
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 ViTImageProcessor





class 							A__		(     unittest.TestCase):
						def __init__(				self   ,  __magic_name__   ,  __magic_name__=7   ,  __magic_name__=3   ,  __magic_name__=1_8   ,  __magic_name__=3_0   ,  __magic_name__=4_0_0   ,  __magic_name__=True   ,  __magic_name__=None   ,  __magic_name__=True   ,  __magic_name__=[0.5, 0.5, 0.5]   ,  __magic_name__=[0.5, 0.5, 0.5]   ,  ):
							lowerCamelCase		:     int          =				size if size is not None else {"""height""": 1_8, """width""": 1_8}
							lowerCamelCase		:     List[str]          =				parent
							lowerCamelCase		:     int          =				batch_size
							lowerCamelCase		:     Any          =				num_channels
							lowerCamelCase		:     Any          =				image_size
							lowerCamelCase		:     Optional[int]          =				min_resolution
							lowerCamelCase		:     Optional[int]          =				max_resolution
							lowerCamelCase		:     Optional[Any]          =				do_resize
							lowerCamelCase		:     Union[str, Any]          =				size
							lowerCamelCase		:     Optional[int]          =				do_normalize
							lowerCamelCase		:     int          =				image_mean
							lowerCamelCase		:     List[Any]          =				image_std






						def 						UpperCamelCase__							(				self				):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}





@require_torch
@require_vision
class 							A__		(     a_  ,       unittest.TestCase):
						_UpperCAmelCase    :   Tuple   	=      ViTImageProcessor if is_vision_available() else None
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Optional[int]          =				ViTImageProcessingTester(self				)
						@property
						def 						UpperCamelCase__							(				self				):
							return self.image_processor_tester.prepare_image_processor_dict()
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Optional[Any]          =				self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(snake_case__   ,  """image_mean"""				)				)
							self.assertTrue(hasattr(snake_case__   ,  """image_std"""				)				)
							self.assertTrue(hasattr(snake_case__   ,  """do_normalize"""				)				)
							self.assertTrue(hasattr(snake_case__   ,  """do_resize"""				)				)
							self.assertTrue(hasattr(snake_case__   ,  """size"""				)				)
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Dict          =				self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size   ,  {"""height""": 1_8, """width""": 1_8}				)

							lowerCamelCase		:     Optional[Any]          =				self.image_processing_class.from_dict(self.image_processor_dict   ,  size=4_2				)
							self.assertEqual(image_processor.size   ,  {"""height""": 4_2, """width""": 4_2}				)
						def 						UpperCamelCase__							(				self				):
							pass
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Any          =				self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCamelCase		:     Tuple          =				prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=snake_case__				)
							for image in image_inputs:
								self.assertIsInstance(snake_case__   ,  Image.Image				)

							# Test not batched input
							lowerCamelCase		:     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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )   ,  )

							# Test batched
							lowerCamelCase		:     List[Any]          =				image_processing(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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )   ,  )
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[str]          =				self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCamelCase		:     int          =				prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=snake_case__   ,  numpify=snake_case__				)
							for image in image_inputs:
								self.assertIsInstance(snake_case__   ,  np.ndarray				)

							# Test not batched input
							lowerCamelCase		:     Optional[int]          =				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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )   ,  )

							# Test batched
							lowerCamelCase		:     List[str]          =				image_processing(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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )   ,  )






						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Tuple          =				self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCamelCase		:     int          =				prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=snake_case__   ,  torchify=snake_case__				)
							for image in image_inputs:
								self.assertIsInstance(snake_case__   ,  torch.Tensor				)

							# Test not batched input
							lowerCamelCase		:     Union[str, 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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )   ,  )

							# Test batched
							lowerCamelCase		:     Any          =				image_processing(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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )   ,  )






from collections import defaultdict





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     defaultdict[str, int]          =				defaultdict(lowerCamelCase							)
 # 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}''')




