




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_		:					Optional[int]          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       ["""pixel_values"""]

						def __init__(		self :Optional[int]     ,			__snake_case :bool = True     ,			__snake_case :Union[int, float] = 1 / 2_55     ,			__snake_case :bool = True     ,			__snake_case :int = 8     ,			**__snake_case :int     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										__magic_name__						:						Optional[Any]										=do_rescale
										__magic_name__						:						List[Any]										=rescale_factor
										__magic_name__						:						Dict										=do_pad
										__magic_name__						:						Tuple										=pad_size

						def      A__    (		self :List[str]     ,			__snake_case :np.ndarray     ,			__snake_case :float     ,			__snake_case :Optional[Union[str, ChannelDimension]] = None     ,			**__snake_case :Tuple			):
										'''simple docstring'''

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

						def      A__    (		self :List[Any]     ,			__snake_case :np.ndarray     ,			__snake_case :int     ,			__snake_case :Optional[Union[str, ChannelDimension]] = None			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						Optional[int]										=get_image_size(__snake_case			)
										__magic_name__						:						List[Any]										=(old_height // size + 1) * size - old_height
										__magic_name__						:						Union[str, Any]										=(old_width // size + 1) * size - old_width

										return pad(__snake_case     ,			((0, pad_height), (0, pad_width))     ,			mode="""symmetric"""     ,			data_format=__snake_case			)


						def      A__    (		self :Union[str, Any]     ,			__snake_case :ImageInput     ,			__snake_case :Optional[bool] = None     ,			__snake_case :Optional[float] = None     ,			__snake_case :Optional[bool] = None     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			__snake_case :Union[str, ChannelDimension] = ChannelDimension.FIRST     ,			**__snake_case :Tuple     ,			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__						:						str										=rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__						:						Dict										=do_pad if do_pad is not None else self.do_pad
										__magic_name__						:						Union[str, Any]										=pad_size if pad_size is not None else self.pad_size

										__magic_name__						:						int										=make_list_of_images(__snake_case			)

										if not valid_images(__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_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

										# All transformations expect numpy arrays.
										__magic_name__						:						Optional[int]										=[to_numpy_array(__snake_case			) for image in images]

										if do_rescale:
														__magic_name__						:						Any										=[self.rescale(image=__snake_case     ,			scale=__snake_case			) for image in images]

										if do_pad:
														__magic_name__						:						Optional[Any]										=[self.pad(__snake_case     ,			size=__snake_case			) for image in images]

										__magic_name__						:						Any										=[to_channel_dimension_format(__snake_case     ,			__snake_case			) for image in images]

										__magic_name__						:						Dict										={"""pixel_values""": images}
										return BatchFeature(data=__snake_case     ,			tensor_type=__snake_case			)










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




