




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  :		Union[str, Any]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		str=13    ,    _lowerCAmelCase  :		Tuple=30    ,    _lowerCAmelCase  :		List[Any]=2    ,    _lowerCAmelCase  :		Any=3    ,    _lowerCAmelCase  :		int=True    ,    _lowerCAmelCase  :		List[Any]=True    ,    _lowerCAmelCase  :		int=32    ,    _lowerCAmelCase  :		int=2    ,    _lowerCAmelCase  :		int=4    ,    _lowerCAmelCase  :		List[str]=37    ,    _lowerCAmelCase  :		Optional[Any]="gelu"    ,    _lowerCAmelCase  :		List[str]=0.1    ,    _lowerCAmelCase  :		Tuple=0.1    ,    _lowerCAmelCase  :		Tuple=10    ,    _lowerCAmelCase  :		Optional[Any]=0.02    ,    _lowerCAmelCase  :		List[str]=3    ,    _lowerCAmelCase  :		Optional[Any]=0.6    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    ):
							SCREAMING_SNAKE_CASE_     						=      parent
							SCREAMING_SNAKE_CASE_     						=      batch_size
							SCREAMING_SNAKE_CASE_     						=      image_size
							SCREAMING_SNAKE_CASE_     						=      patch_size
							SCREAMING_SNAKE_CASE_     						=      num_channels
							SCREAMING_SNAKE_CASE_     						=      is_training
							SCREAMING_SNAKE_CASE_     						=      use_labels
							SCREAMING_SNAKE_CASE_     						=      hidden_size
							SCREAMING_SNAKE_CASE_     						=      num_hidden_layers
							SCREAMING_SNAKE_CASE_     						=      num_attention_heads
							SCREAMING_SNAKE_CASE_     						=      intermediate_size
							SCREAMING_SNAKE_CASE_     						=      hidden_act
							SCREAMING_SNAKE_CASE_     						=      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_     						=      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_     						=      type_sequence_label_size
							SCREAMING_SNAKE_CASE_     						=      initializer_range
							SCREAMING_SNAKE_CASE_     						=      mask_ratio
							SCREAMING_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)
							SCREAMING_SNAKE_CASE_     						=      (image_size // patch_size) ** 2
							SCREAMING_SNAKE_CASE_     						=      int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)



	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

							SCREAMING_SNAKE_CASE_     						=      self.get_config()

							return config, pixel_values, labels



	def 				lowerCAmelCase_  (							self  :		Optional[int]						):
							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 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Tuple						):
							SCREAMING_SNAKE_CASE_     						=      TFViTMAEModel(config=_lowerCamelCase						)
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCamelCase    ,    training=_lowerCamelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)						)



	def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      TFViTMAEForPreTraining(_lowerCamelCase						)
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCamelCase    ,    training=_lowerCamelCase						)
							# expected sequence length = num_patches
							SCREAMING_SNAKE_CASE_     						=      (self.image_size // self.patch_size) ** 2
							SCREAMING_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
							SCREAMING_SNAKE_CASE_     						=      1
							SCREAMING_SNAKE_CASE_     						=      TFViTMAEForPreTraining(_lowerCamelCase						)

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




	def 				lowerCAmelCase_  (							self  :		int						):
							SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
							(SCREAMING_SNAKE_CASE_)     						=      config_and_inputs
							SCREAMING_SNAKE_CASE_     						=      {'''pixel_values''': pixel_values}
							return config, inputs_dict





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


	'''simple docstring'''







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

	lowercase_       =   False
	lowercase_       =   False
	lowercase_       =   False
	lowercase_       =   False



	def 				lowerCAmelCase_  (							self  :		Dict						):
							SCREAMING_SNAKE_CASE_     						=      TFViTMAEModelTester(self						)
							SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCamelCase    ,    has_text_modality=_lowerCamelCase    ,    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  :		Optional[Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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



	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCamelCase						)



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase						)



	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							np.random.seed(2						)

							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

													SCREAMING_SNAKE_CASE_     						=      model(_lowerCamelCase    ,    noise=_lowerCamelCase						)

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

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



	def 				lowerCAmelCase_  (							self  :		List[Any]						):
							np.random.seed(2						)

							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

							def prepare_numpy_arrays(_lowerCAmelCase  :		Optional[Any]						):
													SCREAMING_SNAKE_CASE_     						=      {}
													for k, v in inputs_dict.items():
																			if tf.is_tensor(_lowerCamelCase						):
																									SCREAMING_SNAKE_CASE_     						=      v.numpy()
																			else:
																									SCREAMING_SNAKE_CASE_     						=      np.array(_lowerCamelCase						)

													return inputs_np_dict

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

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

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



	def 				lowerCAmelCase_  (							self  :		List[str]    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Optional[Any]						):
							np.random.seed(2						)

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

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

							super().check_pt_tf_models(_lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase						)



	def 				lowerCAmelCase_  (							self  :		Dict						):
							np.random.seed(2						)

							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

							SCREAMING_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(_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						)
							}

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

							for main_layer_class in tf_main_layer_classes:
													SCREAMING_SNAKE_CASE_     						=      main_layer_class(_lowerCamelCase						)

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

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

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



	@slow
	def 				lowerCAmelCase_  (							self  :		str						):
							np.random.seed(2						)

							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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

																			SCREAMING_SNAKE_CASE_     						=      np.amax(np.abs(out_a - out_a						)						)
																			self.assertLessEqual(_lowerCamelCase    ,    1E-5						)



	def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							np.random.seed(2						)

							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

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




	@slow
	def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							SCREAMING_SNAKE_CASE_     						=      TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'						)
							self.assertIsNotNone(_lowerCamelCase						)


def  UpperCAmelCase_							(				) ->			List[str]:
						SCREAMING_SNAKE_CASE_     						=      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  :		int						):
							return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'						) if is_vision_available() else None




	@slow
	def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							np.random.seed(2						)

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

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

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

							# verify the logits
							SCREAMING_SNAKE_CASE_     						=      tf.convert_to_tensor([1, 196, 768]						)
							self.assertEqual(outputs.logits.shape    ,    _lowerCamelCase						)

							SCREAMING_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]    ,    _lowerCamelCase    ,    atol=1E-4						)





from abc import ABC, abstractmethod
from argparse import ArgumentParser





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @staticmethod
 @abstractmethod
 def 				lowerCAmelCase_  (							_lowerCAmelCase  :		ArgumentParser						):
       raise NotImplementedError()




 @abstractmethod
 def 				lowerCAmelCase_  (							self  :		Dict						):
       raise NotImplementedError()