




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						["""pixel_values"""]

      def __init__(				self    ,				A = True    ,				A = 3_2    ,				A=PILImageResampling.BILINEAR    ,				A = True    ,				**A    ,				)		->    None:
         snake_case				:    Tuple           =	do_resize
         snake_case				:    Optional[int]           =	do_rescale
         snake_case				:    Union[str, Any]           =	size_divisor
         snake_case				:    List[str]           =	resample
         super().__init__(**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A = None    ,				**A     )		->    np.ndarray:
         snake_case     , snake_case				:    Optional[Any]           =	get_image_size(A     )
         # Rounds the height and width down to the closest multiple of size_divisor
         snake_case				:    int           =	height // size_divisor * size_divisor
         snake_case				:    Any           =	width // size_divisor * size_divisor
         snake_case				:    List[Any]           =	resize(A    ,				(new_h, new_w)    ,				resample=A    ,				data_format=A    ,				**A     )
         return image

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = None    ,				**A     )		->    np.ndarray:
         return rescale(image=A    ,				scale=A    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A = None    ,				A = None    ,				A=None    ,				A = None    ,				A = None    ,				A = ChannelDimension.FIRST    ,				**A    ,				)		->    BatchFeature:
         snake_case				:    Any           =	do_resize if do_resize is not None else self.do_resize
         snake_case				:    Any           =	do_rescale if do_rescale is not None else self.do_rescale
         snake_case				:    List[Any]           =	size_divisor if size_divisor is not None else self.size_divisor
         snake_case				:    List[str]           =	resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
            raise ValueError("""size_divisor is required for resizing"""     )

         snake_case				:    Union[str, Any]           =	make_list_of_images(A     )

         if not valid_images(A     ):
            raise ValueError("""Invalid image(s)"""     )

         # All transformations expect numpy arrays.
         snake_case				:    str           =	[to_numpy_array(A     ) for img in images]

         if do_resize:
            snake_case				:    List[str]           =	[self.resize(A    ,				size_divisor=A    ,				resample=A     ) for image in images]

         if do_rescale:
            snake_case				:    str           =	[self.rescale(A    ,				scale=1 / 2_5_5     ) for image in images]

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

         snake_case				:    Optional[Any]           =	{"""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	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Tuple				=   ['RobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase	:	str				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





