





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowerCamelCase       :     str   =     logging.get_logger(__name__)



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Optional[Any]						    =   ['pixel_values']





      def __init__( self       ,							A_ = True       ,							A_ = 1 / 255       ,							A_ = True       ,							A_ = 8       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(**A_   )

       UpperCamelCase	:					List[Any]       	=   do_rescale
       UpperCamelCase	:					Any       	=   rescale_factor
       UpperCamelCase	:					int       	=   do_pad
       UpperCamelCase	:					Optional[int]       	=   pad_size





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None       ,							**A_   ):
       '''simple docstring'''




       return rescale(A_       ,							scale=A_       ,							data_format=A_       ,							**A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   get_image_size(A_   )
       UpperCamelCase	:					int       	=   (old_height // size + 1) * size - old_height
       UpperCamelCase	:					Dict       	=   (old_width // size + 1) * size - old_width

       return pad(A_       ,							((0, pad_height), (0, pad_width))       ,							mode="symmetric"       ,							data_format=A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = ChannelDimension.FIRST       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase	:					str       	=   rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase	:					Union[str, Any]       	=   do_pad if do_pad is not None else self.do_pad
       UpperCamelCase	:					List[Any]       	=   pad_size if pad_size is not None else self.pad_size

       UpperCamelCase	:					Tuple       	=   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_rescale and rescale_factor is None:
        raise ValueError("Rescale factor must be specified if do_rescale is True."   )

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

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

       if do_pad:
        UpperCamelCase	:					List[str]       	=   [self.pad(A_       ,							size=A_   ) for image in images]

       UpperCamelCase	:					Dict       	=   [to_channel_dimension_format(A_       ,							A_   ) for image in images]

       UpperCamelCase	:					Dict       	=   {"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_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase       :     Any   =     {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Dict   =     ["""ElectraTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Tuple   =     [
        """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ElectraForCausalLM""",
        """ElectraForMaskedLM""",
        """ElectraForMultipleChoice""",
        """ElectraForPreTraining""",
        """ElectraForQuestionAnswering""",
        """ElectraForSequenceClassification""",
        """ElectraForTokenClassification""",
        """ElectraModel""",
        """ElectraPreTrainedModel""",
        """load_tf_weights_in_electra""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[Any]   =     [
        """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFElectraForMaskedLM""",
        """TFElectraForMultipleChoice""",
        """TFElectraForPreTraining""",
        """TFElectraForQuestionAnswering""",
        """TFElectraForSequenceClassification""",
        """TFElectraForTokenClassification""",
        """TFElectraModel""",
        """TFElectraPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[str]   =     [
        """FlaxElectraForCausalLM""",
        """FlaxElectraForMaskedLM""",
        """FlaxElectraForMultipleChoice""",
        """FlaxElectraForPreTraining""",
        """FlaxElectraForQuestionAnswering""",
        """FlaxElectraForSequenceClassification""",
        """FlaxElectraForTokenClassification""",
        """FlaxElectraModel""",
        """FlaxElectraPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

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