



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__	:  int 		=logging.get_logger(__name__)

class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						UpperCamelCase__    :				Tuple       						=						['''pixel_values''']






						def __init__(					self   ,							_A = True   ,							_A = None   ,							_A = PILImageResampling.BILINEAR   ,							_A = True   ,							_A = 1 / 255   ,							_A = True   ,							_A = None   ,							_A = None   ,							**_A   ,							):







													'''simple docstring'''

													super().__init__(**__UpperCamelCase	)
													__SCREAMING_SNAKE_CASE								=      size if size is not None else {'''height''': 224, '''width''': 224}
													__SCREAMING_SNAKE_CASE								=      get_size_dict(__UpperCamelCase	)
													__SCREAMING_SNAKE_CASE								=      do_resize
													__SCREAMING_SNAKE_CASE								=      do_rescale
													__SCREAMING_SNAKE_CASE								=      do_normalize
													__SCREAMING_SNAKE_CASE								=      size
													__SCREAMING_SNAKE_CASE								=      resample
													__SCREAMING_SNAKE_CASE								=      rescale_factor
													__SCREAMING_SNAKE_CASE								=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__SCREAMING_SNAKE_CASE								=      image_std if image_std is not None else IMAGENET_STANDARD_STD






						def  _A			(					self   ,							_A   ,							_A   ,							_A = PILImageResampling.BILINEAR   ,							_A = None   ,							**_A   ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      get_size_dict(__UpperCamelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""	)
													return resize(
													    __UpperCamelCase   ,							size=(size['height'], size['width'])   ,							resample=__UpperCamelCase   ,							data_format=__UpperCamelCase   ,							**__UpperCamelCase	)






						def  _A			(					self   ,							_A   ,							_A   ,							_A = None   ,							**_A	):







													'''simple docstring'''

													return rescale(__UpperCamelCase   ,							scale=__UpperCamelCase   ,							data_format=__UpperCamelCase   ,							**__UpperCamelCase	)






						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A = None   ,							**_A   ,							):







													'''simple docstring'''

													return normalize(__UpperCamelCase   ,							mean=__UpperCamelCase   ,							std=__UpperCamelCase   ,							data_format=__UpperCamelCase   ,							**__UpperCamelCase	)






						def  _A			(					self   ,							_A   ,							_A = None   ,							_A = None   ,							_A = None   ,							_A = None   ,							_A = None   ,							_A = None   ,							_A = None   ,							_A = None   ,							_A = None   ,							_A = ChannelDimension.FIRST   ,							**_A   ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      do_resize if do_resize is not None else self.do_resize
													__SCREAMING_SNAKE_CASE								=      do_rescale if do_rescale is not None else self.do_rescale
													__SCREAMING_SNAKE_CASE								=      do_normalize if do_normalize is not None else self.do_normalize
													__SCREAMING_SNAKE_CASE								=      resample if resample is not None else self.resample
													__SCREAMING_SNAKE_CASE								=      rescale_factor if rescale_factor is not None else self.rescale_factor
													__SCREAMING_SNAKE_CASE								=      image_mean if image_mean is not None else self.image_mean
													__SCREAMING_SNAKE_CASE								=      image_std if image_std is not None else self.image_std

													__SCREAMING_SNAKE_CASE								=      size if size is not None else self.size
													__SCREAMING_SNAKE_CASE								=      get_size_dict(__UpperCamelCase	)

													__SCREAMING_SNAKE_CASE								=      make_list_of_images(__UpperCamelCase	)

													if not valid_images(__UpperCamelCase	):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'	)

													if do_resize and size is None:
																				raise ValueError('Size must be specified if do_resize is True.'	)

													if do_rescale and rescale_factor is None:
																				raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

													# All transformations expect numpy arrays.
													__SCREAMING_SNAKE_CASE								=      [to_numpy_array(__UpperCamelCase	) for image in images]

													if do_resize:
																				__SCREAMING_SNAKE_CASE								=      [self.resize(image=__UpperCamelCase   ,							size=__UpperCamelCase   ,							resample=__UpperCamelCase	) for image in images]

													if do_rescale:
																				__SCREAMING_SNAKE_CASE								=      [self.rescale(image=__UpperCamelCase   ,							scale=__UpperCamelCase	) for image in images]

													if do_normalize:
																				__SCREAMING_SNAKE_CASE								=      [self.normalize(image=__UpperCamelCase   ,							mean=__UpperCamelCase   ,							std=__UpperCamelCase	) for image in images]

													__SCREAMING_SNAKE_CASE								=      [to_channel_dimension_format(__UpperCamelCase   ,							__UpperCamelCase	) for image in images]

													__SCREAMING_SNAKE_CASE								=      {'''pixel_values''': images}
													return BatchFeature(data=__UpperCamelCase   ,							tensor_type=__UpperCamelCase	)











import unittest

from cer import CER


lowerCAmelCase__	:  Dict 		=CER()

class      UpperCAmelCase_						(     unittest.TestCase ):

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['White House']
													__SCREAMING_SNAKE_CASE								=      ['white house']
													# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['were wolf']
													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['weae     wolf']
													# S = 1, D = 1, I = 0, N = 8, CER = 0.25
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.2_5	) < 1e-6	)

													# consecutive whitespaces case 1
													__SCREAMING_SNAKE_CASE								=      ['were wolf']
													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
													# S = 0, D = 0, I = 0, N = 9, CER = 0
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)

													# consecutive whitespaces case 2
													__SCREAMING_SNAKE_CASE								=      ['were   wolf']
													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
													# S = 0, D = 0, I = 0, N = 9, CER = 0
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['weaewolf']
													# S = 1, D = 0, I = 0, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['wereawolf']
													# S = 0, D = 1, I = 0, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['wereolf']
													# S = 0, D = 0, I = 1, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertEqual(_A   ,							0.0	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertEqual(_A   ,							0.0	)

													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father', 'doge']
													__SCREAMING_SNAKE_CASE								=      ['werxwolf', 'I       am your father', 'doge']
													# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['My hovercraft', 'is full of eels']
													__SCREAMING_SNAKE_CASE								=      ['My hovercraft is full', ' of eels']
													# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
													# whitespace at the front of " of eels" will be strip during preporcessing
													# so need to insert 2 whitespaces
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A   ,							concatenate_texts=_A	)
													self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
													__SCREAMING_SNAKE_CASE								=      [' 能吞虾玻璃而 不霜身体啦']
													# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃', '而不伤身体']
													__SCREAMING_SNAKE_CASE								=      ['我    能 吞 下 玻 璃', '而不伤身体']
													# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertFalse(_A   ,							0.0	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['']
													__SCREAMING_SNAKE_CASE								=      ['Hypothesis']
													with self.assertRaises(_A	):
																				cer.compute(predictions=_A   ,							references=_A	)


if __name__ == "__main__":
			unittest.main()






