

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


a		=logging.get_logger(__name__)


if is_vision_available():
					import PIL






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict										=				['''pixel_values''']
							def __init__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__		:      PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Union[int, float] = 1 / 2_5_5       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,**SCREAMING_SNAKE_CASE__		:      Optional[int]       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			size if size is not None else {'shortest_edge': 2_2_4}
								__lowerCamelCase    :    List[str] 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__lowerCamelCase    :    List[str] 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__       ,param_name='crop_size')

								__lowerCamelCase    :    Any 	=			do_resize
								__lowerCamelCase    :    Tuple 	=			size
								__lowerCamelCase    :    Tuple 	=			resample
								__lowerCamelCase    :    Dict 	=			do_center_crop
								__lowerCamelCase    :    List[Any] 	=			crop_size
								__lowerCamelCase    :    Optional[Any] 	=			do_rescale
								__lowerCamelCase    :    Dict 	=			rescale_factor
								__lowerCamelCase    :    str 	=			do_normalize
								__lowerCamelCase    :    Optional[Any] 	=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__lowerCamelCase    :    int 	=			image_std if image_std is not None else OPENAI_CLIP_STD
								__lowerCamelCase    :    Dict 	=			do_convert_rgb
							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int]       ,SCREAMING_SNAKE_CASE__		:      PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								__lowerCamelCase    :    int 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__)
								if "shortest_edge" not in size:
									raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
								__lowerCamelCase    :    Tuple 	=			get_resize_output_image_size(SCREAMING_SNAKE_CASE__       ,size=size['shortest_edge']       ,default_to_square=SCREAMING_SNAKE_CASE__)
								return resize(SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__       ,resample=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int]       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      Tuple       ,):
								__lowerCamelCase    :    Dict 	=			get_size_dict(SCREAMING_SNAKE_CASE__)
								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(SCREAMING_SNAKE_CASE__       ,size=(size['height'], size['width'])       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Union[int, float]       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      Dict       ,):
								return rescale(SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__		:      Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      List[str]       ,):
								return normalize(SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      ImageInput       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__		:      PILImageResampling = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      int = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      float = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, TensorType]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[ChannelDimension] = ChannelDimension.FIRST       ,**SCREAMING_SNAKE_CASE__		:      int       ,):
								__lowerCamelCase    :    str 	=			do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase    :    Optional[Any] 	=			size if size is not None else self.size
								__lowerCamelCase    :    Tuple 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,param_name='size'       ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			resample if resample is not None else self.resample
								__lowerCamelCase    :    Optional[int] 	=			do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase    :    Optional[int] 	=			crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase    :    int 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,param_name='crop_size'       ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase    :    Dict 	=			rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase    :    List[str] 	=			do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase    :    int 	=			image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase    :    Tuple 	=			image_std if image_std is not None else self.image_std
								__lowerCamelCase    :    Optional[Any] 	=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__lowerCamelCase    :    Dict 	=			make_list_of_images(SCREAMING_SNAKE_CASE__)

								if not valid_images(SCREAMING_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_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:
									__lowerCamelCase    :    Union[str, Any] 	=			[convert_to_rgb(SCREAMING_SNAKE_CASE__) for image in images]

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

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

								if do_center_crop:
									__lowerCamelCase    :    List[str] 	=			[self.center_crop(image=SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__) for image in images]

								if do_rescale:
									__lowerCamelCase    :    Optional[Any] 	=			[self.rescale(image=SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__) for image in images]

								if do_normalize:
									__lowerCamelCase    :    List[Any] 	=			[self.normalize(image=SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase    :    str 	=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase    :    List[Any] 	=			{'pixel_values': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE__       ,tensor_type=SCREAMING_SNAKE_CASE__)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Pix2StructImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Pix2StructPreTrainedModel""",
					    """Pix2StructForConditionalGeneration""",
					    """Pix2StructVisionModel""",
					    """Pix2StructTextModel""",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




