


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							:   Any						=						logging.get_logger(__name__)


if is_vision_available():
				import PIL






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			['pixel_values']

				def __init__(    self					, a__ = True					, a__ = None					, a__ = PILImageResampling.BICUBIC					, a__ = True					, a__ = None					, a__ = True					, a__ = 1 / 255					, a__ = True					, a__ = None					, a__ = None					, a__ = True					, **a__					, )						->					None:
					super().__init__(**a__ )
					A             =					size if size is not None else {"""shortest_edge""": 224}
					A             =					get_size_dict(a__					, default_to_square=a__ )
					A             =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
					A             =					get_size_dict(a__					, default_to_square=a__					, param_name="""crop_size""" )

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

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ = PILImageResampling.BICUBIC					, a__ = None					, **a__					, )						->					np.ndarray:
					A             =					get_size_dict(a__					, default_to_square=a__ )
					if "shortest_edge" not in size:
						raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
					A             =					get_resize_output_image_size(a__					, size=size["""shortest_edge"""]					, default_to_square=a__ )
					return resize(a__					, size=a__					, resample=a__					, data_format=a__					, **a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ = None					, **a__					, )						->					np.ndarray:
					A             =					get_size_dict(a__ )
					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(a__					, size=(size["""height"""], size["""width"""])					, data_format=a__					, **a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ = None					, **a__					, )						->					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__ = None					, a__ = None					, a__ = ChannelDimension.FIRST					, **a__					, )						->					PIL.Image.Image:
					A             =					do_resize if do_resize is not None else self.do_resize
					A             =					size if size is not None else self.size
					A             =					get_size_dict(a__					, param_name="""size"""					, default_to_square=a__ )
					A             =					resample if resample is not None else self.resample
					A             =					do_center_crop if do_center_crop is not None else self.do_center_crop
					A             =					crop_size if crop_size is not None else self.crop_size
					A             =					get_size_dict(a__					, param_name="""crop_size"""					, default_to_square=a__ )
					A             =					do_rescale if do_rescale is not None else self.do_rescale
					A             =					rescale_factor if rescale_factor is not None else self.rescale_factor
					A             =					do_normalize if do_normalize is not None else self.do_normalize
					A             =					image_mean if image_mean is not None else self.image_mean
					A             =					image_std if image_std is not None else self.image_std
					A             =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					A             =					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:
						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:
						A             =					[convert_to_rgb(a__ ) for image in images]

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

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

					if do_center_crop:
						A             =					[self.center_crop(image=a__					, size=a__ ) for image in images]

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

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

					A             =					[to_channel_dimension_format(a__					, a__ ) for image in images]

					A             =					{"""pixel_values""": images}
					return BatchFeature(data=a__					, tensor_type=a__ )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							:   int						=						{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   List[Any]						=						[
				    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RobertaPreLayerNormForCausalLM",
				    "RobertaPreLayerNormForMaskedLM",
				    "RobertaPreLayerNormForMultipleChoice",
				    "RobertaPreLayerNormForQuestionAnswering",
				    "RobertaPreLayerNormForSequenceClassification",
				    "RobertaPreLayerNormForTokenClassification",
				    "RobertaPreLayerNormModel",
				    "RobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Optional[int]						=						[
				    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRobertaPreLayerNormForCausalLM",
				    "TFRobertaPreLayerNormForMaskedLM",
				    "TFRobertaPreLayerNormForMultipleChoice",
				    "TFRobertaPreLayerNormForQuestionAnswering",
				    "TFRobertaPreLayerNormForSequenceClassification",
				    "TFRobertaPreLayerNormForTokenClassification",
				    "TFRobertaPreLayerNormMainLayer",
				    "TFRobertaPreLayerNormModel",
				    "TFRobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   str						=						[
				    "FlaxRobertaPreLayerNormForCausalLM",
				    "FlaxRobertaPreLayerNormForMaskedLM",
				    "FlaxRobertaPreLayerNormForMultipleChoice",
				    "FlaxRobertaPreLayerNormForQuestionAnswering",
				    "FlaxRobertaPreLayerNormForSequenceClassification",
				    "FlaxRobertaPreLayerNormForTokenClassification",
				    "FlaxRobertaPreLayerNormModel",
				    "FlaxRobertaPreLayerNormPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    RobertaPreLayerNormConfig,
				    RobertaPreLayerNormOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta_prelayernorm import (
								    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaPreLayerNormForCausalLM,
								    RobertaPreLayerNormForMaskedLM,
								    RobertaPreLayerNormForMultipleChoice,
								    RobertaPreLayerNormForQuestionAnswering,
								    RobertaPreLayerNormForSequenceClassification,
								    RobertaPreLayerNormForTokenClassification,
								    RobertaPreLayerNormModel,
								    RobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta_prelayernorm import (
								    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaPreLayerNormForCausalLM,
								    TFRobertaPreLayerNormForMaskedLM,
								    TFRobertaPreLayerNormForMultipleChoice,
								    TFRobertaPreLayerNormForQuestionAnswering,
								    TFRobertaPreLayerNormForSequenceClassification,
								    TFRobertaPreLayerNormForTokenClassification,
								    TFRobertaPreLayerNormMainLayer,
								    TFRobertaPreLayerNormModel,
								    TFRobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta_prelayernorm import (
								    FlaxRobertaPreLayerNormForCausalLM,
								    FlaxRobertaPreLayerNormForMaskedLM,
								    FlaxRobertaPreLayerNormForMultipleChoice,
								    FlaxRobertaPreLayerNormForQuestionAnswering,
								    FlaxRobertaPreLayerNormForSequenceClassification,
								    FlaxRobertaPreLayerNormForTokenClassification,
								    FlaxRobertaPreLayerNormModel,
								    FlaxRobertaPreLayerNormPreTrainedModel,
								)

else:
				import sys

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



