"""simple docstring"""


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
				import PIL

if is_torch_available():
				import torch


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				(		lowerCAmelCase_   ):
				_snake_case     :  Union[str, Any]     							=							['pixel_values']


				def __init__(					self	:  Dict   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Dict[str, int] = None   ,				__lowerCAmelCase	:  PILImageResampling = PILImageResampling.BILINEAR   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Union[int, float] = 1 / 255   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Dict[str, int] = None   ,				__lowerCAmelCase	:  bool = True   ,				**__lowerCAmelCase	:  Dict   ,				):
						super().__init__(**__lowerCAmelCase					)
						_UpperCAmelCase						 =      size if size is not None else {"""shortest_edge""": 224}
						_UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase   ,				default_to_square=__lowerCAmelCase					)
						_UpperCAmelCase						 =      crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
						_UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase   ,				param_name="""crop_size"""					)

						_UpperCAmelCase						 =      do_resize
						_UpperCAmelCase						 =      size
						_UpperCAmelCase						 =      resample
						_UpperCAmelCase						 =      do_rescale
						_UpperCAmelCase						 =      rescale_factor
						_UpperCAmelCase						 =      do_center_crop
						_UpperCAmelCase						 =      crop_size
						_UpperCAmelCase						 =      do_flip_channel_order


				def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  Dict[str, int]   ,				__lowerCAmelCase	:  PILImageResampling = PIL.Image.BILINEAR   ,				__lowerCAmelCase	:  Optional[Union[str, ChannelDimension]] = None   ,				**__lowerCAmelCase	:  str   ,				):
						_UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase   ,				default_to_square=__lowerCAmelCase					)
						if "shortest_edge" not in size:
								raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''					)
						_UpperCAmelCase						 =      get_resize_output_image_size(__lowerCAmelCase   ,				size=size["""shortest_edge"""]   ,				default_to_square=__lowerCAmelCase					)
						return resize(__lowerCAmelCase   ,				size=__lowerCAmelCase   ,				resample=__lowerCAmelCase   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  Dict[str, int]   ,				__lowerCAmelCase	:  Optional[Union[str, ChannelDimension]] = None   ,				**__lowerCAmelCase	:  str   ,				):
						_UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase					)
						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()}'''					)
						return center_crop(__lowerCAmelCase   ,				size=(size["""height"""], size["""width"""])   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase					)


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


				def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  Optional[Union[str, ChannelDimension]] = None					):
						return flip_channel_order(__lowerCAmelCase   ,				data_format=__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  ImageInput   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  Dict[str, int] = None   ,				__lowerCAmelCase	:  PILImageResampling = None   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  float = None   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  Dict[str, int] = None   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  Optional[Union[str, TensorType]] = None   ,				__lowerCAmelCase	:  ChannelDimension = ChannelDimension.FIRST   ,				**__lowerCAmelCase	:  Union[str, Any]   ,				):
						_UpperCAmelCase						 =      do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase						 =      resample if resample is not None else self.resample
						_UpperCAmelCase						 =      do_rescale if do_rescale is not None else self.do_rescale
						_UpperCAmelCase						 =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_UpperCAmelCase						 =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_UpperCAmelCase						 =      (
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

						_UpperCAmelCase						 =      size if size is not None else self.size
						_UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase   ,				default_to_square=__lowerCAmelCase					)
						_UpperCAmelCase						 =      crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase   ,				param_name="""crop_size"""					)

						_UpperCAmelCase						 =      make_list_of_images(__lowerCAmelCase					)

						if not valid_images(__lowerCAmelCase					):
								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_rescale and rescale_factor is None:
								raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_center_crop and crop_size is None:
								raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

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

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

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

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

						# the pretrained checkpoints assume images are BGR, not RGB
						if do_flip_channel_order:
								_UpperCAmelCase						 =      [self.flip_channel_order(image=__lowerCAmelCase					) for image in images]

						_UpperCAmelCase						 =      [to_channel_dimension_format(__lowerCAmelCase   ,				__lowerCAmelCase					) for image in images]

						_UpperCAmelCase						 =      {"""pixel_values""": images}
						return BatchFeature(data=__lowerCAmelCase   ,				tensor_type=__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[Tuple] = None					):
						_UpperCAmelCase						 =      outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
								if len(__lowerCAmelCase					) != len(__lowerCAmelCase					):
										raise ValueError(
										    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""					)

								if is_torch_tensor(__lowerCAmelCase					):
										_UpperCAmelCase						 =      target_sizes.numpy()

								_UpperCAmelCase						 =      []

								for idx in range(len(__lowerCAmelCase					)					):
										_UpperCAmelCase						 =      torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0					)   ,				size=target_sizes[idx]   ,				mode="""bilinear"""   ,				align_corners=__lowerCAmelCase					)
										_UpperCAmelCase						 =      resized_logits[0].argmax(dim=0					)
										semantic_segmentation.append(__lowerCAmelCase					)
						else:
								_UpperCAmelCase						 =      logits.argmax(dim=1					)
								_UpperCAmelCase						 =      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

						return semantic_segmentation

"""simple docstring"""


from typing import TYPE_CHECKING

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


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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """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:
    UpperCAmelCase__  		=						[
        """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:
    UpperCAmelCase__  		=						[
        """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

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