

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 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Union[str, Any]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Optional[int]=1_3							,   _UpperCamelCase :				Union[str, Any]=3_0							,   _UpperCamelCase :				str=2							,   _UpperCamelCase :				Optional[int]=3							,   _UpperCamelCase :				Dict=True							,   _UpperCamelCase :				Dict=True							,   _UpperCamelCase :				List[Any]=3_2							,   _UpperCamelCase :				Optional[Any]=2							,   _UpperCamelCase :				List[Any]=4							,   _UpperCamelCase :				Optional[Any]=3_7							,   _UpperCamelCase :				Any="gelu"							,   _UpperCamelCase :				Union[str, Any]=0.1							,   _UpperCamelCase :				Optional[int]=0.1							,   _UpperCamelCase :				List[Any]=1_0							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=3							,   _UpperCamelCase :				Union[str, Any]=0.6							,   _UpperCamelCase :				str=None							,   )      ->Any:
									snake_case_   =	parent
									snake_case_   =	batch_size
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	is_training
									snake_case_   =	use_labels
									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	type_sequence_label_size
									snake_case_   =	initializer_range
									snake_case_   =	mask_ratio
									snake_case_   =	scope

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







			def 				snake_case__(     self :				List[str]							)      ->Optional[Any]:
									snake_case_   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

									snake_case_   =	None
									if self.use_labels:
															snake_case_   =	ids_tensor([self.batch_size]							,   self.type_sequence_label_size							)

									snake_case_   =	self.get_config()

									return config, pixel_values, labels







			def 				snake_case__(     self :				Optional[int]							)      ->str:
									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 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any							)      ->Union[str, Any]:
									snake_case_   =	TFViTMAEModel(config=_UpperCamelCase							)
									snake_case_   =	model(_UpperCamelCase							,   training=_UpperCamelCase							)
									self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)							)







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

									# test greyscale images
									snake_case_   =	1
									snake_case_   =	TFViTMAEForPreTraining(_UpperCamelCase							)

									snake_case_   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
									snake_case_   =	model(_UpperCamelCase							,   training=_UpperCamelCase							)
									snake_case_   =	self.patch_size**2
									self.parent.assertEqual(result.logits.shape							,   (self.batch_size, num_patches, expected_num_channels)							)







			def 				snake_case__(     self :				Union[str, Any]							)      ->Dict:
									snake_case_   =	self.prepare_config_and_inputs()
									((snake_case_),    (snake_case_),    (snake_case_))   =	config_and_inputs
									snake_case_   =	{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_tf
class 	snake_case_   (		__A	,       __A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   str									=				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
			SCREAMING_SNAKE_CASE     :   Tuple									=				{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				False
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				False
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				False
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				False







			def 				snake_case__(     self :				Dict							)      ->List[Any]:
									snake_case_   =	TFViTMAEModelTester(self							)
									snake_case_   =	ConfigTester(self							,   config_class=_UpperCamelCase							,   has_text_modality=_UpperCamelCase							,   hidden_size=3_7							)







			def 				snake_case__(     self :				Dict							)      ->int:
									self.config_tester.run_common_tests()







			@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''							)
			def 				snake_case__(     self :				Optional[int]							)      ->int:
									pass







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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







			def 				snake_case__(     self :				Tuple							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case_   =	['''pixel_values''']
															self.assertListEqual(arg_names[:1]							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Dict							)      ->int:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							)      ->Dict:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_UpperCamelCase							)







			def 				snake_case__(     self :				Any							)      ->Optional[Any]:
									# make the mask reproducible
									np.random.seed(2							)

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
															snake_case_   =	model_class(_UpperCamelCase							)
															snake_case_   =	self._prepare_for_class(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   noise=_UpperCamelCase							)

															snake_case_   =	copy.deepcopy(self._prepare_for_class(_UpperCamelCase							,   _UpperCamelCase							)							)
															snake_case_   =	model(**_UpperCamelCase							,   noise=_UpperCamelCase							)
															snake_case_   =	outputs_dict[0].numpy()
															snake_case_   =	outputs_keywords[0].numpy()

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







			def 				snake_case__(     self :				Optional[int]							)      ->Optional[Any]:
									# make the mask reproducible
									np.random.seed(2							)

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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

									def prepare_numpy_arrays(_UpperCamelCase :				str							):
															snake_case_   =	{}
															for k, v in inputs_dict.items():
																					if tf.is_tensor(_UpperCamelCase							):
																											snake_case_   =	v.numpy()
																					else:
																											snake_case_   =	np.array(_UpperCamelCase							)

															return inputs_np_dict

									for model_class in self.all_model_classes:
															snake_case_   =	model_class(_UpperCamelCase							)

															snake_case_   =	self._prepare_for_class(_UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	prepare_numpy_arrays(_UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   noise=_UpperCamelCase							)
															snake_case_   =	model(**_UpperCamelCase							,   noise=_UpperCamelCase							)
															self.assert_outputs_same(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Dict							)      ->Dict:
									# make masks reproducible
									np.random.seed(2							)

									snake_case_   =	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
									snake_case_   =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
									snake_case_   =	tf.constant(_UpperCamelCase							)

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

									super().check_pt_tf_models(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Optional[int]							)      ->str:
									# make mask reproducible
									np.random.seed(2							)

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

									snake_case_   =	{
									    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							)
									}

									snake_case_   =	int((config.image_size // config.patch_size) ** 2							)
									snake_case_   =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
									snake_case_   =	tf.convert_to_tensor(_UpperCamelCase							)
									inputs_dict.update({'''noise''': noise}							)

									for main_layer_class in tf_main_layer_classes:
															snake_case_   =	main_layer_class(_UpperCamelCase							)

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

															snake_case_   =	tf.keras.Model(_UpperCamelCase							,   outputs=main_layer(_UpperCamelCase							)							)
															snake_case_   =	model(_UpperCamelCase							)

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







			@slow
			def 				snake_case__(     self :				List[Any]							)      ->str:
									# make mask reproducible
									np.random.seed(2							)

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
															snake_case_   =	model_class(_UpperCamelCase							)
															snake_case_   =	self._prepare_for_class(_UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	model(_UpperCamelCase							,   noise=_UpperCamelCase							)

															if model_class.__name__ == "TFViTMAEModel":
																					snake_case_   =	outputs.last_hidden_state.numpy()
																					snake_case_   =	0
															else:
																					snake_case_   =	outputs.logits.numpy()
																					snake_case_   =	0

															with tempfile.TemporaryDirectory() as tmpdirname:
																					model.save_pretrained(_UpperCamelCase							,   saved_model=_UpperCamelCase							)
																					snake_case_   =	model_class.from_pretrained(_UpperCamelCase							)
																					snake_case_   =	model(_UpperCamelCase							,   noise=_UpperCamelCase							)

																					if model_class.__name__ == "TFViTMAEModel":
																											snake_case_   =	after_outputs['''last_hidden_state'''].numpy()
																											snake_case_   =	0
																					else:
																											snake_case_   =	after_outputs['''logits'''].numpy()
																											snake_case_   =	0

																					snake_case_   =	np.amax(np.abs(out_a - out_a							)							)
																					self.assertLessEqual(_UpperCamelCase							,   1e-5							)







			def 				snake_case__(     self :				int							)      ->List[str]:
									# make mask reproducible
									np.random.seed(2							)

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
															snake_case_   =	model_class(_UpperCamelCase							)
															snake_case_   =	self._prepare_for_class(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   noise=_UpperCamelCase							)
															snake_case_   =	model.get_config()
															# make sure that returned config is jsonifiable, which is required by keras
															json.dumps(_UpperCamelCase							)
															snake_case_   =	model_class.from_config(model.get_config()							)
															# make sure it also accepts a normal config
															snake_case_   =	model_class.from_config(model.config							)
															snake_case_   =	new_model(_UpperCamelCase							)  # Build model
															new_model.set_weights(model.get_weights()							)
															snake_case_   =	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 				snake_case__(     self :				Any							)      ->List[Any]:
									pass







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







			@slow
			def 				snake_case__(     self :				Any							)      ->List[str]:
									snake_case_   =	TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''							)
									self.assertIsNotNone(_UpperCamelCase							)






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





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







			'''simple docstring'''

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







			@slow
			def 				snake_case__(     self :				Dict							)      ->str:
									# make random mask reproducible across the PT and TF model
									np.random.seed(2							)

									snake_case_   =	TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''							)

									snake_case_   =	self.default_image_processor
									snake_case_   =	prepare_img()
									snake_case_   =	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)
									snake_case_   =	ViTMAEConfig()
									snake_case_   =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
									snake_case_   =	np.random.uniform(size=(1, num_patches)							)

									# forward pass
									snake_case_   =	model(**_UpperCamelCase							,   noise=_UpperCamelCase							)

									# verify the logits
									snake_case_   =	tf.convert_to_tensor([1, 1_9_6, 7_6_8]							)
									self.assertEqual(outputs.logits.shape							,   _UpperCamelCase							)

									snake_case_   =	tf.convert_to_tensor(
									    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)