





"""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,
    get_resize_output_image_size,
    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


A 			=  logging.get_logger(__name__)




class 				__lowercase			(			_A							):







					'''simple docstring'''




					__lowerCAmelCase  =  ['''pixel_values''']







					def __init__(						self	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = PILImageResampling.BILINEAR	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = True	,			_UpperCAmelCase = 1 / 255	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									super().__init__(**UpperCamelCase__     )
									__a :						List[Any]			       =  size if size is not None else {'''shortest_edge''': 256}
									__a :						str			       =  get_size_dict(UpperCamelCase__	,			default_to_square=UpperCamelCase__     )
									__a :						Any			       =  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									__a :						List[str]			       =  get_size_dict(UpperCamelCase__     )
									__a :						Union[str, Any]			       =  do_resize
									__a :						Optional[Any]			       =  size
									__a :						Tuple			       =  resample
									__a :						List[Any]			       =  do_center_crop
									__a :						Optional[int]			       =  crop_size
									__a :						Optional[Any]			       =  do_rescale
									__a :						List[str]			       =  rescale_factor
									__a :						Optional[Any]			       =  do_normalize
									__a :						Optional[int]			       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__a :						Any			       =  image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = PILImageResampling.BICUBIC	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						Optional[int]			       =  get_size_dict(UpperCamelCase__	,			default_to_square=UpperCamelCase__     )
									if "shortest_edge" not in size:
													raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
									__a :						Tuple			       =  get_resize_output_image_size(UpperCamelCase__	,			size=size['''shortest_edge''']	,			default_to_square=UpperCamelCase__     )
									return resize(UpperCamelCase__	,			size=UpperCamelCase__	,			resample=UpperCamelCase__	,			data_format=UpperCamelCase__	,			**UpperCamelCase__     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						str			       =  get_size_dict(UpperCamelCase__     )
									return center_crop(UpperCamelCase__	,			size=(size['''height'''], size['''width'''])	,			data_format=UpperCamelCase__	,			**UpperCamelCase__     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase     ):
									return rescale(UpperCamelCase__	,			scale=UpperCamelCase__	,			data_format=UpperCamelCase__	,			**UpperCamelCase__     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									return normalize(UpperCamelCase__	,			mean=UpperCamelCase__	,			std=UpperCamelCase__	,			data_format=UpperCamelCase__	,			**UpperCamelCase__     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = ChannelDimension.FIRST	,			**_UpperCAmelCase	,			):
									__a :						str			       =  do_resize if do_resize is not None else self.do_resize
									__a :						Optional[int]			       =  size if size is not None else self.size
									__a :						Dict			       =  get_size_dict(UpperCamelCase__	,			default_to_square=UpperCamelCase__     )
									__a :						Any			       =  resample if resample is not None else self.resample
									__a :						str			       =  do_center_crop if do_center_crop is not None else self.do_center_crop
									__a :						Union[str, Any]			       =  crop_size if crop_size is not None else self.crop_size
									__a :						List[str]			       =  get_size_dict(UpperCamelCase__     )
									__a :						Optional[int]			       =  do_rescale if do_rescale is not None else self.do_rescale
									__a :						List[str]			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
									__a :						Union[str, Any]			       =  do_normalize if do_normalize is not None else self.do_normalize
									__a :						Optional[int]			       =  image_mean if image_mean is not None else self.image_mean
									__a :						Union[str, Any]			       =  image_std if image_std is not None else self.image_std

									__a :						List[str]			       =  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_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 :						Any			       =  [to_numpy_array(UpperCamelCase__     ) for image in images]

									if do_resize:
													__a :						Optional[Any]			       =  [self.resize(image=UpperCamelCase__	,			size=UpperCamelCase__	,			resample=UpperCamelCase__     ) for image in images]

									if do_center_crop:
													__a :						int			       =  [self.center_crop(image=UpperCamelCase__	,			size=UpperCamelCase__     ) for image in images]

									if do_rescale:
													__a :						Union[str, Any]			       =  [self.rescale(image=UpperCamelCase__	,			scale=UpperCamelCase__     ) for image in images]

									if do_normalize:
													__a :						Tuple			       =  [self.normalize(image=UpperCamelCase__	,			mean=UpperCamelCase__	,			std=UpperCamelCase__     ) for image in images]

									__a :						str			       =  [to_channel_dimension_format(UpperCamelCase__	,			UpperCamelCase__     ) for image in images]

									__a :						Tuple			       =  {'''pixel_values''': images}
									return BatchFeature(data=UpperCamelCase__	,			tensor_type=UpperCamelCase__     )










"""simple docstring"""






import copy
import re




class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  '''hp'''
					__lowerCAmelCase  =  {}
					__lowerCAmelCase  =  None







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  prefix
									__a :						List[str]			       =  defaults
									cls.build_naming_info()







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									if len(_UpperCAmelCase     ) == 0:
													return ""
									__a :						Optional[int]			       =  None
									if any(char.isdigit() for char in word     ):
													raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number"""     )
									if word in info["short_word"]:
													return info["short_word"][word]
									for prefix_len in range(1	,			len(_UpperCAmelCase     ) + 1     ):
													__a :						str			       =  word[:prefix_len]
													if prefix in info["reverse_short_word"]:
																	continue
													else:
																	__a :						Tuple			       =  prefix
																	break

									if short_word is None:
													# Paranoid fallback
													def int_to_alphabetic(_UpperCAmelCase     ):
																	__a :						List[str]			       =  ''''''
																	while integer != 0:
																					__a :						Union[str, Any]			       =  chr(ord('''A'''     ) + integer % 10     ) + s
																					integer //= 10
																	return s

													__a :						Optional[int]			       =  0
													while True:
																	__a :						List[str]			       =  word + '''#''' + int_to_alphabetic(_UpperCAmelCase     )
																	if sword in info["reverse_short_word"]:
																					continue
																	else:
																					__a :						Optional[Any]			       =  sword
																					break

									__a :						List[str]			       =  short_word
									__a :						Dict			       =  word
									return short_word







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  param_name.split('''_'''     )

									__a :						str			       =  [TrialShortNamer.shortname_for_word(_UpperCAmelCase	,			_UpperCAmelCase     ) for word in words]

									# We try to create a separatorless short name, but if there is a collision we have to fallback
									# to a separated short name
									__a :						Dict			       =  ['''''', '''_''']

									for separator in separators:
													__a :						Union[str, Any]			       =  separator.join(_UpperCAmelCase     )
													if shortname not in info["reverse_short_param"]:
																	__a :						List[str]			       =  shortname
																	__a :						Union[str, Any]			       =  param_name
																	return shortname

									return param_name







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						int			       =  TrialShortNamer.shortname_for_key(_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  short_name
									__a :						Optional[Any]			       =  param_name







					@classmethod
					def  _lowerCamelCase		(						cls     ):
									if cls.NAMING_INFO is not None:
													return

									__a :						Optional[Any]			       =  {
									    '''short_word''': {},
									    '''reverse_short_word''': {},
									    '''short_param''': {},
									    '''reverse_short_param''': {},
									}

									__a :						List[str]			       =  list(cls.DEFAULTS.keys()     )

									for k in field_keys:
													cls.add_new_param_name(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						Optional[Any]			       =  info







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									cls.build_naming_info()
									assert cls.PREFIX is not None
									__a :						str			       =  [copy.copy(cls.PREFIX     )]

									for k, v in params.items():
													if k not in cls.DEFAULTS:
																	raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""     )
													if v == cls.DEFAULTS[k]:
																	# The default value is not added to the name
																	continue

													__a :						Union[str, Any]			       =  cls.NAMING_INFO['''short_param'''][k]

													if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						str			       =  1 if v else 0

													__a :						Tuple			       =  '''''' if isinstance(_UpperCAmelCase	,			(int, float)     ) else '''-'''
													__a :						Dict			       =  f"""{key}{sep}{v}"""
													name.append(_UpperCAmelCase     )

									return "_".join(_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  repr[len(cls.PREFIX     ) + 1 :]
									if repr == "":
													__a :						Tuple			       =  []
									else:
													__a :						str			       =  repr.split('''_'''     )

									__a :						Optional[Any]			       =  {}

									for value in values:
													if "-" in value:
																	__a       ,						__a :						List[Any]			       =  value.split('''-'''     )
													else:
																	__a :						int			       =  re.sub('''[0-9.]'''	,			''''''	,			_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  float(re.sub('''[^0-9.]'''	,			''''''	,			_UpperCAmelCase     )     )

													__a :						Dict			       =  cls.NAMING_INFO['''reverse_short_param'''][p_k]

													__a :						Union[str, Any]			       =  p_v

									for k in cls.DEFAULTS:
													if k not in parameters:
																	__a :						Optional[int]			       =  cls.DEFAULTS[k]

									return parameters