





import collections
import os
import re
from pathlib import Path


_lowerCamelCase :    List[Any]  							= '''src/transformers'''


# Matches is_xxx_available()
_lowerCamelCase :    Tuple  							= re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
_lowerCamelCase :    Optional[int]  							= re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_lowerCamelCase :    List[str]  							= re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
_lowerCamelCase :    Optional[int]  							= re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
_lowerCamelCase :    Union[str, Any]  							= re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_lowerCamelCase :    int  							= re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
_lowerCamelCase :    List[Any]  							= re.compile(r'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
_lowerCamelCase :    Union[str, Any]  							= re.compile(r'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
_lowerCamelCase :    Dict  							= re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
_lowerCamelCase :    str  							= re.compile(r'''^\s*try:''')
# Catches a line with else:
_lowerCamelCase :    Any  							= re.compile(r'''^\s*else:''')




def _a (  SCREAMING_SNAKE_CASE__      :  int       )	->    List[str]:

							'''simple docstring'''






							if _re_test_backend.search(SCREAMING_SNAKE_CASE__       ) is None:
														return None
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE__       )]
							backends.sort()
							return "_and_".join(SCREAMING_SNAKE_CASE__       )




def _a (  SCREAMING_SNAKE_CASE__      :  List[str]       )	->    int:

							'''simple docstring'''






							with open(SCREAMING_SNAKE_CASE__			,				"r"			,				encoding="utf-8"			,				newline="\n"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						f.readlines()

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						0
							while line_index < len(SCREAMING_SNAKE_CASE__       ) and not lines[line_index].startswith("_import_structure = {"       ):
														line_index += 1

							# If this is a traditional init, just return.
							if line_index >= len(SCREAMING_SNAKE_CASE__       ):
														return None

							# First grab the objects without a specific backend in _import_structure
							SCREAMING_SNAKE_CASE__       :      int     =						[]
							while not lines[line_index].startswith("if TYPE_CHECKING"       ) and find_backend(lines[line_index]       ) is None:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						lines[line_index]
														# If we have everything on a single line, let's deal with it.
														if _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE__       ):
																					SCREAMING_SNAKE_CASE__       :      str     =						_re_one_line_import_struct.search(SCREAMING_SNAKE_CASE__       ).groups()[0]
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						re.findall(R"\[([^\]]+)\]"			,				SCREAMING_SNAKE_CASE__       )
																					for imp in imports:
																												objects.extend([obj[1:-1] for obj in imp.split(", "       )]       )
																					line_index += 1
																					continue
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_re_import_struct_key_value.search(SCREAMING_SNAKE_CASE__       )
														if single_line_import_search is not None:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "       ) if len(SCREAMING_SNAKE_CASE__       ) > 0]
																					objects.extend(SCREAMING_SNAKE_CASE__       )
														elif line.startswith(" " * 8 + "\""       ):
																					objects.append(line[9:-3]       )
														line_index += 1

							SCREAMING_SNAKE_CASE__       :      List[str]     =						{"none": objects}
							# Let's continue with backend-specific objects in _import_structure
							while not lines[line_index].startswith("if TYPE_CHECKING"       ):
														# If the line is an if not is_backend_available, we grab all objects associated.
														SCREAMING_SNAKE_CASE__       :      Tuple     =						find_backend(lines[line_index]       )
														# Check if the backend declaration is inside a try block:
														if _re_try.search(lines[line_index - 1]       ) is None:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						None

														if backend is not None:
																					line_index += 1

																					# Scroll until we hit the else block of try-except-else
																					while _re_else.search(lines[line_index]       ) is None:
																												line_index += 1

																					line_index += 1

																					SCREAMING_SNAKE_CASE__       :      Any     =						[]
																					# Until we unindent, add backend objects to the list
																					while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 4       ):
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						lines[line_index]
																												if _re_import_struct_add_one.search(SCREAMING_SNAKE_CASE__       ) is not None:
																																			objects.append(_re_import_struct_add_one.search(SCREAMING_SNAKE_CASE__       ).groups()[0]       )
																												elif _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE__       ) is not None:
																																			SCREAMING_SNAKE_CASE__       :      Dict     =						_re_import_struct_add_many.search(SCREAMING_SNAKE_CASE__       ).groups()[0].split(", "       )
																																			SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE__       ) > 0]
																																			objects.extend(SCREAMING_SNAKE_CASE__       )
																												elif _re_between_brackets.search(SCREAMING_SNAKE_CASE__       ) is not None:
																																			SCREAMING_SNAKE_CASE__       :      int     =						_re_between_brackets.search(SCREAMING_SNAKE_CASE__       ).groups()[0].split(", "       )
																																			SCREAMING_SNAKE_CASE__       :      Any     =						[obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE__       ) > 0]
																																			objects.extend(SCREAMING_SNAKE_CASE__       )
																												elif _re_quote_object.search(SCREAMING_SNAKE_CASE__       ) is not None:
																																			objects.append(_re_quote_object.search(SCREAMING_SNAKE_CASE__       ).groups()[0]       )
																												elif line.startswith(" " * 8 + "\""       ):
																																			objects.append(line[9:-3]       )
																												elif line.startswith(" " * 12 + "\""       ):
																																			objects.append(line[13:-3]       )
																												line_index += 1

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						objects
														else:
																					line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[]
							while (
							    line_index < len(SCREAMING_SNAKE_CASE__       )
							    and find_backend(lines[line_index]       ) is None
							    and not lines[line_index].startswith("else"       )
							):
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						lines[line_index]
														SCREAMING_SNAKE_CASE__       :      str     =						_re_import.search(SCREAMING_SNAKE_CASE__       )
														if single_line_import_search is not None:
																					objects.extend(single_line_import_search.groups()[0].split(", "       )       )
														elif line.startswith(" " * 8       ):
																					objects.append(line[8:-2]       )
														line_index += 1

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{"none": objects}
							# Let's continue with backend-specific objects
							while line_index < len(SCREAMING_SNAKE_CASE__       ):
														# If the line is an if is_backend_available, we grab all objects associated.
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						find_backend(lines[line_index]       )
														# Check if the backend declaration is inside a try block:
														if _re_try.search(lines[line_index - 1]       ) is None:
																					SCREAMING_SNAKE_CASE__       :      str     =						None

														if backend is not None:
																					line_index += 1

																					# Scroll until we hit the else block of try-except-else
																					while _re_else.search(lines[line_index]       ) is None:
																												line_index += 1

																					line_index += 1

																					SCREAMING_SNAKE_CASE__       :      List[str]     =						[]
																					# Until we unindent, add backend objects to the list
																					while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 8       ):
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						lines[line_index]
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						_re_import.search(SCREAMING_SNAKE_CASE__       )
																												if single_line_import_search is not None:
																																			objects.extend(single_line_import_search.groups()[0].split(", "       )       )
																												elif line.startswith(" " * 12       ):
																																			objects.append(line[12:-2]       )
																												line_index += 1

																					SCREAMING_SNAKE_CASE__       :      Dict     =						objects
														else:
																					line_index += 1

							return import_dict_objects, type_hint_objects




def _a (  SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]       )	->    List[Any]:

							'''simple docstring'''






							def find_duplicates(SCREAMING_SNAKE_CASE__      :  List[str]       ):
														return [k for k, v in collections.Counter(SCREAMING_SNAKE_CASE__       ).items() if v > 1]

							if list(import_dict_objects.keys()       ) != list(type_hint_objects.keys()       ):
														return ["Both sides of the init do not have the same backends!"]

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[]
							for key in import_dict_objects.keys():
														SCREAMING_SNAKE_CASE__       :      Tuple     =						find_duplicates(import_dict_objects[key]       )
														if duplicate_imports:
																					errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''       )
														SCREAMING_SNAKE_CASE__       :      int     =						find_duplicates(type_hint_objects[key]       )
														if duplicate_type_hints:
																					errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''       )

														if sorted(set(import_dict_objects[key]       )       ) != sorted(set(type_hint_objects[key]       )       ):
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"base imports" if key == "none" else f'''{key} backend'''
																					errors.append(f'''Differences for {name}:'''       )
																					for a in type_hint_objects[key]:
																												if a not in import_dict_objects[key]:
																																			errors.append(f'''  {a} in TYPE_HINT but not in _import_structure.'''       )
																					for a in import_dict_objects[key]:
																												if a not in type_hint_objects[key]:
																																			errors.append(f'''  {a} in _import_structure but not in TYPE_HINT.'''       )
							return errors




def _a (  )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						[]
							for root, _, files in os.walk(SCREAMING_SNAKE_CASE__       ):
														if "__init__.py" in files:
																					SCREAMING_SNAKE_CASE__       :      Any     =						os.path.join(SCREAMING_SNAKE_CASE__			,				"__init__.py"       )
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						parse_init(SCREAMING_SNAKE_CASE__       )
																					if objects is not None:
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						analyze_results(*SCREAMING_SNAKE_CASE__       )
																												if len(SCREAMING_SNAKE_CASE__       ) > 0:
																																			SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
																																			failures.append("\n".join(SCREAMING_SNAKE_CASE__       )       )
							if len(SCREAMING_SNAKE_CASE__       ) > 0:
														raise ValueError("\n\n".join(SCREAMING_SNAKE_CASE__       )       )




def _a (  )	->    List[str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[]
							for path, directories, files in os.walk(SCREAMING_SNAKE_CASE__       ):
														for folder in directories:
																					# Ignore private modules
																					if folder.startswith("_"       ):
																												directories.remove(SCREAMING_SNAKE_CASE__       )
																												continue
																					# Ignore leftovers from branches (empty folders apart from pycache)
																					if len(list((Path(SCREAMING_SNAKE_CASE__       ) / folder).glob("*.py"       )       )       ) == 0:
																												continue
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						str((Path(SCREAMING_SNAKE_CASE__       ) / folder).relative_to(SCREAMING_SNAKE_CASE__       )       )
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						short_path.replace(os.path.sep			,				"."       )
																					submodules.append(SCREAMING_SNAKE_CASE__       )
														for fname in files:
																					if fname == "__init__.py":
																												continue
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						str((Path(SCREAMING_SNAKE_CASE__       ) / fname).relative_to(SCREAMING_SNAKE_CASE__       )       )
																					SCREAMING_SNAKE_CASE__       :      int     =						short_path.replace(".py"			,				""       ).replace(os.path.sep			,				"."       )
																					if len(submodule.split("."       )       ) == 1:
																												submodules.append(SCREAMING_SNAKE_CASE__       )
							return submodules


_lowerCamelCase :    List[str]  							= [
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]




def _a (  )	->    Dict:

							'''simple docstring'''






							from transformers.utils import direct_transformers_import

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						direct_transformers_import(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						set(transformers._import_structure.keys()       )
							# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
							# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
							# (potentiall re-) add them.
							with open(os.path.join(SCREAMING_SNAKE_CASE__			,				"__init__.py"       )			,				"r"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f.read()
							import_structure_keys.update(set(re.findall(R"import_structure\[\"([^\"]*)\"\]"			,				SCREAMING_SNAKE_CASE__       )       )       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						[
							    module
							    for module in get_transformers_submodules()
							    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
							]

							if len(SCREAMING_SNAKE_CASE__       ) > 0:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"\n".join(f'''- {module}''' for module in module_not_registered       )
														raise ValueError(
														    "The following submodules are not properly registed in the main init of Transformers:\n"
														    f'''{list_of_modules}\n'''
														    "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."       )


if __name__ == "__main__":
		check_all_inits()
		check_submodules()













import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					List[Any]=1_3,			_UpperCAmelCase     :					Optional[Any]=3_0,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=3,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[Any]=3_2,			_UpperCAmelCase     :					Any=5,			_UpperCAmelCase     :					Optional[Any]=4,			_UpperCAmelCase     :					List[Any]=3_7,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					int=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					List[Any]=None,			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						parent
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						batch_size
														SCREAMING_SNAKE_CASE__       :      str     =						image_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_channels
														SCREAMING_SNAKE_CASE__       :      List[str]     =						is_training
														SCREAMING_SNAKE_CASE__       :      Any     =						use_labels
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      str     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Any     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Any     =						scope

														# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														SCREAMING_SNAKE_CASE__       :      str     =						(image_size // patch_size) ** 2
														SCREAMING_SNAKE_CASE__       :      str     =						num_patches + 1






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

														SCREAMING_SNAKE_CASE__       :      int     =						self.get_config()

														return config, pixel_values, labels






							def  A_     ( self     :					int						)				->			Tuple:





														"""simple docstring"""
														return ViTMSNConfig(
														    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,			initializer_range=self.initializer_range,			)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNModel(config=_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      int     =						model(_UpperCAmelCase,			labels=_UpperCAmelCase						)
														print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"						)
														print("Labels: {labels}"						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)

														# test greyscale images
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      Any     =						{"pixel_values": pixel_values}
														return config, inputs_dict


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







							"""simple docstring"""




							UpperCAmelCase_      						=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							UpperCAmelCase_      						=   (
							    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      str     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






							def  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





														"""simple docstring"""
														self.config_tester.run_common_tests()






							@unittest.skip(reason="ViTMSN does not use inputs_embeds"						)
							def  A_     ( self     :					List[str]						)				->			Tuple:





														"""simple docstring"""
														pass






							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model_class(_UpperCAmelCase						)
																					self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(_UpperCAmelCase,			nn.Linear						)						)






							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					SCREAMING_SNAKE_CASE__       :      str     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					Optional[int]						)				->			List[Any]:





														"""simple docstring"""
														for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViTMSNModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    Dict:

							'''simple docstring'''






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







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







							"""simple docstring"""




							@cached_property
							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"						) if is_vision_available() else None






							@slow
							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														torch.manual_seed(2						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"						).to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Dict     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Dict     =						model(**_UpperCAmelCase						)

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 1_0_0_0)						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor([-0.0803, -0.4454, -0.2375]						).to(_UpperCAmelCase						)

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






