


import copy
import re

class 						A__							:
						UpperCAmelCase             =						"hp"
						UpperCAmelCase             =						{}
						UpperCAmelCase             =						None




						@classmethod
						def 							__UpperCamelCase				(       cls					:      Optional[int]				,    _a					:      Optional[int]				,    _a					:      List[str]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =prefix
										_SCREAMING_SNAKE_CASE			  =defaults
										cls.build_naming_info()




						@staticmethod
						def 							__UpperCamelCase				(       _a					:      Optional[Any]				,    _a					:      List[str]  )							->     Optional[int]:


										"""simple docstring"""





										if len(_a  ) == 0:
														return ""
										_SCREAMING_SNAKE_CASE			  =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(_a  ) + 1  ):
														_SCREAMING_SNAKE_CASE			  =word[:prefix_len]
														if prefix in info["reverse_short_word"]:
																		continue
														else:
																		_SCREAMING_SNAKE_CASE			  =prefix
																		break

										if short_word is None:
														# Paranoid fallback
														def int_to_alphabetic(_a					:      Optional[Any]  ):
																		_SCREAMING_SNAKE_CASE			  =''''''
																		while integer != 0:
																						_SCREAMING_SNAKE_CASE			  =chr(ord('''A'''  ) + integer % 10  ) + s
																						integer //= 10
																		return s

														_SCREAMING_SNAKE_CASE			  =0
														while True:
																		_SCREAMING_SNAKE_CASE			  =word + '''#''' + int_to_alphabetic(_a  )
																		if sword in info["reverse_short_word"]:
																						continue
																		else:
																						_SCREAMING_SNAKE_CASE			  =sword
																						break

										_SCREAMING_SNAKE_CASE			  =short_word
										_SCREAMING_SNAKE_CASE			  =word
										return short_word




						@staticmethod
						def 							__UpperCamelCase				(       _a					:      Optional[Any]				,    _a					:      Union[str, Any]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =param_name.split('''_'''  )

										_SCREAMING_SNAKE_CASE			  =[TrialShortNamer.shortname_for_word(_a				,    _a  ) 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
										_SCREAMING_SNAKE_CASE			  =['''''', '''_''']

										for separator in separators:
														_SCREAMING_SNAKE_CASE			  =separator.join(_a  )
														if shortname not in info["reverse_short_param"]:
																		_SCREAMING_SNAKE_CASE			  =shortname
																		_SCREAMING_SNAKE_CASE			  =param_name
																		return shortname

										return param_name




						@staticmethod
						def 							__UpperCamelCase				(       _a					:      Optional[Any]				,    _a					:      Optional[int]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =TrialShortNamer.shortname_for_key(_a				,    _a  )
										_SCREAMING_SNAKE_CASE			  =short_name
										_SCREAMING_SNAKE_CASE			  =param_name




						@classmethod
						def 							__UpperCamelCase				(       cls					:      Dict  )							->     Union[str, Any]:


										"""simple docstring"""





										if cls.NAMING_INFO is not None:
														return

										_SCREAMING_SNAKE_CASE			  ={
										    '''short_word''': {},
										    '''reverse_short_word''': {},
										    '''short_param''': {},
										    '''reverse_short_param''': {},
										}

										_SCREAMING_SNAKE_CASE			  =list(cls.DEFAULTS.keys()  )

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

										_SCREAMING_SNAKE_CASE			  =info




						@classmethod
						def 							__UpperCamelCase				(       cls					:      Dict				,    _a					:      Optional[int]  )							->     str:


										"""simple docstring"""





										cls.build_naming_info()
										assert cls.PREFIX is not None
										_SCREAMING_SNAKE_CASE			  =[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

														_SCREAMING_SNAKE_CASE			  =cls.NAMING_INFO['''short_param'''][k]

														if isinstance(_a				,    _a  ):
																		_SCREAMING_SNAKE_CASE			  =1 if v else 0

														_SCREAMING_SNAKE_CASE			  ='''''' if isinstance(_a				,    (int, float)  ) else '''-'''
														_SCREAMING_SNAKE_CASE			  =f"{key}{sep}{v}"
														name.append(_a  )

										return "_".join(_a  )




						@classmethod
						def 							__UpperCamelCase				(       cls					:      Optional[Any]				,    _a					:      Dict  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =repr[len(cls.PREFIX  ) + 1 :]
										if repr == "":
														_SCREAMING_SNAKE_CASE			  =[]
										else:
														_SCREAMING_SNAKE_CASE			  =repr.split('''_'''  )

										_SCREAMING_SNAKE_CASE			  ={}

										for value in values:
														if "-" in value:
																		_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =value.split('''-'''  )
														else:
																		_SCREAMING_SNAKE_CASE			  =re.sub('''[0-9.]'''				,    ''''''				,    _a  )
																		_SCREAMING_SNAKE_CASE			  =float(re.sub('''[^0-9.]'''				,    ''''''				,    _a  )  )

														_SCREAMING_SNAKE_CASE			  =cls.NAMING_INFO['''reverse_short_param'''][p_k]

														_SCREAMING_SNAKE_CASE			  =p_v

										for k in cls.DEFAULTS:
														if k not in parameters:
																		_SCREAMING_SNAKE_CASE			  =cls.DEFAULTS[k]

										return parameters



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

snake_case_						:			Union[str, Any]													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__):
				if isinstance(a__	,(list, tuple)) and isinstance(videos[0]	,(list, tuple)) and is_valid_image(videos[0][0]):
								return videos

				elif isinstance(a__	,(list, tuple)) and is_valid_image(videos[0]):
								return [videos]

				elif is_valid_image(a__):
								return [[videos]]

				raise ValueError(f"Could not make batched video from {videos}")


class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["pixel_values"]




						def __init__(       self					:      Tuple				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      bool = True				,    _a					:      Union[int, float] = 1 / 255				,    _a					:      bool = True				,    _a					:      bool = True				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    **_a					:      Any				,    )							->     None:


										"""simple docstring"""





										super().__init__(**_a  )
										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''shortest_edge''': 256}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										_SCREAMING_SNAKE_CASE			  =do_resize
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_center_crop
										_SCREAMING_SNAKE_CASE			  =crop_size
										_SCREAMING_SNAKE_CASE			  =resample
										_SCREAMING_SNAKE_CASE			  =do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_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 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[Any]				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										if "shortest_edge" in size:
														_SCREAMING_SNAKE_CASE			  =get_resize_output_image_size(_a				,    size['''shortest_edge''']				,    default_to_square=_a  )
										elif "height" in size and "width" in size:
														_SCREAMING_SNAKE_CASE			  =(size['''height'''], size['''width'''])
										else:
														raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
										return resize(_a				,    size=_a				,    resample=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Dict				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a  )
										if "height" not in size or "width" not in size:
														raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
										return center_crop(_a				,    size=(size['''height'''], size['''width'''])				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      np.ndarray				,    _a					:      Union[int, float]				,    _a					:      bool = True				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[str]				,    )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =image.astype(np.floataa  )
										if offset:
														_SCREAMING_SNAKE_CASE			  =image - (scale / 2)
										return rescale(_a				,    scale=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      np.ndarray				,    _a					:      Union[float, List[float]]				,    _a					:      Union[float, List[float]]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Any				,    )							->     np.ndarray:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[ChannelDimension] = ChannelDimension.FIRST				,    )							->     np.ndarray:


										"""simple docstring"""





										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.'''  )

										if offset and not do_rescale:
														raise ValueError('''For offset, do_rescale must also be set to True.'''  )

										# All transformations expect numpy arrays.
										_SCREAMING_SNAKE_CASE			  =to_numpy_array(_a  )

										if do_resize:
														_SCREAMING_SNAKE_CASE			  =self.resize(image=_a				,    size=_a				,    resample=_a  )

										if do_center_crop:
														_SCREAMING_SNAKE_CASE			  =self.center_crop(_a				,    size=_a  )

										if do_rescale:
														_SCREAMING_SNAKE_CASE			  =self.rescale(image=_a				,    scale=_a				,    offset=_a  )

										if do_normalize:
														_SCREAMING_SNAKE_CASE			  =self.normalize(image=_a				,    mean=_a				,    std=_a  )

										_SCREAMING_SNAKE_CASE			  =to_channel_dimension_format(_a				,    _a  )
										return image




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[str, TensorType]] = None				,    _a					:      ChannelDimension = ChannelDimension.FIRST				,    **_a					:      str				,    )							->     PIL.Image.Image:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE			  =resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE			  =do_center_crop if do_center_crop is not None else self.do_center_crop
										_SCREAMING_SNAKE_CASE			  =do_rescale if do_rescale is not None else self.do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor if rescale_factor is not None else self.rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset if offset is not None else self.offset
										_SCREAMING_SNAKE_CASE			  =do_normalize if do_normalize is not None else self.do_normalize
										_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(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else self.crop_size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

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

										_SCREAMING_SNAKE_CASE			  =make_batched(_a  )

										_SCREAMING_SNAKE_CASE			  =[
										    [
										        self._preprocess_image(
										            image=_a				,    do_resize=_a				,    size=_a				,    resample=_a				,    do_center_crop=_a				,    crop_size=_a				,    do_rescale=_a				,    rescale_factor=_a				,    offset=_a				,    do_normalize=_a				,    image_mean=_a				,    image_std=_a				,    data_format=_a				,    )
										        for img in video
										    ]
										    for video in videos
										]

										_SCREAMING_SNAKE_CASE			  ={'''pixel_values''': videos}
										return BatchFeature(data=_a				,    tensor_type=_a  )