
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_			:


  '''simple docstring'''







  def __init__( self			: int			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: int=13			,							UpperCamelCase__			: Union[str, Any]=30			,							UpperCamelCase__			: Optional[Any]=2			,							UpperCamelCase__			: Optional[Any]=3			,							UpperCamelCase__			: Any=True			,							UpperCamelCase__			: str=True			,							UpperCamelCase__			: Tuple=32			,							UpperCamelCase__			: Optional[Any]=2			,							UpperCamelCase__			: Optional[int]=4			,							UpperCamelCase__			: List[str]=37			,							UpperCamelCase__			: Tuple="gelu"			,							UpperCamelCase__			: Optional[int]=0.1			,							UpperCamelCase__			: str=0.1			,							UpperCamelCase__			: int=10			,							UpperCamelCase__			: List[str]=0.02			,							UpperCamelCase__			: Tuple=3			,							UpperCamelCase__			: List[str]=0.6			,							UpperCamelCase__			: Dict=None			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       image_size
    __magic_name__				     =       patch_size
    __magic_name__				     =       num_channels
    __magic_name__				     =       is_training
    __magic_name__				     =       use_labels
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       type_sequence_label_size
    __magic_name__				     =       initializer_range
    __magic_name__				     =       mask_ratio
    __magic_name__				     =       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__				     =       (image_size // patch_size) ** 2
    __magic_name__				     =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )
  def 							_lowercase							( self			: Dict    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

    __magic_name__				     =       self.get_config()

    return config, pixel_values, labels
  def 							_lowercase							( self			: Optional[int]    )			->	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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							mask_ratio=self.mask_ratio			,							)
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: int    )			->	str:







    """simple docstring"""






    __magic_name__				     =       TFViTMAEModel(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__			,							training=UpperCamelCase__    )
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Optional[int]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       TFViTMAEForPreTraining(UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__			,							training=UpperCamelCase__    )
    # expected sequence length = num_patches
    __magic_name__				     =       (self.image_size // self.patch_size) ** 2
    __magic_name__				     =       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__				     =       1
    __magic_name__				     =       TFViTMAEForPreTraining(UpperCamelCase__    )

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





  def 							_lowercase							( self			: Optional[Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       self.prepare_config_and_inputs()
    ((__magic_name__)     ,    (__magic_name__)     ,    (__magic_name__))				     =       config_and_inputs
    __magic_name__				     =       {"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_tf
class        UpperCAmelCase_			( _A  ,       _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  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 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       TFViTMAEModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37    )
  def 							_lowercase							( self			: Optional[int]    )			->	str:







    """simple docstring"""






    self.config_tester.run_common_tests()
  @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""    )
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    pass
  def 							_lowercase							( self			: Optional[int]    )			->	Any:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__				     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]			,							UpperCamelCase__    )
  def 							_lowercase							( self			: List[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Any    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    np.random.seed(2    )

    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

    for model_class in self.all_model_classes:
      __magic_name__				     =       model_class(UpperCamelCase__    )
      __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )

      __magic_name__				     =       model(UpperCamelCase__			,							noise=UpperCamelCase__    )

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

      self.assertLess(np.sum(np.abs(output_dict - output_keywords    )    )			,							1E-6    )
  def 							_lowercase							( self			: Dict    )			->	Any:







    """simple docstring"""






    np.random.seed(2    )

    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

      return inputs_np_dict

    for model_class in self.all_model_classes:
      __magic_name__				     =       model_class(UpperCamelCase__    )

      __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )
      __magic_name__				     =       prepare_numpy_arrays(UpperCamelCase__    )

      __magic_name__				     =       model(UpperCamelCase__			,							noise=UpperCamelCase__    )
      __magic_name__				     =       model(**UpperCamelCase__			,							noise=UpperCamelCase__    )
      self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Tuple    )			->	List[Any]:







    """simple docstring"""






    np.random.seed(2    )

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

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

    super().check_pt_tf_models(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: List[str]    )			->	Dict:







    """simple docstring"""






    np.random.seed(2    )

    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

    for main_layer_class in tf_main_layer_classes:
      __magic_name__				     =       main_layer_class(UpperCamelCase__    )

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

      __magic_name__				     =       tf.keras.Model(UpperCamelCase__			,							outputs=main_layer(UpperCamelCase__    )    )
      __magic_name__				     =       model(UpperCamelCase__    )

      with tempfile.TemporaryDirectory() as tmpdirname:
        __magic_name__				     =       os.path.join(UpperCamelCase__			,							"""keras_model.h5"""    )
        model.save(UpperCamelCase__    )
        __magic_name__				     =       tf.keras.models.load_model(
            UpperCamelCase__			,							custom_objects={main_layer_class.__name__: main_layer_class}    )
        assert isinstance(UpperCamelCase__			,							tf.keras.Model    )
        __magic_name__				     =       model(UpperCamelCase__    )
        self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__    )
  @slow
  def 							_lowercase							( self			: Optional[int]    )			->	Dict:







    """simple docstring"""






    np.random.seed(2    )

    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

    for model_class in self.all_model_classes:
      __magic_name__				     =       model_class(UpperCamelCase__    )
      __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )
      __magic_name__				     =       model(UpperCamelCase__			,							noise=UpperCamelCase__    )

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

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

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

        __magic_name__				     =       np.amax(np.abs(out_a - out_a    )    )
        self.assertLessEqual(UpperCamelCase__			,							1E-5    )
  def 							_lowercase							( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    np.random.seed(2    )

    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

    for model_class in self.all_model_classes:
      __magic_name__				     =       model_class(UpperCamelCase__    )
      __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )

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

      self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__    )
  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""    )
  def 							_lowercase							( self			: Dict    )			->	Any:







    """simple docstring"""






    pass
  @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	List[Any]:







    """simple docstring"""






    pass





  @slow
  def 							_lowercase							( self			: Any    )			->	str:







    """simple docstring"""






    __magic_name__				     =       TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""    )
    self.assertIsNotNone(UpperCamelCase__    )







def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image





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


  '''simple docstring'''







  @cached_property
  def 							_lowercase							( self			: Optional[Any]    )			->	Union[str, Any]:







    """simple docstring"""






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





  @slow
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    np.random.seed(2    )

    __magic_name__				     =       TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""    )

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

    # forward pass
    __magic_name__				     =       model(**UpperCamelCase__			,							noise=UpperCamelCase__    )

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

    __magic_name__				     =       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]			,							UpperCamelCase__			,							atol=1E-4    )



from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     List[str]           =   {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     str           =   [
        'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XLMForMultipleChoice',
        'XLMForQuestionAnswering',
        'XLMForQuestionAnsweringSimple',
        'XLMForSequenceClassification',
        'XLMForTokenClassification',
        'XLMModel',
        'XLMPreTrainedModel',
        'XLMWithLMHeadModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Dict           =   [
        'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFXLMForMultipleChoice',
        'TFXLMForQuestionAnsweringSimple',
        'TFXLMForSequenceClassification',
        'TFXLMForTokenClassification',
        'TFXLMMainLayer',
        'TFXLMModel',
        'TFXLMPreTrainedModel',
        'TFXLMWithLMHeadModel',
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

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

