"""simple docstring"""







import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


_a  :  Tuple=				get_logger(__name__)

class 	UpperCamelCase    :



				def __init__(self       :   Optional[int]			,						_A       :   Optional[str] = None)					->			Any:
					__snake_case    :							Optional[int]   	=				(
					    os.path.join(_A			,						config.EXTRACTED_DATASETS_DIR) if cache_dir else config.EXTRACTED_DATASETS_PATH
					)
					__snake_case    :							Optional[int]   	=				Extractor



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   str)					->			str:
					from .file_utils import hash_url_to_filename

					# Path where we extract compressed archives
					# We extract in the cache dir, and get the extracted path name by hashing the original path"
					__snake_case    :							str   	=				os.path.abspath(_A)
					return os.path.join(self.extract_dir			,						hash_url_to_filename(_A))



				def       _lowercase  (self       :   List[str]			,						_A       :   str			,						_A       :   bool)					->			bool:
					return force_extract or (
					    not os.path.isfile(_A) and not (os.path.isdir(_A) and os.listdir(_A))
					)






				def       _lowercase  (self       :   Optional[Any]			,						_A       :   str			,						_A       :   bool = False)					->			str:
					__snake_case    :							int   	=				self.extractor.infer_extractor_format(_A)
					if not extractor_format:
						return input_path
					__snake_case    :							List[str]   	=				self._get_output_path(_A)
					if self._do_extract(_A			,						_A):
						self.extractor.extract(_A			,						_A			,						_A)
					return output_path







class 	UpperCamelCase    (					lowercase		):



				@classmethod
				@abstractmethod
				def       _lowercase  (cls       :   int			,						_A       :   Union[Path, str]			,						**_A       :   List[Any])					->			bool:
					...






				@staticmethod
				@abstractmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					...







class 	UpperCamelCase    (					lowercase   ,			lowercase		):
				UpperCAmelCase       :   List[bytes]					=	[]



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   int)					->			Optional[int]:
					with open(_A			,						'rb') as f:
						return f.read(_A)






				@classmethod
				def       _lowercase  (cls       :   Dict			,						_A       :   Union[Path, str]			,						_A       :   bytes = b"")					->			bool:
					if not magic_number:
						__snake_case    :							List[str]   	=				max(len(_A) for cls_magic_number in cls.magic_numbers)
						try:
							__snake_case    :							Dict   	=				cls.read_magic_number(_A			,						_A)
						except OSError:
							return False
					return any(magic_number.startswith(_A) for cls_magic_number in cls.magic_numbers)







class 	UpperCamelCase    (					lowercase		):



				@classmethod
				def       _lowercase  (cls       :   Optional[int]			,						_A       :   Union[Path, str]			,						**_A       :   str)					->			bool:
					return tarfile.is_tarfile(_A)



				@staticmethod
				def       _lowercase  (_A       :   str			,						_A       :   Optional[int])					->			List[str]:

					def resolved(_A       :   str) -> str:
						return os.path.realpath(os.path.abspath(_A))

					def badpath(_A       :   str			,						_A       :   str) -> bool:
						# joinpath will ignore base if path is absolute
						return not resolved(os.path.join(_A			,						_A)).startswith(_A)

					def badlink(_A       :   Optional[Any]			,						_A       :   str) -> bool:
						# Links are interpreted relative to the directory containing the link
						__snake_case    :							Any   	=				resolved(os.path.join(_A			,						os.path.dirname(info.name)))
						return badpath(info.linkname			,						base=_A)

					__snake_case    :							Tuple   	=				resolved(_A)

					for finfo in members:
						if badpath(finfo.name			,						_A):
							logger.error(f"Extraction of {finfo.name} is blocked (illegal path)")
						elif finfo.issym() and badlink(_A			,						_A):
							logger.error(f"Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}")
						elif finfo.islnk() and badlink(_A			,						_A):
							logger.error(f"Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}")
						else:
							yield finfo






				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					os.makedirs(_A			,						exist_ok=_A)
					__snake_case    :							str   	=				tarfile.open(_A)
					tar_file.extractall(_A			,						members=TarExtractor.safemembers(_A			,						_A))
					tar_file.close()







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Union[str, Any]					=	[B"""\x1F\x8B"""]



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					with gzip.open(_A			,						'rb') as gzip_file:
						with open(_A			,						'wb') as extracted_file:
							shutil.copyfileobj(_A			,						_A)







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   List[Any]					=	[
				    B"""PK\x03\x04""",
				    B"""PK\x05\x06""",  # empty archive
				    B"""PK\x07\x08""",  # spanned archive
				]



				@classmethod
				def       _lowercase  (cls       :   Any			,						_A       :   Union[Path, str]			,						_A       :   bytes = b"")					->			bool:
					if super().is_extractable(_A			,						magic_number=_A):
						return True
					try:
						# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
						# From: https://github.com/python/cpython/pull/5053
						from zipfile import (
						    _CD_SIGNATURE,
						    _ECD_DISK_NUMBER,
						    _ECD_DISK_START,
						    _ECD_ENTRIES_TOTAL,
						    _ECD_OFFSET,
						    _ECD_SIZE,
						    _EndRecData,
						    sizeCentralDir,
						    stringCentralDir,
						    structCentralDir,
						)

						with open(_A			,						'rb') as fp:
							__snake_case    :							Dict   	=				_EndRecData(_A)
							if endrec:
								if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
									return True  # Empty zipfiles are still zipfiles
								elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
									fp.seek(endrec[_ECD_OFFSET])  # Central directory is on the same disk
									if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
										__snake_case    :							Tuple   	=				fp.read(_A)  # CD is where we expect it to be
										if len(_A) == sizeCentralDir:
											__snake_case    :							int   	=				struct.unpack(_A			,						_A)  # CD is the right size
											if centdir[_CD_SIGNATURE] == stringCentralDir:
												return True  # First central directory entry  has correct magic number
						return False
					except Exception:  # catch all errors in case future python versions change the zipfile internals
						return False






				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					os.makedirs(_A			,						exist_ok=_A)
					with zipfile.ZipFile(_A			,						'r') as zip_file:
						zip_file.extractall(_A)
						zip_file.close()







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Optional[int]					=	[B"""\xFD\x37\x7A\x58\x5A\x00"""]



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					with lzma.open(_A) as compressed_file:
						with open(_A			,						'wb') as extracted_file:
							shutil.copyfileobj(_A			,						_A)







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Any					=	[B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""]  # RAR_ID  # RAR5_ID



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					if not config.RARFILE_AVAILABLE:
						raise ImportError('Please pip install rarfile')
					import rarfile

					os.makedirs(_A			,						exist_ok=_A)
					__snake_case    :							Optional[int]   	=				rarfile.RarFile(_A)
					rf.extractall(_A)
					rf.close()







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Any					=	[B"""\x28\xb5\x2F\xFD"""]



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					if not config.ZSTANDARD_AVAILABLE:
						raise ImportError('Please pip install zstandard')
					import zstandard as zstd

					__snake_case    :							Union[str, Any]   	=				zstd.ZstdDecompressor()
					with open(_A			,						'rb') as ifh, open(_A			,						'wb') as ofh:
						dctx.copy_stream(_A			,						_A)







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Union[str, Any]					=	[B"""\x42\x5A\x68"""]



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					with bza.open(_A			,						'rb') as compressed_file:
						with open(_A			,						'wb') as extracted_file:
							shutil.copyfileobj(_A			,						_A)







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   str					=	[B"""\x37\x7A\xBC\xAF\x27\x1C"""]



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					if not config.PY7ZR_AVAILABLE:
						raise ImportError('Please pip install py7zr')
					import pyazr

					os.makedirs(_A			,						exist_ok=_A)
					with pyazr.SevenZipFile(_A			,						'r') as archive:
						archive.extractall(_A)







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Optional[int]					=	[B"""\x04\x22\x4D\x18"""]



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   Union[Path, str])					->			None:
					if not config.LZ4_AVAILABLE:
						raise ImportError('Please pip install lz4')
					import lza.frame

					with lza.frame.open(_A			,						'rb') as compressed_file:
						with open(_A			,						'wb') as extracted_file:
							shutil.copyfileobj(_A			,						_A)







class 	UpperCamelCase    :
				#  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
				UpperCAmelCase       :   Dict[str, Type[BaseExtractor]]					=	{
				    "tar": TarExtractor,
				    "gzip": GzipExtractor,
				    "zip": ZipExtractor,
				    "xz": XzExtractor,
				    "rar": RarExtractor,
				    "zstd": ZstdExtractor,
				    "bz2": BzipaExtractor,
				    "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
				    "lz4": LzaExtractor,  # <Added version="2.4.0"/>
				}



				@classmethod
				def       _lowercase  (cls       :   Tuple)					->			Any:
					return max(
					    len(_A)
					    for extractor in cls.extractors.values()
					    if issubclass(_A			,						_A)
					    for extractor_magic_number in extractor.magic_numbers)



				@staticmethod
				def       _lowercase  (_A       :   Union[Path, str]			,						_A       :   int)					->			Tuple:
					try:
						return MagicNumberBaseExtractor.read_magic_number(_A			,						magic_number_length=_A)
					except OSError:
						return b""



				@classmethod
				def       _lowercase  (cls       :   Optional[int]			,						_A       :   Union[Path, str]			,						_A       :   bool = False)					->			bool:
					warnings.warn(
					    'Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
					    'Use \'infer_extractor_format\' instead.'			,						category=_A			,						)
					__snake_case    :							Tuple   	=				cls.infer_extractor_format(_A)
					if extractor_format:
						return True if not return_extractor else (True, cls.extractors[extractor_format])
					return False if not return_extractor else (False, None)



				@classmethod
				def       _lowercase  (cls       :   Union[str, Any]			,						_A       :   Union[Path, str])					->			str:  # <Added version="2.4.0"/>
					__snake_case    :							int   	=				cls._get_magic_number_max_length()
					__snake_case    :							int   	=				cls._read_magic_number(_A			,						_A)
					for extractor_format, extractor in cls.extractors.items():
						if extractor.is_extractable(_A			,						magic_number=_A):
							return extractor_format






				@classmethod
				def       _lowercase  (cls       :   Optional[Any]			,						_A       :   Union[Path, str]			,						_A       :   Union[Path, str]			,						_A       :   Optional[str] = None			,						_A       :   Optional[BaseExtractor] = "deprecated"			,						)					->			None:
					os.makedirs(os.path.dirname(_A)			,						exist_ok=_A)
					# Prevent parallel extractions
					__snake_case    :							Any   	=				str(Path(_A).with_suffix('.lock'))
					with FileLock(_A):
						shutil.rmtree(_A			,						ignore_errors=_A)
						if extractor_format or extractor != "deprecated":
							if extractor != "deprecated" or not isinstance(_A			,						_A):  # passed as positional arg
								warnings.warn(
								    'Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
								    'Use \'extractor_format\' instead.'			,						category=_A			,						)
								__snake_case    :							List[Any]   	=				extractor if extractor != 'deprecated' else extractor_format
							else:
								__snake_case    :							Optional[Any]   	=				cls.extractors[extractor_format]
							return extractor.extract(_A			,						_A)
						else:
							warnings.warn(
							    'Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '
							    'exception in 3.0.0.'			,						category=_A			,						)
							for extractor in cls.extractors.values():
								if extractor.is_extractable(_A):
									return extractor.extract(_A			,						_A)






"""simple docstring"""







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 	UpperCamelCase    (					lowercase		):



				def       _lowercase  (self       :   List[str])					->			Dict:
					__snake_case    :							Any   	=				self.config_class(**self.inputs_dict)
					self.parent.assertTrue(hasattr(_A			,						'hidden_sizes'))
					self.parent.assertTrue(hasattr(_A			,						'num_attention_heads'))







class 	UpperCamelCase    :



				def __init__(self       :   str			,						_A       :   Dict			,						_A       :   Dict=13			,						_A       :   int=64			,						_A       :   Tuple=3			,						_A       :   List[str]=3			,						_A       :   Tuple=2			,						_A       :   int=1			,						_A       :   Optional[int]=16			,						_A       :   Tuple=[1_28, 2_56, 3_84]			,						_A       :   List[Any]=[4, 6, 8]			,						_A       :   int=[2, 3, 4]			,						_A       :   Union[str, Any]=[16, 16, 16]			,						_A       :   List[Any]=0			,						_A       :   Optional[int]=[2, 2, 2]			,						_A       :   List[Any]=[2, 2, 2]			,						_A       :   Dict=0.02			,						_A       :   str=True			,						_A       :   List[str]=True			,						_A       :   Tuple=2			,						)					->			Tuple:
					__snake_case    :							Optional[int]   	=				parent
					__snake_case    :							int   	=				batch_size
					__snake_case    :							Tuple   	=				image_size
					__snake_case    :							Dict   	=				num_channels
					__snake_case    :							Optional[int]   	=				kernel_size
					__snake_case    :							Union[str, Any]   	=				stride
					__snake_case    :							Dict   	=				padding
					__snake_case    :							Optional[int]   	=				hidden_sizes
					__snake_case    :							Any   	=				num_attention_heads
					__snake_case    :							Optional[Any]   	=				depths
					__snake_case    :							Dict   	=				key_dim
					__snake_case    :							Dict   	=				drop_path_rate
					__snake_case    :							Optional[int]   	=				patch_size
					__snake_case    :							List[Any]   	=				attention_ratio
					__snake_case    :							List[str]   	=				mlp_ratio
					__snake_case    :							str   	=				initializer_range
					__snake_case    :							str   	=				[
					    ['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],
					]
					__snake_case    :							List[str]   	=				is_training
					__snake_case    :							Dict   	=				use_labels
					__snake_case    :							Optional[int]   	=				num_labels
					__snake_case    :							Optional[int]   	=				initializer_range



				def       _lowercase  (self       :   Optional[Any])					->			Dict:
					__snake_case    :							List[Any]   	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

					__snake_case    :							Tuple   	=				self.get_config()
					return config, pixel_values, labels



				def       _lowercase  (self       :   List[str])					->			Optional[Any]:
					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       _lowercase  (self       :   List[Any]			,						_A       :   int			,						_A       :   Optional[Any]			,						_A       :   List[str])					->			str:
					__snake_case    :							Tuple   	=				LevitModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							List[str]   	=				model(_A)
					__snake_case    :							List[Any]   	=				(self.image_size, self.image_size)
					__snake_case	,       __snake_case    :							List[Any]   	=				image_size[0], image_size[1]
					for _ in range(4):
						__snake_case    :							str   	=				floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
						__snake_case    :							Optional[int]   	=				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       _lowercase  (self       :   str			,						_A       :   Optional[Any]			,						_A       :   List[str]			,						_A       :   int)					->			Optional[Any]:
					__snake_case    :							int   	=				self.num_labels
					__snake_case    :							Any   	=				LevitForImageClassification(_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Tuple   	=				model(_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))






				def       _lowercase  (self       :   List[str])					->			Any:
					__snake_case    :							Optional[int]   	=				self.prepare_config_and_inputs()
					__snake_case	,       __snake_case	,       __snake_case    :							Dict   	=				config_and_inputs
					__snake_case    :							str   	=				{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Union[str, Any]					=	(
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase       :   Optional[Any]					=	(
				    {
				        """feature-extraction""": LevitModel,
				        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase       :   str					=	False
				UpperCAmelCase       :   Optional[Any]					=	False
				UpperCAmelCase       :   Optional[int]					=	False
				UpperCAmelCase       :   Any					=	False
				UpperCAmelCase       :   str					=	False



				def       _lowercase  (self       :   Any)					->			List[Any]:
					__snake_case    :							Any   	=				LevitModelTester(self)
					__snake_case    :							List[Any]   	=				ConfigTester(self			,						config_class=_A			,						has_text_modality=_A			,						hidden_size=37)



				def       _lowercase  (self       :   int)					->			int:
					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       _lowercase  (self       :   Dict)					->			Tuple:
					return



				@unittest.skip(reason='Levit does not use inputs_embeds')
				def       _lowercase  (self       :   int)					->			Dict:
					pass



				@unittest.skip(reason='Levit does not support input and output embeddings')
				def       _lowercase  (self       :   Tuple)					->			Optional[int]:
					pass



				@unittest.skip(reason='Levit does not output attentions')
				def       _lowercase  (self       :   List[str])					->			Dict:
					pass



				def       _lowercase  (self       :   str)					->			List[str]:
					__snake_case	,       __snake_case    :							Optional[int]   	=				self.model_tester.prepare_config_and_inputs_for_common()

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

						__snake_case    :							str   	=				['pixel_values']
						self.assertListEqual(arg_names[:1]			,						_A)



				def       _lowercase  (self       :   Dict)					->			Optional[int]:
					def check_hidden_states_output(_A       :   Any			,						_A       :   List[Any]			,						_A       :   int):
						__snake_case    :							int   	=				model_class(_A)
						model.to(_A)
						model.eval()

						with torch.no_grad():
							__snake_case    :							Optional[Any]   	=				model(**self._prepare_for_class(_A			,						_A))

						__snake_case    :							Union[str, Any]   	=				outputs.hidden_states

						__snake_case    :							List[str]   	=				len(self.model_tester.depths) + 1
						self.assertEqual(len(_A)			,						_A)

						__snake_case    :							Any   	=				(self.model_tester.image_size, self.model_tester.image_size)
						__snake_case	,       __snake_case    :							str   	=				image_size[0], image_size[1]
						for _ in range(4):
							__snake_case    :							Tuple   	=				floor(
							    (
							        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
							        / self.model_tester.stride
							    )
							    + 1)
							__snake_case    :							List[str]   	=				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],
						    ]			,						)

					__snake_case	,       __snake_case    :							str   	=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						__snake_case    :							Optional[Any]   	=				True
						check_hidden_states_output(_A			,						_A			,						_A)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__snake_case    :							List[Any]   	=				True

						check_hidden_states_output(_A			,						_A			,						_A)



				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
				def       _lowercase  (self       :   List[Any])					->			Optional[int]:
					pass



				def       _lowercase  (self       :   List[Any]			,						_A       :   Optional[Any]			,						_A       :   List[Any]			,						_A       :   Dict=False)					->			Tuple:
					__snake_case    :							Any   	=				super()._prepare_for_class(_A			,						_A			,						return_labels=_A)

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

					return inputs_dict



				def       _lowercase  (self       :   Union[str, Any])					->			Any:
					__snake_case    :							Optional[int]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							int   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_A)



				def       _lowercase  (self       :   Dict)					->			List[Any]:
					if not self.model_tester.is_training:
						return

					__snake_case	,       __snake_case    :							Union[str, Any]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							Union[str, Any]   	=				True

					for model_class in self.all_model_classes:
						# LevitForImageClassificationWithTeacher supports inference-only
						if (
						    model_class in get_values(_A)
						    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
						):
							continue
						__snake_case    :							Any   	=				model_class(_A)
						model.to(_A)
						model.train()
						__snake_case    :							Optional[Any]   	=				self._prepare_for_class(_A			,						_A			,						return_labels=_A)
						__snake_case    :							Dict   	=				model(**_A).loss
						loss.backward()



				def       _lowercase  (self       :   Union[str, Any])					->			int:
					__snake_case	,       __snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs_for_common()
					if not self.model_tester.is_training:
						return

					__snake_case    :							str   	=				False
					__snake_case    :							List[str]   	=				True

					for model_class in self.all_model_classes:
						if model_class in get_values(_A) or not model_class.supports_gradient_checkpointing:
							continue
						# LevitForImageClassificationWithTeacher supports inference-only
						if model_class.__name__ == "LevitForImageClassificationWithTeacher":
							continue
						__snake_case    :							Optional[int]   	=				model_class(_A)
						model.gradient_checkpointing_enable()
						model.to(_A)
						model.train()
						__snake_case    :							Optional[int]   	=				self._prepare_for_class(_A			,						_A			,						return_labels=_A)
						__snake_case    :							Optional[int]   	=				model(**_A).loss
						loss.backward()



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

					__snake_case    :							List[Any]   	=				[
					    {'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(_A),
						    ]
						    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
						):
							continue

						for problem_type in problem_types:
							with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
								__snake_case    :							int   	=				problem_type['title']
								__snake_case    :							List[Any]   	=				problem_type['num_labels']

								__snake_case    :							List[str]   	=				model_class(_A)
								model.to(_A)
								model.train()

								__snake_case    :							Optional[Any]   	=				self._prepare_for_class(_A			,						_A			,						return_labels=_A)

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

								__snake_case    :							List[Any]   	=				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=_A) as warning_list:
									__snake_case    :							Optional[Any]   	=				model(**_A).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       _lowercase  (self       :   Dict)					->			Tuple:
					for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case    :							List[str]   	=				LevitModel.from_pretrained(_A)
						self.assertIsNotNone(_A)







def 						__UpperCAmelCase       (					)							->    List[str]:

	'''simple docstring'''





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

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



				@cached_property
				def       _lowercase  (self       :   Tuple)					->			Union[str, Any]:
					return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])






				@slow
				def       _lowercase  (self       :   Optional[int])					->			str:
					__snake_case    :							int   	=				LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
					    _A)

					__snake_case    :							Any   	=				self.default_image_processor
					__snake_case    :							Optional[Any]   	=				prepare_img()
					__snake_case    :							Union[str, Any]   	=				image_processor(images=_A			,						return_tensors='pt').to(_A)

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

					# verify the logits
					__snake_case    :							List[str]   	=				torch.Size((1, 10_00))
					self.assertEqual(outputs.logits.shape			,						_A)

					__snake_case    :							List[str]   	=				torch.tensor([1.0_448, -0.3_745, -1.8_317]).to(_A)

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





