'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowercase		:					Union[str, Any]													=		{"configuration_bert_generation": ["BertGenerationConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:					int													=		["BertGenerationTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:					str													=		[
						    "BertGenerationDecoder",
						    "BertGenerationEncoder",
						    "BertGenerationPreTrainedModel",
						    "load_tf_weights_in_bert_generation",
						]


if TYPE_CHECKING:
						from .configuration_bert_generation import BertGenerationConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_generation import BertGenerationTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bert_generation import (
												    BertGenerationDecoder,
												    BertGenerationEncoder,
												    BertGenerationPreTrainedModel,
												    load_tf_weights_in_bert_generation,
												)

else:
						import sys

						_lowercase		:					Dict													=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''simple docstring'''




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


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


if is_vision_available():
						import PIL





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			['''pixel_values''']







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

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Dict							,     ):
										lowercase_   :     Tuple									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			get_resize_output_image_size(
										    lowercase_							,     size=(size["""height"""], size["""width"""])							,     default_to_square=lowercase_       )
										return resize(lowercase_							,     size=lowercase_							,     resample=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Any							,     ):
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										return center_crop(lowercase_							,     size=(size["""height"""], size["""width"""])							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[int, float]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    List[Any]							,     ):
										return rescale(lowercase_							,     scale=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Optional[Any]							,     ):
										return normalize(lowercase_							,     mean=lowercase_							,     std=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







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

										lowercase_   :     int									=			make_list_of_images(lowercase_       )

										if not valid_images(lowercase_       ):
																	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:
																	lowercase_   :     Dict									=			[convert_to_rgb(lowercase_       ) for image in images]

										# All transformations expect numpy arrays.
										lowercase_   :     Any									=			[to_numpy_array(lowercase_       ) for image in images]

										if do_resize:
																	lowercase_   :     Union[str, Any]									=			[self.resize(image=lowercase_							,     size=lowercase_							,     resample=lowercase_       ) for image in images]

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

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

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

										lowercase_   :     List[Any]									=			[to_channel_dimension_format(lowercase_							,     lowercase_       ) for image in images]

										lowercase_   :     Any									=			{"""pixel_values""": images}
										return BatchFeature(data=lowercase_							,     tensor_type=lowercase_       )





