






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_				:







				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=0.02       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=0.6       ,					__lowerCAmelCase=None       ,					):
								"""simple docstring"""
								__magic_name__		:Tuple        =      parent
								__magic_name__		:Dict        =      batch_size
								__magic_name__		:Any        =      image_size
								__magic_name__		:Dict        =      patch_size
								__magic_name__		:List[Any]        =      num_channels
								__magic_name__		:int        =      is_training
								__magic_name__		:Union[str, Any]        =      use_labels
								__magic_name__		:str        =      hidden_size
								__magic_name__		:Dict        =      num_hidden_layers
								__magic_name__		:Any        =      num_attention_heads
								__magic_name__		:Dict        =      intermediate_size
								__magic_name__		:Optional[Any]        =      hidden_act
								__magic_name__		:Optional[Any]        =      hidden_dropout_prob
								__magic_name__		:Optional[int]        =      attention_probs_dropout_prob
								__magic_name__		:str        =      type_sequence_label_size
								__magic_name__		:int        =      initializer_range
								__magic_name__		:Union[str, Any]        =      mask_ratio
								__magic_name__		:Union[str, Any]        =      scope

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

								__magic_name__		:int        =      None
								if self.use_labels:
												__magic_name__		:str        =      ids_tensor([self.batch_size]       ,					self.type_sequence_label_size     )

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

								return config, pixel_values, labels







				def 			A	(						self     ):
								"""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=__lowerCAmelCase       ,					initializer_range=self.initializer_range       ,					mask_ratio=self.mask_ratio       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      TFViTMAEModel(config=__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      model(__lowerCAmelCase       ,					training=__lowerCAmelCase     )
								self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      TFViTMAEForPreTraining(__lowerCAmelCase     )
								__magic_name__		:Optional[int]        =      model(__lowerCAmelCase       ,					training=__lowerCAmelCase     )
								# expected sequence length = num_patches
								__magic_name__		:Dict        =      (self.image_size // self.patch_size) ** 2
								__magic_name__		:str        =      self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, num_patches, expected_num_channels)     )

								# test greyscale images
								__magic_name__		:Tuple        =      1
								__magic_name__		:int        =      TFViTMAEForPreTraining(__lowerCAmelCase     )

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.prepare_config_and_inputs()
								((__magic_name__)     ,				(__magic_name__)     ,				(__magic_name__))		:List[Any]        =      config_and_inputs
								__magic_name__		:int        =      {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_tf
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
				a__					       =  {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      TFViTMAEModelTester(self     )
								__magic_name__		:int        =      ConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase       ,					hidden_size=3_7     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.config_tester.run_common_tests()







				@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Optional[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:str        =      model_class(__lowerCAmelCase     )
												self.assertIsInstance(model.get_input_embeddings()       ,					(tf.keras.layers.Layer)     )
												__magic_name__		:List[Any]        =      model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__lowerCAmelCase       ,					tf.keras.layers.Layer     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:List[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

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

												__magic_name__		:Tuple        =      ['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								# make the mask reproducible
								np.random.seed(2     )

								__magic_name__     ,				__magic_name__		:Optional[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

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

								for model_class in self.all_model_classes:
												__magic_name__		:Any        =      model_class(__lowerCAmelCase     )
												__magic_name__		:Tuple        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )

												__magic_name__		:Any        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

												__magic_name__		:Optional[Any]        =      copy.deepcopy(self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )
												__magic_name__		:List[str]        =      model(**__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
												__magic_name__		:Optional[Any]        =      outputs_dict[0].numpy()
												__magic_name__		:str        =      outputs_keywords[0].numpy()

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







				def 			A	(						self     ):
								"""simple docstring"""
								# make the mask reproducible
								np.random.seed(2     )

								__magic_name__     ,				__magic_name__		:int        =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

												return inputs_np_dict

								for model_class in self.all_model_classes:
												__magic_name__		:Dict        =      model_class(__lowerCAmelCase     )

												__magic_name__		:Optional[int]        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )
												__magic_name__		:int        =      prepare_numpy_arrays(__lowerCAmelCase     )

												__magic_name__		:Union[str, Any]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
												__magic_name__		:Any        =      model(**__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
												self.assert_outputs_same(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								# make masks reproducible
								np.random.seed(2     )

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

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

								super().check_pt_tf_models(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								# make mask reproducible
								np.random.seed(2     )

								__magic_name__     ,				__magic_name__		:Dict        =      self.model_tester.prepare_config_and_inputs_for_common()

								__magic_name__		:Optional[int]        =      {
								    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     )
								}

								__magic_name__		:Union[str, Any]        =      int((config.image_size // config.patch_size) ** 2     )
								__magic_name__		:Union[str, Any]        =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
								__magic_name__		:Dict        =      tf.convert_to_tensor(__lowerCAmelCase     )
								inputs_dict.update({'''noise''': noise}     )

								for main_layer_class in tf_main_layer_classes:
												__magic_name__		:Tuple        =      main_layer_class(__lowerCAmelCase     )

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

												__magic_name__		:List[Any]        =      tf.keras.Model(__lowerCAmelCase       ,					outputs=main_layer(__lowerCAmelCase     )     )
												__magic_name__		:int        =      model(__lowerCAmelCase     )

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







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								# make mask reproducible
								np.random.seed(2     )

								__magic_name__     ,				__magic_name__		:List[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

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

								for model_class in self.all_model_classes:
												__magic_name__		:str        =      model_class(__lowerCAmelCase     )
												__magic_name__		:Optional[int]        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )
												__magic_name__		:List[Any]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

												if model_class.__name__ == "TFViTMAEModel":
																__magic_name__		:Optional[int]        =      outputs.last_hidden_state.numpy()
																__magic_name__		:Union[str, Any]        =      0
												else:
																__magic_name__		:Any        =      outputs.logits.numpy()
																__magic_name__		:Union[str, Any]        =      0

												with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(__lowerCAmelCase       ,					saved_model=__lowerCAmelCase     )
																__magic_name__		:Union[str, Any]        =      model_class.from_pretrained(__lowerCAmelCase     )
																__magic_name__		:List[str]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

																if model_class.__name__ == "TFViTMAEModel":
																				__magic_name__		:Tuple        =      after_outputs['''last_hidden_state'''].numpy()
																				__magic_name__		:Optional[int]        =      0
																else:
																				__magic_name__		:int        =      after_outputs['''logits'''].numpy()
																				__magic_name__		:Union[str, Any]        =      0

																__magic_name__		:Optional[int]        =      np.amax(np.abs(out_a - out_a     )     )
																self.assertLessEqual(__lowerCAmelCase       ,					1E-5     )







				def 			A	(						self     ):
								"""simple docstring"""
								# make mask reproducible
								np.random.seed(2     )

								__magic_name__     ,				__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs_for_common()

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

								for model_class in self.all_model_classes:
												__magic_name__		:List[Any]        =      model_class(__lowerCAmelCase     )
												__magic_name__		:Union[str, Any]        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )

												__magic_name__		:List[str]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
												__magic_name__		:List[Any]        =      model.get_config()
												# make sure that returned config is jsonifiable, which is required by keras
												json.dumps(__lowerCAmelCase     )
												__magic_name__		:int        =      model_class.from_config(model.get_config()     )
												# make sure it also accepts a normal config
												__magic_name__		:Dict        =      model_class.from_config(model.config     )
												__magic_name__		:Any        =      new_model(__lowerCAmelCase     )  # Build model
												new_model.set_weights(model.get_weights()     )
												__magic_name__		:Any        =      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 			A	(						self     ):
								"""simple docstring"""
								pass







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







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''     )
								self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







				"""simple docstring"""






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

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







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







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

								__magic_name__		:Union[str, Any]        =      TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''     )

								__magic_name__		:str        =      self.default_image_processor
								__magic_name__		:str        =      prepare_img()
								__magic_name__		:List[str]        =      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)
								__magic_name__		:List[str]        =      ViTMAEConfig()
								__magic_name__		:Dict        =      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
								__magic_name__		:Dict        =      np.random.uniform(size=(1, num_patches)     )

								# forward pass
								__magic_name__		:Tuple        =      model(**__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

								# verify the logits
								__magic_name__		:Tuple        =      tf.convert_to_tensor([1, 1_9_6, 7_6_8]     )
								self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

								__magic_name__		:Any        =      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
					from .tokenization_herbert import HerbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_herbert_fast import HerbertTokenizerFast

else:
					import sys

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






