num_attention_heads
		lowercase       =    depths
		lowercase       =    key_dim
		lowercase       =    drop_path_rate
		lowercase       =    patch_size
		lowercase       =    attention_ratio
		lowercase       =    mlp_ratio
		lowercase       =    initializer_range
		lowercase       =    [
		    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
		    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
		]
		lowercase       =    is_training
		lowercase       =    use_labels
		lowercase       =    num_labels
		lowercase       =    initializer_range





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		lowercase       =    None
		if self.use_labels:
			lowercase       =    ids_tensor([self.batch_size]		,    self.num_labels       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return LevitConfig(
		    image_size=self.image_size		,    num_channels=self.num_channels		,    kernel_size=self.kernel_size		,    stride=self.stride		,    padding=self.padding		,    patch_size=self.patch_size		,    hidden_sizes=self.hidden_sizes		,    num_attention_heads=self.num_attention_heads		,    depths=self.depths		,    key_dim=self.key_dim		,    drop_path_rate=self.drop_path_rate		,    mlp_ratio=self.mlp_ratio		,    attention_ratio=self.attention_ratio		,    initializer_range=self.initializer_range		,    down_ops=self.down_ops		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    LevitModel(config=snake_case       )
		model.to(snake_case       )
		model.eval()
		lowercase       =    model(snake_case       )
		lowercase       =    (self.image_size, self.image_size)
		lowercase  ,					lowercase       =    image_size[0], image_size[1]
		for _ in range(4       ):
			lowercase       =    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1       )
			lowercase       =    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1       )
		self.parent.assertEqual(
		    result.last_hidden_state.shape		,    (self.batch_size, ceil(height / 4       ) * ceil(width / 4       ), self.hidden_sizes[-1])		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    self.num_labels
		lowercase       =    LevitForImageClassification(snake_case       )
		model.to(snake_case       )
		model.eval()
		lowercase       =    model(snake_case		,    labels=snake_case       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.prepare_config_and_inputs()
		lowercase  ,					lowercase  ,					lowercase       =    config_and_inputs
		lowercase       =    {'pixel_values': pixel_values}
		return config, inputs_dict







@require_torch
class        A_   (			__lowerCamelCase      ,	__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       (
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	_UpperCamelCase     :     Dict							 =       (
	    {
	        """feature-extraction""": LevitModel,
	        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	_UpperCamelCase     :     Dict							 =       False
	_UpperCamelCase     :     List[str]							 =       False
	_UpperCamelCase     :     List[str]							 =       False
	_UpperCamelCase     :     str							 =       False
	_UpperCamelCase     :     List[str]							 =       False





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    LevitModelTester(self       )
		lowercase       =    ConfigTester(self		,    config_class=snake_case		,    has_text_modality=snake_case		,    hidden_size=37       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return





	@unittest.skip(reason='Levit does not use inputs_embeds'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	@unittest.skip(reason='Levit does not support input and output embeddings'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	@unittest.skip(reason='Levit does not output attentions'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

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

			lowercase       =    ['pixel_values']
			self.assertListEqual(arg_names[:1]		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		def check_hidden_states_output(snake_case		,    snake_case		,    snake_case       ):
			lowercase       =    model_class(snake_case       )
			model.to(snake_case       )
			model.eval()

			with torch.no_grad():
				lowercase       =    model(**self._prepare_for_class(snake_case		,    snake_case       )       )

			lowercase       =    outputs.hidden_states

			lowercase       =    len(self.model_tester.depths       ) + 1
			self.assertEqual(len(snake_case       )		,    snake_case       )

			lowercase       =    (self.model_tester.image_size, self.model_tester.image_size)
			lowercase  ,					lowercase       =    image_size[0], image_size[1]
			for _ in range(4       ):
				lowercase       =    floor(
				    (
				        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
				        / self.model_tester.stride
				    )
				    + 1       )
				lowercase       =    floor(
				    (
				        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
				        / self.model_tester.stride
				    )
				    + 1       )
			# verify the first hidden states (first block)
			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]       )		,    [
			        height * width,
			        self.model_tester.hidden_sizes[0],
			    ]		,    )

		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowercase       =    True
			check_hidden_states_output(snake_case		,    snake_case		,    snake_case       )

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

			check_hidden_states_output(snake_case		,    snake_case		,    snake_case       )





	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case=False       ):
		lowercase       =    super()._prepare_for_class(snake_case		,    snake_case		,    return_labels=snake_case       )

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

		return inputs_dict





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		if not self.model_tester.is_training:
			return

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()
		if not self.model_tester.is_training:
			return

		lowercase       =    False
		lowercase       =    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
			# LevitForImageClassificationWithTeacher supports inference-only
			if model_class.__name__ == "LevitForImageClassificationWithTeacher":
				continue
			lowercase       =    model_class(snake_case       )
			model.gradient_checkpointing_enable()
			model.to(snake_case       )
			model.train()
			lowercase       =    self._prepare_for_class(snake_case		,    snake_case		,    return_labels=snake_case       )
			lowercase       =    model(**snake_case       ).loss
			loss.backward()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		lowercase       =    [
		    {'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(snake_case       ),
			    ]
			    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
			):
				continue

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

					lowercase       =    model_class(snake_case       )
					model.to(snake_case       )
					model.train()

					lowercase       =    self._prepare_for_class(snake_case		,    snake_case		,    return_labels=snake_case       )

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

					lowercase       =    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=snake_case       ) as warning_list:
						lowercase       =    model(**snake_case       ).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       ):
		for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase       =    LevitModel.from_pretrained(snake_case       )
			self.assertIsNotNone(snake_case       )






def 							UpperCAmelCase_		(							):
	lowercase       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







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




	'''simple docstring'''


	@cached_property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
		    snake_case       )

		lowercase       =    self.default_image_processor
		lowercase       =    prepare_img()
		lowercase       =    image_processor(images=snake_case		,    return_tensors='pt'       ).to(snake_case       )

		# forward pass
		with torch.no_grad():
			lowercase       =    model(**snake_case       )

		# verify the logits
		lowercase       =    torch.Size((1, 1000)       )
		self.assertEqual(outputs.logits.shape		,    snake_case       )

		lowercase       =    torch.tensor([1.0_448, -0.3_745, -1.8_317]       ).to(snake_case       )

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

