

import collections
import os
import re
from pathlib import Path


a_     =			'src/transformers'


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

def        lowerCamelCase__       (    _a):
	if _re_test_backend.search(_a) is None:
		return None
	SCREAMING_SNAKE_CASE      :					int    =				[b[0] for b in _re_backend.findall(_a)]
	backends.sort()
	return "_and_".join(_a)

def        lowerCamelCase__       (    _a):
	with open(_a							,	"r"							,	encoding="utf-8"							,	newline="\n") as f:
		SCREAMING_SNAKE_CASE      :					List[str]    =				f.readlines()

	SCREAMING_SNAKE_CASE      :					Any    =				0
	while line_index < len(_a) and not lines[line_index].startswith("_import_structure = {"):
		line_index += 1

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

	# First grab the objects without a specific backend in _import_structure
	SCREAMING_SNAKE_CASE      :					Dict    =				[]
	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(_a):
			SCREAMING_SNAKE_CASE      :					List[Any]    =				_re_one_line_import_struct.search(_a).groups()[0]
			SCREAMING_SNAKE_CASE      :					Any    =				re.findall(r"\[([^\]]+)\]"							,	_a)
			for imp in imports:
				objects.extend([obj[1:-1] for obj in imp.split(", ")])
			line_index += 1
			continue
		SCREAMING_SNAKE_CASE      :					Tuple    =				_re_import_struct_key_value.search(_a)
		if single_line_import_search is not None:
			SCREAMING_SNAKE_CASE      :					int    =				[obj[1:-1] for obj in single_line_import_search.groups()[0].split(", ") if len(_a) > 0]
			objects.extend(_a)
		elif line.startswith(" " * 8 + "\""):
			objects.append(line[9:-3])
		line_index += 1

	SCREAMING_SNAKE_CASE      :					Any    =				{"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      :					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      :					str    =				[]
			# 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(_a) is not None:
					objects.append(_re_import_struct_add_one.search(_a).groups()[0])
				elif _re_import_struct_add_many.search(_a) is not None:
					SCREAMING_SNAKE_CASE      :					Any    =				_re_import_struct_add_many.search(_a).groups()[0].split(", ")
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				[obj[1:-1] for obj in imports if len(_a) > 0]
					objects.extend(_a)
				elif _re_between_brackets.search(_a) is not None:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				_re_between_brackets.search(_a).groups()[0].split(", ")
					SCREAMING_SNAKE_CASE      :					int    =				[obj[1:-1] for obj in imports if len(_a) > 0]
					objects.extend(_a)
				elif _re_quote_object.search(_a) is not None:
					objects.append(_re_quote_object.search(_a).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      :					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      :					Any    =				[]
	while (
	    line_index < len(_a)
	    and find_backend(lines[line_index]) is None
	    and not lines[line_index].startswith("else")
	):
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				lines[line_index]
		SCREAMING_SNAKE_CASE      :					int    =				_re_import.search(_a)
		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      :					str    =				{"none": objects}
	# Let's continue with backend-specific objects
	while line_index < len(_a):
		# If the line is an if is_backend_available, we grab all objects associated.
		SCREAMING_SNAKE_CASE      :					int    =				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      :					Union[str, Any]    =				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      :					Optional[Any]    =				lines[line_index]
				SCREAMING_SNAKE_CASE      :					Dict    =				_re_import.search(_a)
				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      :					List[Any]    =				objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects

def        lowerCamelCase__       (    _a							,	_a):

	def find_duplicates(_a):
		return [k for k, v in collections.Counter(_a).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      :					Dict    =				[]
	for key in import_dict_objects.keys():
		SCREAMING_SNAKE_CASE      :					List[Any]    =				find_duplicates(import_dict_objects[key])
		if duplicate_imports:
			errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}")
		SCREAMING_SNAKE_CASE      :					Dict    =				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      :					Dict    =				"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        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				[]
	for root, _, files in os.walk(_a):
		if "__init__.py" in files:
			SCREAMING_SNAKE_CASE      :					List[Any]    =				os.path.join(_a							,	"__init__.py")
			SCREAMING_SNAKE_CASE      :					List[str]    =				parse_init(_a)
			if objects is not None:
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				analyze_results(*_a)
				if len(_a) > 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(_a))
	if len(_a) > 0:
		raise ValueError("\n\n".join(_a))

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


a_     =			[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]

def        lowerCamelCase__       (    ):
	# This is to make sure the transformers module imported is the one in the repo.
	from transformers.utils import direct_transformers_import

	SCREAMING_SNAKE_CASE      :					str    =				direct_transformers_import(_a)

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				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(_a							,	"__init__.py")							,	"r") as f:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				f.read()
	import_structure_keys.update(set(re.findall(r"import_structure\[\"([^\"]*)\"\]"							,	_a)))

	SCREAMING_SNAKE_CASE      :					str    =				[
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
	]

	if len(_a) > 0:
		SCREAMING_SNAKE_CASE      :					int    =				"\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()







'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class _lowercase	:




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Dict=13			,							UpperCamelCase__:				int=30			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				Tuple=3			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				Dict=True			,							UpperCamelCase__:				Optional[int]=32			,							UpperCamelCase__:				Tuple=2			,							UpperCamelCase__:				Tuple=4			,							UpperCamelCase__:				Optional[Any]=37			,							UpperCamelCase__:				List[Any]="gelu"			,							UpperCamelCase__:				Union[str, Any]=0.1			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				Tuple=10			,							UpperCamelCase__:				Optional[Any]=0.02			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				str=0.6			,							UpperCamelCase__:				str=None			,							):
														lowerCamelCase__					:	List[Any]         =       parent
														lowerCamelCase__					:	Optional[Any]         =       batch_size
														lowerCamelCase__					:	Union[str, Any]         =       image_size
														lowerCamelCase__					:	Any         =       patch_size
														lowerCamelCase__					:	Union[str, Any]         =       num_channels
														lowerCamelCase__					:	Optional[Any]         =       is_training
														lowerCamelCase__					:	int         =       use_labels
														lowerCamelCase__					:	List[str]         =       hidden_size
														lowerCamelCase__					:	Optional[Any]         =       num_hidden_layers
														lowerCamelCase__					:	Any         =       num_attention_heads
														lowerCamelCase__					:	Optional[Any]         =       intermediate_size
														lowerCamelCase__					:	Optional[Any]         =       hidden_act
														lowerCamelCase__					:	Any         =       hidden_dropout_prob
														lowerCamelCase__					:	Tuple         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Dict         =       type_sequence_label_size
														lowerCamelCase__					:	Optional[int]         =       initializer_range
														lowerCamelCase__					:	List[str]         =       mask_ratio
														lowerCamelCase__					:	Optional[int]         =       scope

														# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
														# (we add 1 for the [CLS] token)
														lowerCamelCase__					:	Any         =       (image_size // patch_size) ** 2
														lowerCamelCase__					:	str         =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)     )     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	str         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

														lowerCamelCase__					:	Optional[Any]         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Any     ):
														return ViTMAEConfig(
														    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			,							decoder_hidden_size=self.hidden_size			,							decoder_num_hidden_layers=self.num_hidden_layers			,							decoder_num_attention_heads=self.num_attention_heads			,							decoder_intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							is_decoder=UpperCamelCase__			,							initializer_range=self.initializer_range			,							mask_ratio=self.mask_ratio			,							)




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[str]     ):
														lowerCamelCase__					:	Tuple         =       TFViTMAEModel(config=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       TFViTMAEForPreTraining(UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														# expected sequence length = num_patches
														lowerCamelCase__					:	List[str]         =       (self.image_size // self.patch_size) ** 2
														lowerCamelCase__					:	Union[str, Any]         =       self.patch_size**2 * self.num_channels
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, num_patches, expected_num_channels)     )

														# test greyscale images
														lowerCamelCase__					:	List[Any]         =       1
														lowerCamelCase__					:	Union[str, Any]         =       TFViTMAEForPreTraining(UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	Optional[int]         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       self.patch_size**2
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, num_patches, expected_num_channels)     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.prepare_config_and_inputs()
														((lowerCamelCase__)  ,		(lowerCamelCase__)  ,		(lowerCamelCase__))					:	Optional[int]         =       config_and_inputs
														lowerCamelCase__					:	List[Any]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							a				      =       {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	int         =       TFViTMAEModelTester(self     )
														lowerCamelCase__					:	List[Any]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(tf.keras.layers.Layer)     )
																					lowerCamelCase__					:	Dict         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__			,							tf.keras.layers.Layer     )     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       inspect.signature(model.call     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	str         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Tuple         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														# make the mask reproducible
														np.random.seed(2     )

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

														lowerCamelCase__					:	Union[str, Any]         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Optional[Any]         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )

																					lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																					lowerCamelCase__					:	int         =       copy.deepcopy(self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )
																					lowerCamelCase__					:	List[str]         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       outputs_dict[0].numpy()
																					lowerCamelCase__					:	Optional[int]         =       outputs_keywords[0].numpy()

																					self.assertLess(np.sum(np.abs(output_dict - output_keywords     )     )			,							1e-6     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														# make the mask reproducible
														np.random.seed(2     )

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

														lowerCamelCase__					:	Tuple         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Optional[Any]         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														def prepare_numpy_arrays(UpperCamelCase__:				int     ):
																					lowerCamelCase__					:	Optional[int]         =       {}
																					for k, v in inputs_dict.items():
																												if tf.is_tensor(UpperCamelCase__     ):
																																			lowerCamelCase__					:	List[str]         =       v.numpy()
																												else:
																																			lowerCamelCase__					:	Union[str, Any]         =       np.array(UpperCamelCase__     )

																					return inputs_np_dict

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )

																					lowerCamelCase__					:	List[Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       prepare_numpy_arrays(UpperCamelCase__     )

																					lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )
																					self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				str     ):
														# make masks reproducible
														np.random.seed(2     )

														lowerCamelCase__					:	List[str]         =       int((tf_model.config.image_size // tf_model.config.patch_size) ** 2     )
														lowerCamelCase__					:	int         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
														lowerCamelCase__					:	Optional[int]         =       tf.constant(UpperCamelCase__     )

														# Add `noise` argument.
														# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
														lowerCamelCase__					:	Tuple         =       tf_noise

														super().check_pt_tf_models(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														# make mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	List[Any]         =       {
														    module_member
														    for model_class in self.all_model_classes
														    for module in (import_module(model_class.__module__     ),)
														    for module_member_name in dir(UpperCamelCase__     )
														    if module_member_name.endswith("""MainLayer"""     )
														    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
														    and module_member_name[: -len("""MainLayer"""     )] == model_class.__name__[: -len("""Model"""     )]
														    for module_member in (getattr(UpperCamelCase__			,							UpperCamelCase__     ),)
														    if isinstance(UpperCamelCase__			,							UpperCamelCase__     )
														    and tf.keras.layers.Layer in module_member.__bases__
														    and getattr(UpperCamelCase__			,							"""_keras_serializable"""			,							UpperCamelCase__     )
														}

														lowerCamelCase__					:	List[str]         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Dict         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
														lowerCamelCase__					:	List[str]         =       tf.convert_to_tensor(UpperCamelCase__     )
														inputs_dict.update({"""noise""": noise}     )

														for main_layer_class in tf_main_layer_classes:
																					lowerCamelCase__					:	List[str]         =       main_layer_class(UpperCamelCase__     )

																					lowerCamelCase__					:	int         =       {
																					    name: tf.keras.Input(tensor.shape[1:]			,							dtype=tensor.dtype     ) for name, tensor in inputs_dict.items()
																					}

																					lowerCamelCase__					:	List[str]         =       tf.keras.Model(UpperCamelCase__			,							outputs=main_layer(UpperCamelCase__     )     )
																					lowerCamelCase__					:	Union[str, Any]         =       model(UpperCamelCase__     )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												lowerCamelCase__					:	int         =       os.path.join(UpperCamelCase__			,							"""keras_model.h5"""     )
																												model.save(UpperCamelCase__     )
																												lowerCamelCase__					:	int         =       tf.keras.models.load_model(
																												    UpperCamelCase__			,							custom_objects={main_layer_class.__name__: main_layer_class}     )
																												assert isinstance(UpperCamelCase__			,							tf.keras.Model     )
																												lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__     )
																												self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				str     ):
														# make mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	Tuple         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	str         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Tuple         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																					if model_class.__name__ == "TFViTMAEModel":
																												lowerCamelCase__					:	Any         =       outputs.last_hidden_state.numpy()
																												lowerCamelCase__					:	List[str]         =       0
																					else:
																												lowerCamelCase__					:	int         =       outputs.logits.numpy()
																												lowerCamelCase__					:	Dict         =       0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(UpperCamelCase__			,							saved_model=UpperCamelCase__     )
																												lowerCamelCase__					:	Dict         =       model_class.from_pretrained(UpperCamelCase__     )
																												lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																												if model_class.__name__ == "TFViTMAEModel":
																																			lowerCamelCase__					:	str         =       after_outputs["""last_hidden_state"""].numpy()
																																			lowerCamelCase__					:	Optional[Any]         =       0
																												else:
																																			lowerCamelCase__					:	Union[str, Any]         =       after_outputs["""logits"""].numpy()
																																			lowerCamelCase__					:	List[Any]         =       0

																												lowerCamelCase__					:	int         =       np.amax(np.abs(out_a - out_a     )     )
																												self.assertLessEqual(UpperCamelCase__			,							1e-5     )




							def 		lowerCamelCase_				(							self:				Any     ):
														# make mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	Optional[int]         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Union[str, Any]         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )

																					lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       model.get_config()
																					# make sure that returned config is jsonifiable, which is required by keras
																					json.dumps(UpperCamelCase__     )
																					lowerCamelCase__					:	int         =       model_class.from_config(model.get_config()     )
																					# make sure it also accepts a normal config
																					lowerCamelCase__					:	int         =       model_class.from_config(model.config     )
																					lowerCamelCase__					:	List[Any]         =       new_model(UpperCamelCase__     )  # Build model
																					new_model.set_weights(model.get_weights()     )
																					lowerCamelCase__					:	List[Any]         =       new_model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																					self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__     )




							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""     )
							def 		lowerCamelCase_				(							self:				List[str]     ):
														pass




							@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Tuple         =       TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""     )
														self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Any         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_tf
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""     ) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														# make random mask reproducible across the PT and TF model
														np.random.seed(2     )

														lowerCamelCase__					:	Optional[int]         =       TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""     )

														lowerCamelCase__					:	Dict         =       self.default_image_processor
														lowerCamelCase__					:	int         =       prepare_img()
														lowerCamelCase__					:	List[Any]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""tf"""     )

														# prepare a noise vector that will be also used for testing the TF model
														# (this way we can ensure that the PT and TF models operate on the same inputs)
														lowerCamelCase__					:	Tuple         =       ViTMAEConfig()
														lowerCamelCase__					:	Dict         =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
														lowerCamelCase__					:	str         =       np.random.uniform(size=(1, num_patches)     )

														# forward pass
														lowerCamelCase__					:	str         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	Any         =       tf.convert_to_tensor([1, 196, 768]     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	str         =       tf.convert_to_tensor(
														    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]     )

														tf.debugging.assert_near(outputs.logits[0, :3, :3]			,							UpperCamelCase__			,							atol=1e-4     )

