
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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_  :	List[Any] 				=					logging.get_logger(__name__)


if is_vision_available():
							import PIL

class     _UpperCamelCase			(   _snake_case					):






			'''simple docstring'''

			__UpperCamelCase :  Any    =	["""pixel_values"""]

			def __init__(    self	:    Tuple  ,		snake_case_	:    int = True  ,		snake_case_	:    int = None  ,		snake_case_	:    Optional[int] = PILImageResampling.BICUBIC  ,		snake_case_	:    Optional[Any] = True  ,		snake_case_	:    int = None  ,		snake_case_	:    str = True  ,		snake_case_	:    int = 1 / 255  ,		snake_case_	:    Union[str, Any] = True  ,		snake_case_	:    int = None  ,		snake_case_	:    Dict = None  ,		snake_case_	:    Any = True  ,		**snake_case_	:    Dict  ,		):
							super().__init__(**UpperCamelCase__ )
							UpperCamelCase_:							Tuple		=  size if size is not None else {"""shortest_edge""": 224}
							UpperCamelCase_:							Optional[int]		=  get_size_dict(UpperCamelCase__  ,		default_to_square=UpperCamelCase__ )
							UpperCamelCase_:							Any		=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							UpperCamelCase_:							Any		=  get_size_dict(UpperCamelCase__  ,		default_to_square=UpperCamelCase__  ,		param_name="""crop_size""" )

							UpperCamelCase_:							Tuple		=  do_resize
							UpperCamelCase_:							Union[str, Any]		=  size
							UpperCamelCase_:							Dict		=  resample
							UpperCamelCase_:							Optional[Any]		=  do_center_crop
							UpperCamelCase_:							Any		=  crop_size
							UpperCamelCase_:							List[Any]		=  do_rescale
							UpperCamelCase_:							Optional[int]		=  rescale_factor
							UpperCamelCase_:							str		=  do_normalize
							UpperCamelCase_:							Optional[Any]		=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							UpperCamelCase_:							Optional[Any]		=  image_std if image_std is not None else OPENAI_CLIP_STD
							UpperCamelCase_:							Union[str, Any]		=  do_convert_rgb

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    str  ,		snake_case_	:    List[str]  ,		snake_case_	:    Optional[Any] = PILImageResampling.BICUBIC  ,		snake_case_	:    Tuple = None  ,		**snake_case_	:    Any  ,		):
							UpperCamelCase_:							List[Any]		=  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()}''' )
							UpperCamelCase_:							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	:    Optional[Any]  ,		snake_case_	:    List[Any]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    List[str] = None  ,		**snake_case_	:    str  ,		):
							UpperCamelCase_:							Union[str, Any]		=  get_size_dict(UpperCamelCase__ )
							if "height" not in size or "width" not in size:
											raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
							return center_crop(UpperCamelCase__  ,		size=(size["""height"""], size["""width"""])  ,		data_format=UpperCamelCase__  ,		**UpperCamelCase__ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    List[str]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Any = None  ,		**snake_case_	:    int  ,		):
							return rescale(UpperCamelCase__  ,		scale=UpperCamelCase__  ,		data_format=UpperCamelCase__  ,		**UpperCamelCase__ )

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    int  ,		snake_case_	:    int  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    Union[str, Any] = None  ,		**snake_case_	:    str  ,		):
							return normalize(UpperCamelCase__  ,		mean=UpperCamelCase__  ,		std=UpperCamelCase__  ,		data_format=UpperCamelCase__  ,		**UpperCamelCase__ )




			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Tuple  ,		snake_case_	:    Union[str, Any] = None  ,		snake_case_	:    List[Any] = None  ,		snake_case_	:    Dict = None  ,		snake_case_	:    List[str] = None  ,		snake_case_	:    Union[str, Any] = None  ,		snake_case_	:    Tuple = None  ,		snake_case_	:    Optional[Any] = None  ,		snake_case_	:    List[str] = None  ,		snake_case_	:    List[Any] = None  ,		snake_case_	:    Union[str, Any] = None  ,		snake_case_	:    List[str] = None  ,		snake_case_	:    int = None  ,		snake_case_	:    str = ChannelDimension.FIRST  ,		**snake_case_	:    int  ,		):
							UpperCamelCase_:							List[str]		=  do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_:							Optional[int]		=  size if size is not None else self.size
							UpperCamelCase_:							str		=  get_size_dict(UpperCamelCase__  ,		param_name="""size"""  ,		default_to_square=UpperCamelCase__ )
							UpperCamelCase_:							Dict		=  resample if resample is not None else self.resample
							UpperCamelCase_:							Optional[Any]		=  do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase_:							Any		=  crop_size if crop_size is not None else self.crop_size
							UpperCamelCase_:							Any		=  get_size_dict(UpperCamelCase__  ,		param_name="""crop_size"""  ,		default_to_square=UpperCamelCase__ )
							UpperCamelCase_:							Optional[Any]		=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase_:							Dict		=  rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase_:							int		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							str		=  image_mean if image_mean is not None else self.image_mean
							UpperCamelCase_:							str		=  image_std if image_std is not None else self.image_std
							UpperCamelCase_:							List[str]		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							UpperCamelCase_:							Optional[int]		=  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.""" )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
											UpperCamelCase_:							Dict		=  [convert_to_rgb(UpperCamelCase__ ) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase_:							str		=  [to_numpy_array(UpperCamelCase__ ) for image in images]

							if do_resize:
											UpperCamelCase_:							str		=  [self.resize(image=UpperCamelCase__  ,		size=UpperCamelCase__  ,		resample=UpperCamelCase__ ) for image in images]

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

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

							if do_normalize:
											UpperCamelCase_:							List[str]		=  [self.normalize(image=UpperCamelCase__  ,		mean=UpperCamelCase__  ,		std=UpperCamelCase__ ) for image in images]

							UpperCamelCase_:							Any		=  [to_channel_dimension_format(UpperCamelCase__  ,		UpperCamelCase__ ) for image in images]

							UpperCamelCase_:							Optional[Any]		=  {"""pixel_values""": images}
							return BatchFeature(data=UpperCamelCase__  ,		tensor_type=UpperCamelCase__ )








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_  :	Dict 				=					{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							lowerCamelCase_  :	Any 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






