



"""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 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							Optional[int]						,     snake_case:							Optional[int]=13						,     snake_case:							str=30						,     snake_case:							Dict=2						,     snake_case:							Tuple=3						,     snake_case:							Optional[Any]=True						,     snake_case:							Optional[Any]=True						,     snake_case:							str=32						,     snake_case:							List[str]=2						,     snake_case:							Union[str, Any]=4						,     snake_case:							Union[str, Any]=37						,     snake_case:							Union[str, Any]="gelu"						,     snake_case:							Optional[int]=0.1						,     snake_case:							str=0.1						,     snake_case:							Dict=10						,     snake_case:							Union[str, Any]=0.0_2						,     snake_case:							Union[str, Any]=3						,     snake_case:							int=0.6						,     snake_case:							List[Any]=None						,     )   ->	List[str]:
											snake_case_ :Optional[int]						=			parent
											snake_case_ :Dict						=			batch_size
											snake_case_ :Union[str, Any]						=			image_size
											snake_case_ :Tuple						=			patch_size
											snake_case_ :Union[str, Any]						=			num_channels
											snake_case_ :Optional[Any]						=			is_training
											snake_case_ :Optional[Any]						=			use_labels
											snake_case_ :List[str]						=			hidden_size
											snake_case_ :Tuple						=			num_hidden_layers
											snake_case_ :Optional[int]						=			num_attention_heads
											snake_case_ :Optional[int]						=			intermediate_size
											snake_case_ :str						=			hidden_act
											snake_case_ :Dict						=			hidden_dropout_prob
											snake_case_ :Union[str, Any]						=			attention_probs_dropout_prob
											snake_case_ :Any						=			type_sequence_label_size
											snake_case_ :Any						=			initializer_range
											snake_case_ :Any						=			mask_ratio
											snake_case_ :int						=			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_ :Optional[Any]						=			(image_size // patch_size) ** 2
											snake_case_ :Optional[int]						=			int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)






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

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

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

											return config, pixel_values, labels






					def    lowerCAmelCase_				(       self:							str						)   ->	Tuple:
											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=snake_case						,     initializer_range=self.initializer_range						,     mask_ratio=self.mask_ratio						,     )






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






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Union[str, Any]						,     snake_case:							Optional[int]						,     snake_case:							Optional[int]						)   ->	List[Any]:
											snake_case_ :str						=			TFViTMAEForPreTraining(snake_case						)
											snake_case_ :Union[str, Any]						=			model(snake_case						,     training=snake_case						)
											# expected sequence length = num_patches
											snake_case_ :Union[str, Any]						=			(self.image_size // self.patch_size) ** 2
											snake_case_ :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
											snake_case_ :List[Any]						=			1
											snake_case_ :List[str]						=			TFViTMAEForPreTraining(snake_case						)

											snake_case_ :int						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											snake_case_ :Union[str, Any]						=			model(snake_case						,     training=snake_case						)
											snake_case_ :str						=			self.patch_size**2
											self.parent.assertEqual(result.logits.shape						,     (self.batch_size, num_patches, expected_num_channels)						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			self.prepare_config_and_inputs()
											((snake_case_),       (snake_case_),       (snake_case_)) :List[str]						=			config_and_inputs
											snake_case_ :List[str]						=			{"""pixel_values""": pixel_values}
											return config, inputs_dict

@require_tf
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			int						=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
					_A		:			Optional[int]						=     {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

					_A		:			int						=     False
					_A		:			Any						=     False
					_A		:			List[str]						=     False
					_A		:			Optional[int]						=     False






					def    lowerCAmelCase_				(       self:							str						)   ->	Tuple:
											snake_case_ :str						=			TFViTMAEModelTester(self						)
											snake_case_ :str						=			ConfigTester(self						,     config_class=snake_case						,     has_text_modality=snake_case						,     hidden_size=37						)






					def    lowerCAmelCase_				(       self:							Any						)   ->	str:
											self.config_tester.run_common_tests()






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






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

											for model_class in self.all_model_classes:
																	snake_case_ :Optional[int]						=			model_class(snake_case						)
																	self.assertIsInstance(model.get_input_embeddings()						,     (tf.keras.layers.Layer)						)
																	snake_case_ :Dict						=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case						,     tf.keras.layers.Layer						)						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	str:
											snake_case_,       snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	snake_case_ :Any						=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Tuple:
											snake_case_ :List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[Any]:
											snake_case_ :Dict						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*snake_case						)






					def    lowerCAmelCase_				(       self:							int						)   ->	Any:
											# make the mask reproducible
											np.random.seed(2						)

											snake_case_,       snake_case_ :Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

																	snake_case_ :Union[str, Any]						=			model(snake_case						,     noise=snake_case						)

																	snake_case_ :Union[str, Any]						=			copy.deepcopy(self._prepare_for_class(snake_case						,     snake_case						)						)
																	snake_case_ :int						=			model(**snake_case						,     noise=snake_case						)
																	snake_case_ :Any						=			outputs_dict[0].numpy()
																	snake_case_ :Tuple						=			outputs_keywords[0].numpy()

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






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Union[str, Any]:
											# make the mask reproducible
											np.random.seed(2						)

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

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

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

																	return inputs_np_dict

											for model_class in self.all_model_classes:
																	snake_case_ :str						=			model_class(snake_case						)

																	snake_case_ :Dict						=			self._prepare_for_class(snake_case						,     snake_case						)
																	snake_case_ :Any						=			prepare_numpy_arrays(snake_case						)

																	snake_case_ :Any						=			model(snake_case						,     noise=snake_case						)
																	snake_case_ :List[str]						=			model(**snake_case						,     noise=snake_case						)
																	self.assert_outputs_same(snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Optional[int]						,     snake_case:							Optional[Any]						,     snake_case:							Any						)   ->	Union[str, Any]:
											# make masks reproducible
											np.random.seed(2						)

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

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

											super().check_pt_tf_models(snake_case						,     snake_case						,     snake_case						)






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

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

											snake_case_ :Tuple						=			{
											    module_member
											    for model_class in self.all_model_classes
											    for module in (import_module(model_class.__module__						),)
											    for module_member_name in dir(snake_case						)
											    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(snake_case						,     snake_case						),)
											    if isinstance(snake_case						,     snake_case						)
											    and tf.keras.layers.Layer in module_member.__bases__
											    and getattr(snake_case						,     """_keras_serializable"""						,     snake_case						)
											}

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

											for main_layer_class in tf_main_layer_classes:
																	snake_case_ :List[str]						=			main_layer_class(snake_case						)

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

																	snake_case_ :int						=			tf.keras.Model(snake_case						,     outputs=main_layer(snake_case						)						)
																	snake_case_ :int						=			model(snake_case						)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							snake_case_ :List[Any]						=			os.path.join(snake_case						,     """keras_model.h5"""						)
																							model.save(snake_case						)
																							snake_case_ :List[str]						=			tf.keras.models.load_model(
																							    snake_case						,     custom_objects={main_layer_class.__name__: main_layer_class}						)
																							assert isinstance(snake_case						,     tf.keras.Model						)
																							snake_case_ :int						=			model(snake_case						)
																							self.assert_outputs_same(snake_case						,     snake_case						)






					@slow
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Tuple:
											# make mask reproducible
											np.random.seed(2						)

											snake_case_,       snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs_for_common()

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

											for model_class in self.all_model_classes:
																	snake_case_ :Optional[int]						=			model_class(snake_case						)
																	snake_case_ :Any						=			self._prepare_for_class(snake_case						,     snake_case						)
																	snake_case_ :Tuple						=			model(snake_case						,     noise=snake_case						)

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

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

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

																							snake_case_ :Any						=			np.amax(np.abs(out_a - out_a						)						)
																							self.assertLessEqual(snake_case						,     1E-5						)






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

											snake_case_,       snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																	self.assert_outputs_same(snake_case						,     snake_case						)






					@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:							Tuple						)   ->	str:
											pass






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






					@slow
					def    lowerCAmelCase_				(       self:							int						)   ->	Union[str, Any]:
											snake_case_ :Optional[Any]						=			TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""						)
											self.assertIsNotNone(snake_case						)

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
						return image

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






					'''simple docstring'''





					@cached_property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[str]:
											return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""						) if is_vision_available() else None






					@slow
					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Dict:
											# make random mask reproducible across the PT and TF model
											np.random.seed(2						)

											snake_case_ :Any						=			TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""						)

											snake_case_ :str						=			self.default_image_processor
											snake_case_ :Optional[int]						=			prepare_img()
											snake_case_ :int						=			image_processor(images=snake_case						,     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_ :Optional[Any]						=			ViTMAEConfig()
											snake_case_ :List[str]						=			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
											snake_case_ :Union[str, Any]						=			np.random.uniform(size=(1, num_patches)						)

											# forward pass
											snake_case_ :Tuple						=			model(**snake_case						,     noise=snake_case						)

											# verify the logits
											snake_case_ :int						=			tf.convert_to_tensor([1, 196, 768]						)
											self.assertEqual(outputs.logits.shape						,     snake_case						)

											snake_case_ :Union[str, Any]						=			tf.convert_to_tensor(
											    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]						)

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






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   {'configuration_mmbt': ['MMBTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      List[Any]  =   ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
       from .configuration_mmbt import MMBTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
       import sys

       SCREAMING_SNAKE_CASE__       :      int  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






