"""simple docstring"""


from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class        a				:


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]=13   ,				__lowerCAmelCase	:  Optional[Any]=30   ,				__lowerCAmelCase	:  Optional[Any]=2   ,				__lowerCAmelCase	:  List[str]=3   ,				__lowerCAmelCase	:  List[Any]=True   ,				__lowerCAmelCase	:  List[str]=True   ,				__lowerCAmelCase	:  List[str]=32   ,				__lowerCAmelCase	:  Any=2   ,				__lowerCAmelCase	:  Optional[int]=4   ,				__lowerCAmelCase	:  Optional[Any]=37   ,				__lowerCAmelCase	:  str="gelu"   ,				__lowerCAmelCase	:  Optional[Any]=0.1   ,				__lowerCAmelCase	:  Optional[Any]=0.1   ,				__lowerCAmelCase	:  int=10   ,				__lowerCAmelCase	:  List[Any]=0.02   ,				__lowerCAmelCase	:  Optional[Any]=3   ,				__lowerCAmelCase	:  int=None   ,				):
      _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

      # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      _UpperCAmelCase						 =      (image_size // patch_size) ** 2
      _UpperCAmelCase						 =      num_patches + 1


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _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      lowerCAmelCase_  (					self	:  Optional[Any]					):
      return ViTConfig(
          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=__lowerCAmelCase   ,				initializer_range=self.initializer_range   ,				)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      TFViTModel(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)					)

      # Test with an image with different size than the one specified in config.
      _UpperCAmelCase						 =      self.image_size // 2
      _UpperCAmelCase						 =      pixel_values[:, :, :image_size, :image_size]
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				interpolate_pos_encoding=__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      _UpperCAmelCase						 =      (image_size // self.patch_size) ** 2 + 1
      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, seq_length, self.hidden_size)					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.type_sequence_label_size
      _UpperCAmelCase						 =      TFViTForImageClassification(__lowerCAmelCase					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				labels=__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)

      # Test with an image with different size than the one specified in config.
      _UpperCAmelCase						 =      self.image_size // 2
      _UpperCAmelCase						 =      pixel_values[:, :, :image_size, :image_size]
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				interpolate_pos_encoding=__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)

      # test greyscale images
      _UpperCAmelCase						 =      1
      _UpperCAmelCase						 =      TFViTForImageClassification(__lowerCAmelCase					)
      _UpperCAmelCase						 =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _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   ):
    _snake_case     :  List[Any]     							=							(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    _snake_case     :  int     							=							(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    _snake_case     :  Optional[int]     							=							False
    _snake_case     :  Optional[Any]     							=							False
    _snake_case     :  Union[str, Any]     							=							False


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      TFViTModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__lowerCAmelCase   ,				has_text_modality=__lowerCAmelCase   ,				hidden_size=37					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      self.config_tester.run_common_tests()


    @unittest.skip(reason="""ViT does not use inputs_embeds"""					)
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      pass


    @unittest.skip(reason="""ViT does not use inputs_embeds"""					)
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      pass


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()

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


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase						 =      model_class(__lowerCAmelCase					)
        _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]   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase					)


    @slow
    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      TFViTModel.from_pretrained("""google/vit-base-patch16-224"""					)
      self.assertIsNotNone(__lowerCAmelCase					)






def     __UpperCAmelCase					(			):







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


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


    @cached_property
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""					) if is_vision_available() else None


    @slow
    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""					)

      _UpperCAmelCase						 =      self.default_image_processor
      _UpperCAmelCase						 =      prepare_img()
      _UpperCAmelCase						 =      image_processor(images=__lowerCAmelCase   ,				return_tensors="""tf"""					)

      # forward pass
      _UpperCAmelCase						 =      model(**__lowerCAmelCase					)

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

      _UpperCAmelCase						 =      tf.constant([-0.2_744, 0.8_215, -0.0_836]					)

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

"""simple docstring"""


def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  _UpperCAmelCase						 =      0
  while n > 0:
    res += n % 10
    n //= 10
  return res






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  return n if n < 10 else n % 10 + sum_of_digits(n // 10		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return sum(int(lowercase		) for c in str(abs(lowercase		)		)		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(lowercase   ,lowercase		) -> None:
    _UpperCAmelCase						 =      f'''{func.__name__}({value})'''
    _UpperCAmelCase						 =      timeit(f'''__main__.{call}'''   ,setup="""import __main__"""		)
    print(f'''{call:56} = {func(lowercase		)} -- {timing:.4f} seconds'''		)

  for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
    for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
      benchmark_a_function(lowercase   ,lowercase		)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
