import argparse
import os
import re


__a  :     Tuple       =  """src/transformers"""

# Pattern that looks at the indentation in a line.
__a  :     List[str]       =  re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
__a  :     str       =  re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__a  :     int       =  re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
__a  :     Tuple       =  re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__a  :     int       =  re.compile(r"""\[([^\]]+)\]""")





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							_re_indent.search(lowercase							)
				return "" if search is None else search.groups()[0]





def 				UpperCAmelCase						(    lowercase							,			lowercase=""							,			lowercase=None							,			lowercase=None							):







				"""simple docstring"""



				__lowercase 			=							0
				__lowercase 			=							code.split('''\n'''							)
				if start_prompt is not None:
								while not lines[index].startswith(lowercase							):
												index += 1
								__lowercase 			=							['''\n'''.join(lines[:index]							)]
				else:
								__lowercase 			=							[]

				# We split into blocks until we get to the `end_prompt` (or the end of the block).
				__lowercase 			=							[lines[index]]
				index += 1
				while index < len(lowercase							) and (end_prompt is None or not lines[index].startswith(lowercase							)):
								if len(lines[index]							) > 0 and get_indent(lines[index]							) == indent_level:
												if len(lowercase							) > 0 and get_indent(current_block[-1]							).startswith(indent_level + ''' '''							):
																current_block.append(lines[index]							)
																blocks.append('''\n'''.join(lowercase							)							)
																if index < len(lowercase							) - 1:
																				__lowercase 			=							[lines[index + 1]]
																				index += 1
																else:
																				__lowercase 			=							[]
												else:
																blocks.append('''\n'''.join(lowercase							)							)
																__lowercase 			=							[lines[index]]
								else:
												current_block.append(lines[index]							)
								index += 1

				# Adds current block if it's nonempty.
				if len(lowercase							) > 0:
								blocks.append('''\n'''.join(lowercase							)							)

				# Add final block after end_prompt if provided.
				if end_prompt is not None and index < len(lowercase							):
								blocks.append('''\n'''.join(lines[index:]							)							)

				return blocks





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				def _inner(lowercase							):
								return key(lowercase							).lower().replace('''_'''							,			''''''							)

				return _inner





def 				UpperCAmelCase						(    lowercase							,			lowercase=None							):







				"""simple docstring"""



				def noop(lowercase							):
								return x

				if key is None:
								__lowercase 			=							noop
				# Constants are all uppercase, they go first.
				__lowercase 			=							[obj for obj in objects if key(lowercase							).isupper()]
				# Classes are not all uppercase but start with a capital, they go second.
				__lowercase 			=							[obj for obj in objects if key(lowercase							)[0].isupper() and not key(lowercase							).isupper()]
				# Functions begin with a lowercase, they go last.
				__lowercase 			=							[obj for obj in objects if not key(lowercase							)[0].isupper()]

				__lowercase 			=							ignore_underscore(lowercase							)
				return sorted(lowercase							,			key=lowercase							) + sorted(lowercase							,			key=lowercase							) + sorted(lowercase							,			key=lowercase							)





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				def _replace(lowercase							):
								__lowercase 			=							match.groups()[0]
								if "," not in imports:
												return F"[{imports}]"
								__lowercase 			=							[part.strip().replace('''"'''							,			''''''							) for part in imports.split(''','''							)]
								# We will have a final empty element if the line finished with a comma.
								if len(keys[-1]							) == 0:
												__lowercase 			=							keys[:-1]
								return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(lowercase							)]							) + "]"

				__lowercase 			=							import_statement.split('''\n'''							)
				if len(lowercase							) > 3:
								# Here we have to sort internal imports that are on several lines (one per name):
								# key: [
								#     "object1",
								#     "object2",
								#     ...
								# ]

								# We may have to ignore one or two lines on each side.
								__lowercase 			=							2 if lines[1].strip() == '''[''' else 1
								__lowercase 			=							[(i, _re_strip_line.search(lowercase							).groups()[0]) for i, line in enumerate(lines[idx:-idx]							)]
								__lowercase 			=							sort_objects(lowercase							,			key=lambda lowercase							: x[1]							)
								__lowercase 			=							[lines[x[0] + idx] for x in sorted_indices]
								return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]							)
				elif len(lowercase							) == 3:
								# Here we have to sort internal imports that are on one separate line:
								# key: [
								#     "object1", "object2", ...
								# ]
								if _re_bracket_content.search(lines[1]							) is not None:
												__lowercase 			=							_re_bracket_content.sub(_replace							,			lines[1]							)
								else:
												__lowercase 			=							[part.strip().replace('''"'''							,			''''''							) for part in lines[1].split(''','''							)]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]							) == 0:
																__lowercase 			=							keys[:-1]
												__lowercase 			=							get_indent(lines[1]							) + ''', '''.join([F"\"{k}\"" for k in sort_objects(lowercase							)]							)
								return "\n".join(lowercase							)
				else:
								# Finally we have to deal with imports fitting on one line
								__lowercase 			=							_re_bracket_content.sub(_replace							,			lowercase							)
								return import_statement





def 				UpperCAmelCase						(    lowercase							,			lowercase=True							):







				"""simple docstring"""



				with open(lowercase							,			encoding='''utf-8'''							) as f:
								__lowercase 			=							f.read()

				if "_import_structure" not in code:
								return

				# Blocks of indent level 0
				__lowercase 			=							split_code_in_indented_blocks(
				    lowercase							,			start_prompt='''_import_structure = {'''							,			end_prompt='''if TYPE_CHECKING:'''							)

				# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
				for block_idx in range(1							,			len(lowercase							) - 1							):
								# Check if the block contains some `_import_structure`s thingy to sort.
								__lowercase 			=							main_blocks[block_idx]
								__lowercase 			=							block.split('''\n'''							)

								# Get to the start of the imports.
								__lowercase 			=							0
								while line_idx < len(lowercase							) and "_import_structure" not in block_lines[line_idx]:
												# Skip dummy import blocks
												if "import dummy" in block_lines[line_idx]:
																__lowercase 			=							len(lowercase							)
												else:
																line_idx += 1
								if line_idx >= len(lowercase							):
												continue

								# Ignore beginning and last line: they don't contain anything.
								__lowercase 			=							'''\n'''.join(block_lines[line_idx:-1]							)
								__lowercase 			=							get_indent(block_lines[1]							)
								# Slit the internal block into blocks of indent level 1.
								__lowercase 			=							split_code_in_indented_blocks(lowercase							,			indent_level=lowercase							)
								# We have two categories of import key: list or _import_structure[key].append/extend
								__lowercase 			=							_re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key
								# Grab the keys, but there is a trap: some lines are empty or just comments.
								__lowercase 			=							[(pattern.search(lowercase							).groups()[0] if pattern.search(lowercase							) is not None else None) for b in internal_blocks]
								# We only sort the lines with a key.
								__lowercase 			=							[(i, key) for i, key in enumerate(lowercase							) if key is not None]
								__lowercase 			=							[x[0] for x in sorted(lowercase							,			key=lambda lowercase							: x[1]							)]

								# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
								__lowercase 			=							0
								__lowercase 			=							[]
								for i in range(len(lowercase							)							):
												if keys[i] is None:
																reorderded_blocks.append(internal_blocks[i]							)
												else:
																__lowercase 			=							sort_objects_in_import(internal_blocks[sorted_indices[count]]							)
																reorderded_blocks.append(lowercase							)
																count += 1

        # And we put our main block back together with its first and last line.
								__lowercase 			=							'''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]							)

				if code != "\n".join(lowercase							):
								if check_only:
												return True
								else:
												print(F"Overwriting {file}."							)
												with open(lowercase							,			'''w'''							,			encoding='''utf-8'''							) as f:
																f.write('''\n'''.join(lowercase							)							)





def 				UpperCAmelCase						(    lowercase=True							):







				"""simple docstring"""



				__lowercase 			=							[]
				for root, _, files in os.walk(lowercase							):
								if "__init__.py" in files:
												__lowercase 			=							sort_imports(os.path.join(lowercase							,			'''__init__.py'''							)							,			check_only=lowercase							)
												if result:
																__lowercase 			=							[os.path.join(lowercase							,			'''__init__.py'''							)]
				if len(lowercase							) > 0:
								raise ValueError(F"Would overwrite {len(lowercase							)} files, run `make style`."							)


if __name__ == "__main__":
		__a  :     int       =  argparse.ArgumentParser()
		parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
		__a  :     Optional[int]       =  parser.parse_args()

		sort_imports_in_all_inits(check_only=args.check_only)
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      (						_UpperCAmelCase ):


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,   '''hidden_sizes'''						)						)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,   '''num_attention_heads'''						)						)




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=13      ,   lowerCAmelCase__=64      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=[1_28, 2_56, 3_84]      ,   lowerCAmelCase__=[4, 6, 8]      ,   lowerCAmelCase__=[2, 3, 4]      ,   lowerCAmelCase__=[16, 16, 16]      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=[2, 2, 2]      ,   lowerCAmelCase__=[2, 2, 2]      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=2      ,   )     ->					Tuple:

							'''simple docstring'''


							__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						)     ->					Optional[int]:

							'''simple docstring'''


							__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						)     ->					int:

							'''simple docstring'''


							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      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							LevitModel(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__						)
							__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      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							self.num_labels
							__lowercase 			=							LevitForImageClassification(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__      ,   labels=lowerCAmelCase__						)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


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




@require_torch
class    _UpperCamelCase      (						_UpperCAmelCase						,_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : int			 =						(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			__a  : List[str]			 =						(
			    {
			        '''feature-extraction''': LevitModel,
			        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			__a  : int			 =						False
			__a  : Dict			 =						False
			__a  : Optional[Any]			 =						False
			__a  : Optional[int]			 =						False
			__a  : Dict			 =						False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							LevitModelTester(self						)
							__lowercase 			=							ConfigTester(self      ,   config_class=lowerCAmelCase__      ,   has_text_modality=lowerCAmelCase__      ,   hidden_size=37						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							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						)     ->					List[str]:

							'''simple docstring'''


							return





			@unittest.skip(reason='''Levit does not use inputs_embeds'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Levit does not support input and output embeddings'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Levit does not output attentions'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase 			=							model_class(lowerCAmelCase__						)
											__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]      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							def check_hidden_states_output(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						):
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.eval()

											with torch.no_grad():
															__lowercase 			=							model(**self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__						)						)

											__lowercase 			=							outputs.hidden_states

											__lowercase 			=							len(self.model_tester.depths						) + 1
											self.assertEqual(len(lowerCAmelCase__						)      ,   lowerCAmelCase__						)

											__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(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

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

											check_hidden_states_output(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)





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

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=False						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							super()._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)

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

							return inputs_dict





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							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(lowerCAmelCase__						)
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
															continue
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__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(lowerCAmelCase__						) or not model_class.supports_gradient_checkpointing:
															continue
											# LevitForImageClassificationWithTeacher supports inference-only
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
															continue
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.gradient_checkpointing_enable()
											model.to(lowerCAmelCase__						)
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__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(lowerCAmelCase__						),
											    ]
											    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(lowerCAmelCase__						)
																			model.to(lowerCAmelCase__						)
																			model.train()

																			__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)

																			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=lowerCAmelCase__						) as warning_list:
																							__lowercase 			=							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						)     ->					Dict:

							'''simple docstring'''


							for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase 			=							LevitModel.from_pretrained(lowerCAmelCase__						)
											self.assertIsNotNone(lowerCAmelCase__						)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



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




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


			"""simple docstring"""






			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    lowerCAmelCase__						)

							__lowercase 			=							self.default_image_processor
							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(images=lowerCAmelCase__      ,   return_tensors='''pt'''						).to(lowerCAmelCase__						)

							# forward pass
							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)

							# verify the logits
							__lowercase 			=							torch.Size((1, 10_00)						)
							self.assertEqual(outputs.logits.shape      ,   lowerCAmelCase__						)

							__lowercase 			=							torch.tensor([1.0448, -0.3745, -1.8317]						).to(lowerCAmelCase__						)

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