

"""simple docstring"""




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 center_crop, 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, is_vision_available, logging


if is_vision_available():
	import PIL


__A     :    Optional[Any]    =		logging.get_logger(__name__)





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    ["pixel_values"]


		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		PILImageResampling = PIL.Image.NEAREST     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		Union[int, float] = 1 / 2_5_5     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					size if size is not None else {"height": 3_4_6, "width": 3_4_6}
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							A			=					crop_size if crop_size is not None else {"height": 2_8_9, "width": 2_8_9}
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE     ,     param_name="crop_size")

							A			=					do_resize
							A			=					size
							A			=					resample
							A			=					do_center_crop
							A			=					crop_size
							A			=					do_rescale
							A			=					rescale_factor
							A			=					rescale_offset
							A			=					do_normalize
							A			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A			=					image_std if image_std is not None else IMAGENET_STANDARD_STD
							A			=					include_top


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int]     ,     __SCREAMING_SNAKE_CASE :		PILImageResampling = PIL.Image.NEAREST     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		List[str]     ,     ):
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							if "height" not in size or "width" not in size:
												raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
							return resize(
							    __SCREAMING_SNAKE_CASE     ,     size=(size["height"], size["width"])     ,     resample=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							if "height" not in size or "width" not in size:
												raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
							return center_crop(__SCREAMING_SNAKE_CASE     ,     size=(size["height"], size["width"])     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Any     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Union[int, float]     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		str     ,     ):
							if offset:
												A			=					(image - 1_2_7.5) * scale
												if data_format is not None:
																	A			=					to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
												A			=					rescaled_image.astype(np.floataa)
							else:
												A			=					rescale(__SCREAMING_SNAKE_CASE     ,     scale=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)
							return rescaled_image


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Union[float, List[float]]     ,     __SCREAMING_SNAKE_CASE :		Union[float, List[float]]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Dict     ,     ):
							return normalize(__SCREAMING_SNAKE_CASE     ,     mean=__SCREAMING_SNAKE_CASE     ,     std=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		ImageInput     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		int=None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		float = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, TensorType]] = None     ,     __SCREAMING_SNAKE_CASE :		ChannelDimension = ChannelDimension.FIRST     ,     **__SCREAMING_SNAKE_CASE :		Dict     ,     ):
							A			=					do_resize if do_resize is not None else self.do_resize
							A			=					resample if resample is not None else self.resample
							A			=					do_center_crop if do_center_crop is not None else self.do_center_crop
							A			=					do_rescale if do_rescale is not None else self.do_rescale
							A			=					rescale_factor if rescale_factor is not None else self.rescale_factor
							A			=					rescale_offset if rescale_offset is not None else self.rescale_offset
							A			=					do_normalize if do_normalize is not None else self.do_normalize
							A			=					image_mean if image_mean is not None else self.image_mean
							A			=					image_std if image_std is not None else self.image_std
							A			=					include_top if include_top is not None else self.include_top

							A			=					size if size is not None else self.size
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							A			=					crop_size if crop_size is not None else self.crop_size
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE     ,     param_name="crop_size")

							A			=					make_list_of_images(__SCREAMING_SNAKE_CASE)

							if not valid_images(__SCREAMING_SNAKE_CASE):
												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 or resample is None:
												raise ValueError("Size and resample must be specified if do_resize is True.")

							if do_center_crop and crop_size is None:
												raise ValueError("Crop size must be specified if do_center_crop is True.")

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

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("Image mean and std must be specified if do_normalize is True.")

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

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

							if do_center_crop:
												A			=					[self.center_crop(image=__SCREAMING_SNAKE_CASE     ,     size=__SCREAMING_SNAKE_CASE) for image in images]

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

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

							if include_top:
												A			=					[self.normalize(image=__SCREAMING_SNAKE_CASE     ,     mean=[0, 0, 0]     ,     std=__SCREAMING_SNAKE_CASE) for image in images]

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

							A			=					{"pixel_values": images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,     tensor_type=__SCREAMING_SNAKE_CASE)













"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

     for letter in word:
          if letter not in letter_nums:
               UpperCAmelCase__		:  Tuple				=				str(snake_case							)
               next_num += 1
          word_pattern.append(letter_nums[letter]							)
     return ".".join(snake_case							)


if __name__ == "__main__":
 import pprint
 import time

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

 _lowerCAmelCase     :     dict            =	{}
 for word in word_list:
  _lowerCAmelCase     :     int            =	get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _lowerCAmelCase     :     Any            =	[word]

 with open("""word_patterns.txt""", """w""") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _lowerCAmelCase     :     Union[str, Any]            =	round(time.time() - start_time, 2)
 print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.


