



"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      ['''EncoderDecoderModel''']

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						str    							=      ['''TFEncoderDecoderModel''']

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      ['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
			from .configuration_encoder_decoder import EncoderDecoderConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_encoder_decoder import EncoderDecoderModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""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				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=3_0				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=4				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=1_0				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=3				,       lowerCamelCase__=0.6				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				patch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				mask_ratio
									_lowerCamelCase        =				scope

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

									_lowerCamelCase        =				self.get_config()

									return config, pixel_values, labels

					def 			snake_case__      (    self   ):
									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=lowerCamelCase__				,       initializer_range=self.initializer_range				,       mask_ratio=self.mask_ratio				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				TFViTMAEModel(config=lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       training=lowerCamelCase__   )
									self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				TFViTMAEForPreTraining(lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       training=lowerCamelCase__   )
									# expected sequence length = num_patches
									_lowerCamelCase        =				(self.image_size // self.patch_size) ** 2
									_lowerCamelCase        =				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        =				1
									_lowerCamelCase        =				TFViTMAEForPreTraining(lowerCamelCase__   )

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





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


@require_tf
class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[Any]										= (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
					lowercase__   :       Dict										= {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

					lowercase__   :       Optional[Any]										= False
					lowercase__   :       Union[str, Any]										= False
					lowercase__   :       str										= False
					lowercase__   :       List[str]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFViTMAEModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__				,       has_text_modality=lowerCamelCase__				,       hidden_size=3_7   )

					def 			snake_case__      (    self   ):
									self.config_tester.run_common_tests()

					@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''   )
					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

													_lowerCamelCase        =				['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*lowerCamelCase__   )

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )

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

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

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

													return inputs_np_dict

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )

													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
													_lowerCamelCase        =				prepare_numpy_arrays(lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )
													_lowerCamelCase        =				model(**lowerCamelCase__				,       noise=lowerCamelCase__   )
													self.assert_outputs_same(lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									# make masks reproducible
									np.random.seed(2   )

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

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

									super().check_pt_tf_models(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				{
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__   ),)
									    for module_member_name in dir(lowerCamelCase__   )
									    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(lowerCamelCase__				,       lowerCamelCase__   ),)
									    if isinstance(lowerCamelCase__				,       lowerCamelCase__   )
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(lowerCamelCase__				,       '''_keras_serializable'''				,       lowerCamelCase__   )
									}

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

									for main_layer_class in tf_main_layer_classes:
													_lowerCamelCase        =				main_layer_class(lowerCamelCase__   )

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

													_lowerCamelCase        =				tf.keras.Model(lowerCamelCase__				,       outputs=main_layer(lowerCamelCase__   )   )
													_lowerCamelCase        =				model(lowerCamelCase__   )

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

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )

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

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

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

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

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

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )
													_lowerCamelCase        =				model.get_config()
													# make sure that returned config is jsonifiable, which is required by keras
													json.dumps(lowerCamelCase__   )
													_lowerCamelCase        =				model_class.from_config(model.get_config()   )
													# make sure it also accepts a normal config
													_lowerCamelCase        =				model_class.from_config(model.config   )
													_lowerCamelCase        =				new_model(lowerCamelCase__   )  # Build model
													new_model.set_weights(model.get_weights()   )
													_lowerCamelCase        =				new_model(lowerCamelCase__				,       noise=lowerCamelCase__   )

													self.assert_outputs_same(lowerCamelCase__				,       lowerCamelCase__   )

					@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   ):
									pass

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





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''   )
									self.assertIsNotNone(lowerCamelCase__   )



def        lowerCAmelCase_(   )	->       List[Any]:
				_lowerCamelCase        =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




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





					'''simple docstring'''



					@cached_property
					def 			snake_case__      (    self   ):
									return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''   ) if is_vision_available() else None





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

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

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

									# forward pass
									_lowerCamelCase        =				model(**lowerCamelCase__				,       noise=lowerCamelCase__   )

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

									_lowerCamelCase        =				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]				,       lowerCamelCase__				,       atol=1e-4   )
