


'''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    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Tuple    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Optional[int]=13    ,						lowerCamelCase    :  Union[str, Any]=30    ,						lowerCamelCase    :  Dict=2    ,						lowerCamelCase    :  Dict=3    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=32    ,						lowerCamelCase    :  Any=2    ,						lowerCamelCase    :  List[Any]=4    ,						lowerCamelCase    :  Any=37    ,						lowerCamelCase    :  Optional[Any]="gelu"    ,						lowerCamelCase    :  Dict=0.1    ,						lowerCamelCase    :  List[str]=0.1    ,						lowerCamelCase    :  int=10    ,						lowerCamelCase    :  Dict=0.02    ,						lowerCamelCase    :  Optional[Any]=3    ,						lowerCamelCase    :  Any=0.6    ,						lowerCamelCase    :  Any=None    ,						)   ->  Dict:
      lowerCAmelCase_			:      int			=   parent
      lowerCAmelCase_			:      Any			=   batch_size
      lowerCAmelCase_			:      Optional[int]			=   image_size
      lowerCAmelCase_			:      Optional[int]			=   patch_size
      lowerCAmelCase_			:      Any			=   num_channels
      lowerCAmelCase_			:      str			=   is_training
      lowerCAmelCase_			:      Dict			=   use_labels
      lowerCAmelCase_			:      List[str]			=   hidden_size
      lowerCAmelCase_			:      Optional[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[Any]			=   num_attention_heads
      lowerCAmelCase_			:      List[Any]			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      Dict			=   hidden_dropout_prob
      lowerCAmelCase_			:      Optional[int]			=   attention_probs_dropout_prob
      lowerCAmelCase_			:      int			=   type_sequence_label_size
      lowerCAmelCase_			:      Any			=   initializer_range
      lowerCAmelCase_			:      Optional[int]			=   mask_ratio
      lowerCAmelCase_			:      Tuple			=   scope

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

    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Optional[Any]			=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

      lowerCAmelCase_			:      List[Any]			=   None
      if self.use_labels:
        lowerCAmelCase_			:      int			=   ids_tensor([self.batch_size]    ,						self.type_sequence_label_size		)

      lowerCAmelCase_			:      Any			=   self.get_config()

      return config, pixel_values, labels

    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      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 __lowercase   (   self    :  Any    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int		)   ->  Dict:
      lowerCAmelCase_			:      Optional[Any]			=   TFViTMAEModel(config=lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   model(lowerCamelCase    ,						training=lowerCamelCase		)
      self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Tuple    ,						lowerCamelCase    :  Union[str, Any]		)   ->  Optional[int]:
      lowerCAmelCase_			:      Dict			=   TFViTMAEForPreTraining(lowerCamelCase		)
      lowerCAmelCase_			:      Any			=   model(lowerCamelCase    ,						training=lowerCamelCase		)
      # expected sequence length = num_patches
      lowerCAmelCase_			:      int			=   (self.image_size // self.patch_size) ** 2
      lowerCAmelCase_			:      Dict			=   self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)		)

      # test greyscale images
      lowerCAmelCase_			:      Tuple			=   1
      lowerCAmelCase_			:      Tuple			=   TFViTMAEForPreTraining(lowerCamelCase		)

      lowerCAmelCase_			:      List[str]			=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
      lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						training=lowerCamelCase		)
      lowerCAmelCase_			:      Union[str, Any]			=   self.patch_size**2
      self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)		)





    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      List[str]			=   self.prepare_config_and_inputs()
      ((lowerCAmelCase_),							(lowerCAmelCase_),							(lowerCAmelCase_))			:      List[str]			=   config_and_inputs
      lowerCAmelCase_			:      Union[str, Any]			=   {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_tf
class    __snake_case       (   _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,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 __lowercase   (   self    :  str		)   ->  str:
      lowerCAmelCase_			:      List[str]			=   TFViTMAEModelTester(self		)
      lowerCAmelCase_			:      int			=   ConfigTester(self    ,						config_class=lowerCamelCase    ,						has_text_modality=lowerCamelCase    ,						hidden_size=37		)

    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      self.config_tester.run_common_tests()

    @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""		)
    def __lowercase   (   self    :  int		)   ->  Optional[Any]:
      pass

    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   self.model_tester.prepare_config_and_inputs_for_common()

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

    def __lowercase   (   self    :  Dict		)   ->  Optional[int]:
      lowerCAmelCase_,							lowerCAmelCase_			:      Any			=   self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase_			:      Tuple			=   ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]    ,						lowerCamelCase		)

    def __lowercase   (   self    :  List[str]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      str			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase   (   self    :  Union[str, Any]		)   ->  str:
      lowerCAmelCase_			:      Union[str, Any]			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]		)   ->  Any:
      # make the mask reproducible
      np.random.seed(2		)

      lowerCAmelCase_,							lowerCAmelCase_			:      Union[str, Any]			=   self.model_tester.prepare_config_and_inputs_for_common()

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

      for model_class in self.all_model_classes:
        lowerCAmelCase_			:      Union[str, Any]			=   model_class(lowerCamelCase		)
        lowerCAmelCase_			:      int			=   self._prepare_for_class(lowerCamelCase    ,						lowerCamelCase		)

        lowerCAmelCase_			:      Any			=   model(lowerCamelCase    ,						noise=lowerCamelCase		)

        lowerCAmelCase_			:      Optional[int]			=   copy.deepcopy(self._prepare_for_class(lowerCamelCase    ,						lowerCamelCase		)		)
        lowerCAmelCase_			:      Tuple			=   model(**lowerCamelCase    ,						noise=lowerCamelCase		)
        lowerCAmelCase_			:      str			=   outputs_dict[0].numpy()
        lowerCAmelCase_			:      List[Any]			=   outputs_keywords[0].numpy()

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

    def __lowercase   (   self    :  List[Any]		)   ->  Optional[int]:
      # make the mask reproducible
      np.random.seed(2		)

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

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

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

        return inputs_np_dict

      for model_class in self.all_model_classes:
        lowerCAmelCase_			:      int			=   model_class(lowerCamelCase		)

        lowerCAmelCase_			:      Dict			=   self._prepare_for_class(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      List[Any]			=   prepare_numpy_arrays(lowerCamelCase		)

        lowerCAmelCase_			:      int			=   model(lowerCamelCase    ,						noise=lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   model(**lowerCamelCase    ,						noise=lowerCamelCase		)
        self.assert_outputs_same(lowerCamelCase    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  Dict		)   ->  Optional[Any]:
      # make masks reproducible
      np.random.seed(2		)

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

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

      super().check_pt_tf_models(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		)

    def __lowercase   (   self    :  int		)   ->  Union[str, Any]:
      # make mask reproducible
      np.random.seed(2		)

      lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   self.model_tester.prepare_config_and_inputs_for_common()

      lowerCAmelCase_			:      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		)
      }

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

      for main_layer_class in tf_main_layer_classes:
        lowerCAmelCase_			:      str			=   main_layer_class(lowerCamelCase		)

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

        lowerCAmelCase_			:      str			=   tf.keras.Model(lowerCamelCase    ,						outputs=main_layer(lowerCamelCase		)		)
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase		)

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

    @slow
    def __lowercase   (   self    :  List[Any]		)   ->  Any:
      # make mask reproducible
      np.random.seed(2		)

      lowerCAmelCase_,							lowerCAmelCase_			:      Optional[int]			=   self.model_tester.prepare_config_and_inputs_for_common()

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

      for model_class in self.all_model_classes:
        lowerCAmelCase_			:      int			=   model_class(lowerCamelCase		)
        lowerCAmelCase_			:      Union[str, Any]			=   self._prepare_for_class(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      Union[str, Any]			=   model(lowerCamelCase    ,						noise=lowerCamelCase		)

        if model_class.__name__ == "TFViTMAEModel":
          lowerCAmelCase_			:      Optional[Any]			=   outputs.last_hidden_state.numpy()
          lowerCAmelCase_			:      int			=   0
        else:
          lowerCAmelCase_			:      Tuple			=   outputs.logits.numpy()
          lowerCAmelCase_			:      List[str]			=   0

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(lowerCamelCase    ,						saved_model=lowerCamelCase		)
          lowerCAmelCase_			:      int			=   model_class.from_pretrained(lowerCamelCase		)
          lowerCAmelCase_			:      List[Any]			=   model(lowerCamelCase    ,						noise=lowerCamelCase		)

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

          lowerCAmelCase_			:      str			=   np.amax(np.abs(out_a - out_a		)		)
          self.assertLessEqual(lowerCamelCase    ,						1E-5		)

    def __lowercase   (   self    :  int		)   ->  str:
      # make mask reproducible
      np.random.seed(2		)

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

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

      for model_class in self.all_model_classes:
        lowerCAmelCase_			:      int			=   model_class(lowerCamelCase		)
        lowerCAmelCase_			:      Union[str, Any]			=   self._prepare_for_class(lowerCamelCase    ,						lowerCamelCase		)

        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						noise=lowerCamelCase		)
        lowerCAmelCase_			:      Dict			=   model.get_config()
        # make sure that returned config is jsonifiable, which is required by keras
        json.dumps(lowerCamelCase		)
        lowerCAmelCase_			:      Optional[int]			=   model_class.from_config(model.get_config()		)
        # make sure it also accepts a normal config
        lowerCAmelCase_			:      int			=   model_class.from_config(model.config		)
        lowerCAmelCase_			:      Any			=   new_model(lowerCamelCase		)  # Build model
        new_model.set_weights(model.get_weights()		)
        lowerCAmelCase_			:      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 __lowercase   (   self    :  Any		)   ->  List[Any]:
      pass

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





    @slow
    def __lowercase   (   self    :  Union[str, Any]		)   ->  Any:
      lowerCAmelCase_			:      List[str]			=   TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""		)
      self.assertIsNotNone(lowerCamelCase		)







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





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






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







    """simple docstring"""



    @cached_property
    def __lowercase   (   self    :  Union[str, Any]		)   ->  List[Any]:
      return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""		) if is_vision_available() else None





    @slow
    def __lowercase   (   self    :  List[Any]		)   ->  List[Any]:
      # make random mask reproducible across the PT and TF model
      np.random.seed(2		)

      lowerCAmelCase_			:      str			=   TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		)

      lowerCAmelCase_			:      Any			=   self.default_image_processor
      lowerCAmelCase_			:      Any			=   prepare_img()
      lowerCAmelCase_			:      Optional[Any]			=   image_processor(images=lowerCamelCase    ,						return_tensors="""tf"""		)

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      lowerCAmelCase_			:      Optional[Any]			=   ViTMAEConfig()
      lowerCAmelCase_			:      str			=   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
      lowerCAmelCase_			:      Optional[Any]			=   np.random.uniform(size=(1, num_patches)		)

      # forward pass
      lowerCAmelCase_			:      Optional[Any]			=   model(**lowerCamelCase    ,						noise=lowerCamelCase		)

      # verify the logits
      lowerCAmelCase_			:      List[str]			=   tf.convert_to_tensor([1, 1_96, 7_68]		)
      self.assertEqual(outputs.logits.shape    ,						lowerCamelCase		)

      lowerCAmelCase_			:      Union[str, Any]			=   tf.convert_to_tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

      tf.debugging.assert_near(outputs.logits[0, :3, :3]    ,						lowerCamelCase    ,						atol=1E-4		)







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   str							,    A__					:   list[str]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   """"""
  for word_or_phrase in separated:
    if not isinstance(A__							,    A__					):
      raise Exception("""join() accepts only strings to be joined"""					)
    joined += word_or_phrase + separator
  return joined.strip(A__					)


if __name__ == "__main__":
  from doctest import testmod

  testmod()



