

"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor
class 	_A						(					unittest.TestCase):

  def __init__(			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE=7	,   _SCREAMING_SNAKE_CASE=3	,   _SCREAMING_SNAKE_CASE=18	,   _SCREAMING_SNAKE_CASE=30	,   _SCREAMING_SNAKE_CASE=400	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=0.875	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]	,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       size if size is not None else {'shortest_edge': 20}
        SCREAMING_SNAKE_CASE_			:					List[str]          =       parent
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       batch_size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       num_channels
        SCREAMING_SNAKE_CASE_			:					int          =       image_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       min_resolution
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       max_resolution
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       do_resize
        SCREAMING_SNAKE_CASE_			:					str          =       size
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       crop_pct
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       do_normalize
        SCREAMING_SNAKE_CASE_			:					int          =       image_mean
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       image_std



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_pct": self.crop_pct,
        }

@require_torch
@require_vision
class 	_A						(					__magic_name__				,       unittest.TestCase):
  SCREAMING_SNAKE_CASE      : Dict    =					ConvNextImageProcessor if is_vision_available() else None

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       ConvNextImageProcessingTester(self   )

  @property
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        return self.image_processor_tester.prepare_image_processor_dict()

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,   'do_resize'   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,   'size'   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,   'crop_pct'   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,   'do_normalize'   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,   'image_mean'   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,   'image_std'   )   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size	,   {'shortest_edge': 20}   )

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       self.image_processing_class.from_dict(self.image_processor_dict	,   size=42   )
        self.assertEqual(image_processor.size	,   {'shortest_edge': 42}   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        pass

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_			:					Any          =       prepare_image_inputs(self.image_processor_tester	,   equal_resolution=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE	,   Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       image_processing(image_inputs[0]	,   return_tensors='pt'   ).pixel_values
        self.assertEqual(
            encoded_images.shape	,   (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )	,   )

        # Test batched
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       image_processing(_SCREAMING_SNAKE_CASE	,   return_tensors='pt'   ).pixel_values
        self.assertEqual(
            encoded_images.shape	,   (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )	,   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_			:					Dict          =       prepare_image_inputs(self.image_processor_tester	,   equal_resolution=_SCREAMING_SNAKE_CASE	,   numpify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE	,   np.ndarray   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_			:					List[str]          =       image_processing(image_inputs[0]	,   return_tensors='pt'   ).pixel_values
        self.assertEqual(
            encoded_images.shape	,   (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )	,   )

        # Test batched
        SCREAMING_SNAKE_CASE_			:					Any          =       image_processing(_SCREAMING_SNAKE_CASE	,   return_tensors='pt'   ).pixel_values
        self.assertEqual(
            encoded_images.shape	,   (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )	,   )



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       prepare_image_inputs(self.image_processor_tester	,   equal_resolution=_SCREAMING_SNAKE_CASE	,   torchify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE	,   torch.Tensor   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       image_processing(image_inputs[0]	,   return_tensors='pt'   ).pixel_values
        self.assertEqual(
            encoded_images.shape	,   (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )	,   )

        # Test batched
        SCREAMING_SNAKE_CASE_			:					str          =       image_processing(_SCREAMING_SNAKE_CASE	,   return_tensors='pt'   ).pixel_values
        self.assertEqual(
            encoded_images.shape	,   (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            )	,   )









from collections import defaultdict

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					defaultdict[str, int]          =       defaultdict(a					)
      # Creating a dictionary containing count of each word
      for word in sentence.split():
            occurrence[word] += 1
      return occurrence


if __name__ == "__main__":
    for word, count in word_occurrence('INPUT STRING').items():
        print(F'{word}: {count}')






