
"""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    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=30    ,		__a=2    ,		__a=3    ,		__a=True    ,		__a=True    ,		__a=32    ,		__a=2    ,		__a=4    ,		__a=37    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=10    ,		__a=0.02    ,		__a=3    ,		__a=0.6    ,		__a=None    ,		)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  is_training
							_UpperCamelCase										=  use_labels
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  type_sequence_label_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  mask_ratio
							_UpperCamelCase										=  scope

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



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

							_UpperCamelCase										=  self.get_config()

							return config, pixel_values, labels



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFViTMAEModel(config=snake_case__)
							_UpperCamelCase										=  model(snake_case__    ,		training=snake_case__)
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  TFViTMAEForPreTraining(snake_case__)
							_UpperCamelCase										=  model(snake_case__    ,		training=snake_case__)
							# expected sequence length = num_patches
							_UpperCamelCase										=  (self.image_size // self.patch_size) ** 2
							_UpperCamelCase										=  self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, num_patches, expected_num_channels))

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

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




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							(_UpperCamelCase)										=  config_and_inputs
							_UpperCamelCase										=  {'pixel_values': pixel_values}
							return config, inputs_dict






@require_tf
class    _UpperCAmelCase(   SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					unittest.TestCase     ):
		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 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  TFViTMAEModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=snake_case__    ,		has_text_modality=snake_case__    ,		hidden_size=37)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViTMAE does not use inputs_embeds''')
		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

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



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*snake_case__)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							np.random.seed(2)

							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

												_UpperCamelCase										=  model(snake_case__    ,		noise=snake_case__)

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

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



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							np.random.seed(2)

							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

												return inputs_np_dict

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

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

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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							np.random.seed(2)

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

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

							super().check_pt_tf_models(snake_case__    ,		snake_case__    ,		snake_case__)



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							np.random.seed(2)

							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase										=  {
							    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__)
							}

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

							for main_layer_class in tf_main_layer_classes:
												_UpperCamelCase										=  main_layer_class(snake_case__)

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

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

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



		@slow
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							np.random.seed(2)

							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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

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



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							np.random.seed(2)

							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

												_UpperCamelCase										=  model(snake_case__    ,		noise=snake_case__)
												_UpperCamelCase										=  model.get_config()
												# make sure that returned config is jsonifiable, which is required by keras
												json.dumps(snake_case__)
												_UpperCamelCase										=  model_class.from_config(model.get_config())
												# make sure it also accepts a normal config
												_UpperCamelCase										=  model_class.from_config(model.config)
												_UpperCamelCase										=  new_model(snake_case__)  # Build model
												new_model.set_weights(model.get_weights())
												_UpperCamelCase										=  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\n    to get deterministic results.''')
		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							pass



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






							'''simple docstring'''
							pass




		@slow
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''')
							self.assertIsNotNone(snake_case__)



def 				lowerCamelCase__		(      )							->		Any:


					"""simple docstring"""





					_UpperCamelCase										=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
					return image






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



		@cached_property
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''') if is_vision_available() else None




		@slow
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							np.random.seed(2)

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

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

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

							# verify the logits
							_UpperCamelCase										=  tf.convert_to_tensor([1, 1_96, 7_68])
							self.assertEqual(outputs.logits.shape    ,		snake_case__)

							_UpperCamelCase										=  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]    ,		snake_case__    ,		atol=1e-4)








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'xlm-roberta-xl'



		def __init__( self    ,		__a=25_08_80    ,		__a=25_60    ,		__a=36    ,		__a=32    ,		__a=1_02_40    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_14    ,		__a=1    ,		__a=0.02    ,		__a=1e-05    ,		__a=1    ,		__a=0    ,		__a=2    ,		__a="absolute"    ,		__a=True    ,		__a=None    ,		**__a    ,		)	->	Tuple:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  classifier_dropout






class    _UpperCAmelCase(   lowerCamelCase     ):



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase										=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ])






