






'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 (
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
         TFDeiTModel,
     )
     from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=30    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=37    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=2    ,	)						->   Optional[Any]:



        """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       						=      scope
        UpperCamelCase       						=      encoder_stride

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        UpperCamelCase       						=      (image_size // patch_size) ** 2
        UpperCamelCase       						=      num_patches + 2

  def 						A__		(   self   )						->   Dict:



        """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 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        return DeiTConfig(
            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    ,	hidden_act=self.hidden_act    ,	hidden_dropout_prob=self.hidden_dropout_prob    ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,	is_decoder=_SCREAMING_SNAKE_CASE    ,	initializer_range=self.initializer_range    ,	encoder_stride=self.encoder_stride    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      TFDeiTModel(config=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      TFDeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(
            result.reconstruction.shape    ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

        # test greyscale images
        UpperCamelCase       						=      1
        UpperCamelCase       						=      TFDeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.reconstruction.shape    ,	(self.batch_size, 1, self.image_size, self.image_size)   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.type_sequence_label_size
        UpperCamelCase       						=      TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.type_sequence_label_size)   )

        # test greyscale images
        UpperCamelCase       						=      1
        UpperCamelCase       						=      TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.type_sequence_label_size)   )


  def 						A__		(   self   )						->   str:



        """simple docstring"""






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






@require_tf
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  lowercase  	=   (
      {
          """feature-extraction""": TFDeiTModel,
          """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

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



        """simple docstring"""






        UpperCamelCase       						=      TFDeiTModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE    ,	hidden_size=37   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""DeiT does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        pass

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



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(_SCREAMING_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]    ,	_SCREAMING_SNAKE_CASE   )

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



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=False   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      super()._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_labels=_SCREAMING_SNAKE_CASE   )

        if return_labels:
              if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call   ).parameters:
                    del inputs_dict["labels"]

        return inputs_dict


  @slow
  def 						A__		(   self   )						->   str:



        """simple docstring"""






        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      TFDeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   )->				Dict:
      UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image






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

  @cached_property
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        return (
            DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""   )
            if is_vision_available()
            else None
        )


  @slow
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""   )

        UpperCamelCase       						=      self.default_image_processor
        UpperCamelCase       						=      prepare_img()
        UpperCamelCase       						=      image_processor(images=_SCREAMING_SNAKE_CASE    ,	return_tensors="""tf"""   )

        # forward pass
        UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )

        # verify the logits
        UpperCamelCase       						=      tf.TensorShape((1, 1000)   )
        self.assertEqual(outputs.logits.shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]   )

        self.assertTrue(np.allclose(outputs.logits[0, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-4   )   )








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase					)->				str:
      if not all(char in """01""" for char in bin_string					):
            raise ValueError("""Non-binary value was passed to the function"""					)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""					)
      UpperCamelCase       						=      """"""
      while len(__UpperCamelCase					) % 3 != 0:
            UpperCamelCase       						=      """0""" + bin_string
      UpperCamelCase       						=      [
          bin_string[index : index + 3]
          for index in range(len(__UpperCamelCase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            UpperCamelCase       						=      0
            for index, val in enumerate(__UpperCamelCase					):
                  oct_val += int(2 ** (2 - index) * int(__UpperCamelCase					)					)
            oct_string += str(__UpperCamelCase					)
      return oct_string


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

     testmod()
