

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase=13							,  lowerCamelCase=32							,  lowerCamelCase=3							,  lowerCamelCase=4							,  lowerCamelCase=[10, 20, 30, 40]							,  lowerCamelCase=[2, 2, 3, 2]							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase=37							,  lowerCamelCase="gelu"							,  lowerCamelCase=10							,  lowerCamelCase=0.02							,  lowerCamelCase=3							,  lowerCamelCase=None							,  )						->       List[Any]:
        snake_case_       						=      parent
        snake_case_       						=      batch_size
        snake_case_       						=      image_size
        snake_case_       						=      num_channels
        snake_case_       						=      num_stages
        snake_case_       						=      hidden_sizes
        snake_case_       						=      depths
        snake_case_       						=      is_training
        snake_case_       						=      use_labels
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      type_sequence_label_size
        snake_case_       						=      initializer_range
        snake_case_       						=      scope







      def 	lowerCAmelCase_    (     self   )						->       Any:
        snake_case_       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

        snake_case_       						=      self.get_config()

        return config, pixel_values, labels







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        return ConvNextConfig(
            num_channels=self.num_channels							,  hidden_sizes=self.hidden_sizes							,  depths=self.depths							,  num_stages=self.num_stages							,  hidden_act=self.hidden_act							,  is_decoder=lowerCamelCase							,  initializer_range=self.initializer_range							,  )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      TFConvNextModel(config=lowerCamelCase   )
        snake_case_       						=      model(lowerCamelCase							,  training=lowerCamelCase   )
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape							,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,  )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      self.type_sequence_label_size
        snake_case_       						=      TFConvNextForImageClassification(lowerCamelCase   )
        snake_case_       						=      model(lowerCamelCase							,  labels=lowerCamelCase							,  training=lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)   )







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







@require_tf
class  __lowerCamelCase				(	__snake_case  ,							__snake_case  ,							unittest.TestCase	):
      lowerCamelCase_	:      Union[str, Any]          =					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
      lowerCamelCase_	:      Any          =					(
          {'feature-extraction': TFConvNextModel, 'image-classification': TFConvNextForImageClassification}
          if is_tf_available()
          else {}
      )

      lowerCamelCase_	:      Any          =					False
      lowerCamelCase_	:      List[str]          =					False
      lowerCamelCase_	:      Any          =					False
      lowerCamelCase_	:      Dict          =					False
      lowerCamelCase_	:      Optional[Any]          =					False







      def 	lowerCAmelCase_    (     self   )						->       str:
        snake_case_       						=      TFConvNextModelTester(self   )
        snake_case_       						=      ConfigTester(
            self							,  config_class=lowerCamelCase							,  has_text_modality=lowerCamelCase							,  hidden_size=37							,  )







      @unittest.skip(reason="""ConvNext does not use inputs_embeds"""   )
      def 	lowerCAmelCase_    (     self   )						->       Any:
        pass







      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        super().test_keras_fit()







      @unittest.skip(reason="""ConvNext does not support input and output embeddings"""   )
      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        pass







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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







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







      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        super().test_dataset_conversion()







      def 	lowerCAmelCase_    (     self   )						->       str:
        def check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ):
          snake_case_       						=      model_class(lowerCamelCase   )

          snake_case_       						=      model(**self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )   )
          snake_case_       						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          snake_case_       						=      self.model_tester.num_stages
          self.assertEqual(len(lowerCamelCase   )							,  expected_num_stages + 1   )

          # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]   )							,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,  )

        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          snake_case_       						=      True
          check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          snake_case_       						=      True

          check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase={}   ):
          snake_case_       						=      model(lowerCamelCase							,  return_dict=lowerCamelCase							,  **lowerCamelCase   )
          snake_case_       						=      model(lowerCamelCase							,  return_dict=lowerCamelCase							,  **lowerCamelCase   ).to_tuple()

          def recursive_check(lowerCamelCase							,  lowerCamelCase   ):
            if isinstance(lowerCamelCase							,  (List, Tuple)   ):
              for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase							,  lowerCamelCase   ):
                recursive_check(lowerCamelCase							,  lowerCamelCase   )
            elif tuple_object is None:
              return
            else:
              self.assertTrue(
                  all(tf.equal(lowerCamelCase							,  lowerCamelCase   )   )							,  msg=(
                      """Tuple and dict output are not equal. Difference:"""
                      f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'''
                  )							,  )

            recursive_check(lowerCamelCase							,  lowerCamelCase   )

        for model_class in self.all_model_classes:
          snake_case_       						=      model_class(lowerCamelCase   )

          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )
          check_equivalence(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
          check_equivalence(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )
          check_equivalence(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  {"""output_hidden_states""": True}   )

          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
          snake_case_       						=      self._prepare_for_class(lowerCamelCase							,  lowerCamelCase							,  return_labels=lowerCamelCase   )
          check_equivalence(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  {"""output_hidden_states""": True}   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase   )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_       						=      TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224"""   )
        self.assertIsNotNone(lowerCamelCase   )



def    UpperCamelCase(					)	->				Tuple:





  '''simple docstring'''





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







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







      @cached_property
      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""   ) if is_vision_available() else None







      @slow
      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""   )

        snake_case_       						=      self.default_image_processor
        snake_case_       						=      prepare_img()
        snake_case_       						=      image_processor(images=lowerCamelCase							,  return_tensors="""tf"""   )

        # forward pass
        snake_case_       						=      model(**lowerCamelCase   )

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

        snake_case_       						=      tf.constant([-0.0260, -0.4739, 0.1911]   )

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


def    UpperCamelCase(					lowercase_							)	->				str:





  '''simple docstring'''





  if len(lowercase_							) == 0 or not all(len(lowercase_							) == 2 for pair in coordinates							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  if len({tuple(lowercase_							) for pair in coordinates}							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      {x for x, _ in coordinates}
  if len(lowercase_							) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(lowercase_							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      len(lowercase_							)

  snake_case_       						=      0
  snake_case_       						=      []
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    snake_case_       						=      0
    snake_case_       						=      coordinates[count_of_line][0]
    snake_case_       						=      []
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))							)
      count_in_line += 1
    matrix.append(lowercase_							)
    count_of_line += 1

  snake_case_       						=      0
  # put the y values into a vector
  snake_case_       						=      []
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]							)
    count_of_line += 1

  snake_case_       						=      0

  while count < x:
    snake_case_       						=      0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      snake_case_       						=      matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]							):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  snake_case_       						=      0
  # make solutions
  snake_case_       						=      []
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]							)							)
    count += 1

  snake_case_       						=      0
  snake_case_       						=      """f(x)="""

  while count < x:
    snake_case_       						=      solution[count].split("""E"""							)
    if len(lowercase_							) > 1:
      snake_case_       						=      f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))