






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 convert_to_rgb, 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


if is_vision_available():
	import PIL


lowercase    				=   logging.get_logger(__name__)




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					['''pixel_values''']




	def __init__(			self			,     a = True			,     a = None			,     a = PILImageResampling.BICUBIC			,     a = True			,     a = 1 / 2_55			,     a = True			,     a = None			,     a = None			,     a = True			,     **a			,     )			->       None:
								super().__init__(**a   )
								snake_case_           =				size if size is not None else {'height': 3_84, 'width': 3_84}
								snake_case_           =				get_size_dict(a			,     default_to_square=a   )

								snake_case_           =				do_resize
								snake_case_           =				size
								snake_case_           =				resample
								snake_case_           =				do_rescale
								snake_case_           =				rescale_factor
								snake_case_           =				do_normalize
								snake_case_           =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								snake_case_           =				image_std if image_std is not None else OPENAI_CLIP_STD
								snake_case_           =				do_convert_rgb




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a = PILImageResampling.BICUBIC			,     a = None			,     **a			,     )			->       np.ndarray:
								snake_case_           =				get_size_dict(a			,     default_to_square=a   )
								if "height" not in size or "width" not in size:
															raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''   )
								snake_case_           =				(size['height'], size['width'])
								return resize(a			,     size=a			,     resample=a			,     data_format=a			,     **a   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a = None			,     **a			,     )			->       List[str]:
								return rescale(a			,     scale=a			,     data_format=a			,     **a   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a = None			,     **a			,     )			->       np.ndarray:
								return normalize(a			,     mean=a			,     std=a			,     data_format=a			,     **a   )






	def 				_UpperCamelCase     (			self			,     a			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     a = ChannelDimension.FIRST			,     **a			,     )			->       PIL.Image.Image:
								snake_case_           =				do_resize if do_resize is not None else self.do_resize
								snake_case_           =				resample if resample is not None else self.resample
								snake_case_           =				do_rescale if do_rescale is not None else self.do_rescale
								snake_case_           =				rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case_           =				do_normalize if do_normalize is not None else self.do_normalize
								snake_case_           =				image_mean if image_mean is not None else self.image_mean
								snake_case_           =				image_std if image_std is not None else self.image_std
								snake_case_           =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								snake_case_           =				size if size is not None else self.size
								snake_case_           =				get_size_dict(a			,     default_to_square=a   )

								snake_case_           =				make_list_of_images(a   )

								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.'   )

								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_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:
															snake_case_           =				[convert_to_rgb(a   ) for image in images]

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

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

								if do_rescale:
															snake_case_           =				[self.rescale(image=a			,     scale=a   ) for image in images]

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

								snake_case_           =				[to_channel_dimension_format(a			,     a   ) for image in images]

								snake_case_           =				BatchFeature(data={'pixel_values': images}			,     tensor_type=a   )

								return encoded_outputs













from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





