

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 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_vision_available, logging


if is_vision_available():
       import PIL


_SCREAMING_SNAKE_CASE  :						Optional[int]         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					b.T
     SCREAMING_SNAKE_CASE__					=					np.sum(np.square(_A       )   ,	axis=1       )
     SCREAMING_SNAKE_CASE__					=					np.sum(np.square(_A       )   ,	axis=0       )
     SCREAMING_SNAKE_CASE__					=					np.matmul(_A   ,	_A       )
     SCREAMING_SNAKE_CASE__					=					aa[:, None] - 2 * ab + ba[None, :]
     return d
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					x.reshape(-1   ,	3       )
     SCREAMING_SNAKE_CASE__					=					squared_euclidean_distance(_A   ,	_A       )
     return np.argmin(_A   ,	axis=1       )







class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      ["pixel_values"]






  def __init__(							self		:	Any			,     __lowerCamelCase		:	Optional[Union[List[List[int]], np.ndarray]] = None			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BILINEAR			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	bool = True			,     **__lowerCamelCase		:	List[Any]			,     )	->							None:
       super().__init__(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					size if size is not None else {'''height''': 256, '''width''': 256}
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					np.array(__lowerCamelCase      ) if clusters is not None else None
       SCREAMING_SNAKE_CASE__					=					do_resize
       SCREAMING_SNAKE_CASE__					=					size
       SCREAMING_SNAKE_CASE__					=					resample
       SCREAMING_SNAKE_CASE__					=					do_normalize
       SCREAMING_SNAKE_CASE__					=					do_color_quantize






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BILINEAR			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	List[Any]			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase      )
       if "height" not in size or "width" not in size:
            raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''      )
       return resize(
           __lowerCamelCase			,     size=(size['''height'''], size['''width'''])			,     resample=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__					=					rescale(image=__lowerCamelCase			,     scale=1 / 127.5			,     data_format=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					image - 1
       return image







  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	ImageInput			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	PILImageResampling = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Optional[Union[List[List[int]], np.ndarray]] = None			,     __lowerCamelCase		:	Optional[Union[str, TensorType]] = None			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST			,     **__lowerCamelCase		:	Optional[Any]			,     )	->							PIL.Image.Image:
       SCREAMING_SNAKE_CASE__					=					do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__					=					size if size is not None else self.size
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__					=					do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       SCREAMING_SNAKE_CASE__					=					clusters if clusters is not None else self.clusters
       SCREAMING_SNAKE_CASE__					=					np.array(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					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 or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

       if do_color_quantize and clusters is None:
            raise ValueError('''Clusters must be specified if do_color_quantize is True.'''      )

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

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

       if do_normalize:
            SCREAMING_SNAKE_CASE__					=					[self.normalize(image=__lowerCamelCase      ) for image in images]

       if do_color_quantize:
            SCREAMING_SNAKE_CASE__					=					[to_channel_dimension_format(__lowerCamelCase			,     ChannelDimension.LAST      ) for image in images]
            # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
            SCREAMING_SNAKE_CASE__					=					np.array(__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					color_quantize(__lowerCamelCase			,     __lowerCamelCase      ).reshape(images.shape[:-1]      )

            # flatten to (batch_size, height*width)
            SCREAMING_SNAKE_CASE__					=					images.shape[0]
            SCREAMING_SNAKE_CASE__					=					images.reshape(__lowerCamelCase			,     -1      )

            # We need to convert back to a list of images to keep consistent behaviour across processors.
            SCREAMING_SNAKE_CASE__					=					list(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					[to_channel_dimension_format(__lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       SCREAMING_SNAKE_CASE__					=					{'''input_ids''': images}
       return BatchFeature(data=__lowerCamelCase			,     tensor_type=__lowerCamelCase      )





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						List[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:
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    ['''RobertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    [
           '''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:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''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:
       _SCREAMING_SNAKE_CASE  :						Optional[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

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


