


"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_   :   List[Any]									=  logging.get_logger(__name__)






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  ["pixel_values"]


 def __init__(		self ,    __A = True ,    __A = None ,    __A = PILImageResampling.BICUBIC ,    __A = True ,    __A = True ,    __A = 1 / 255 ,    __A = None ,    __A = True ,    __A = None ,    __A = None ,    **__A ,    )					->     None:
     super().__init__(**__A						)
     a 		=size if size is not None else {'''height''': 224, '''width''': 224}
     a 		=get_size_dict(__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 		=do_rescale
     a 		=do_normalize
     a 		=do_center_crop
     a 		=crop_size
     a 		=size
     a 		=resample
     a 		=rescale_factor
     a 		=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     a 		=image_std if image_std is not None else IMAGENET_DEFAULT_STD


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = PILImageResampling.BILINEAR ,    __A = None ,    **__A ,    )					->     np.ndarray:
     a 		=get_size_dict(__A						)

     if "shortest_edge" in size:
         a 		=get_resize_output_image_size(__A ,    size=size['''shortest_edge'''] ,    default_to_square=__A						)
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
     elif "height" in size and "width" in size:
         a 		=(size['''height'''], size['''width'''])
     else:
         raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''						)
     return resize(__A ,    size=__A ,    resample=__A ,    data_format=__A ,    **__A						)


 def       SCREAMING_SNAKE_CASE							(		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       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = None ,    **__A						)					->     np.ndarray:
     return rescale(__A ,    scale=__A ,    data_format=__A ,    **__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A = None ,    **__A ,    )					->     np.ndarray:
     return normalize(__A ,    mean=__A ,    std=__A ,    data_format=__A ,    **__A						)





 def       SCREAMING_SNAKE_CASE							(		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 = ChannelDimension.FIRST ,    **__A ,    )					->     BatchFeature:
     a 		=do_resize if do_resize is not None else self.do_resize
     a 		=do_rescale if do_rescale is not None else self.do_rescale
     a 		=do_normalize if do_normalize is not None else self.do_normalize
     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 		=resample if resample is not None else self.resample
     a 		=rescale_factor if rescale_factor is not None else self.rescale_factor
     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 		=size if size is not None else self.size
     a 		=get_size_dict(__A						)

     if not is_batched(__A						):
         a 		=[images]

     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.'''						)

     # 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_tokenizers_available,
    is_torch_available,
)


UpperCamelCase  							=						{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''ElectraTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''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:
      UpperCamelCase  							=						[
          '''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:
      UpperCamelCase  							=						[
          '''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

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



