
"""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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    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


_a														=		logging.get_logger(__name__)






class     _UpperCAmelCase(    __lowercase	):
		lowercase__						=      ["pixel_values"]



		def __init__(					self				,		__a = True				,		__a = None				,		__a = PILImageResampling.BICUBIC				,		__a = True				,		__a = 1 / 2_55				,		__a = True				,		__a = None				,		__a = None				,		__a = True				,		**__a				,		)       ->       Dict:






							'''simple docstring'''
							super().__init__(**snake_case_)
							_UpperCamelCase							 =  size if size is not None else {'''height''': 3_84, '''width''': 3_84}
							_UpperCamelCase							 =  get_size_dict(snake_case_				,		default_to_square=snake_case_)

							_UpperCamelCase							 =  do_resize
							_UpperCamelCase							 =  size
							_UpperCamelCase							 =  resample
							_UpperCamelCase							 =  do_rescale
							_UpperCamelCase							 =  rescale_factor
							_UpperCamelCase							 =  do_normalize
							_UpperCamelCase							 =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							_UpperCamelCase							 =  image_std if image_std is not None else OPENAI_CLIP_STD
							_UpperCamelCase							 =  do_convert_rgb



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = PILImageResampling.BICUBIC				,		__a = None				,		**__a				,		)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  get_size_dict(snake_case_				,		default_to_square=snake_case_)
							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()}''')
							_UpperCamelCase							 =  (size['''height'''], size['''width'''])
							return resize(snake_case_				,		size=snake_case_				,		resample=snake_case_				,		data_format=snake_case_				,		**snake_case_)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = None				,		**__a				,		)       ->       List[str]:






							'''simple docstring'''
							return rescale(snake_case_				,		scale=snake_case_				,		data_format=snake_case_				,		**snake_case_)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a = None				,		**__a				,		)       ->       int:






							'''simple docstring'''
							return normalize(snake_case_				,		mean=snake_case_				,		std=snake_case_				,		data_format=snake_case_				,		**snake_case_)




		def 						UpperCAmelCase							(					self				,		__a				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = ChannelDimension.FIRST				,		**__a				,		)       ->       Tuple:






							'''simple docstring'''
							_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_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase							 =  image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase							 =  image_std if image_std is not None else self.image_std
							_UpperCamelCase							 =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							_UpperCamelCase							 =  size if size is not None else self.size
							_UpperCamelCase							 =  get_size_dict(snake_case_				,		default_to_square=snake_case_)

							_UpperCamelCase							 =  make_list_of_images(snake_case_)

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

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
												_UpperCamelCase							 =  [convert_to_rgb(snake_case_) for image in images]

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

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

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

							if do_normalize:
												_UpperCamelCase							 =  [self.normalize(image=snake_case_				,		mean=snake_case_				,		std=snake_case_) for image in images]

							_UpperCamelCase							 =  [to_channel_dimension_format(snake_case_				,		snake_case_) for image in images]

							_UpperCamelCase							 =  BatchFeature(data={'''pixel_values''': images}				,		tensor_type=snake_case_)

							return encoded_outputs








from typing import TYPE_CHECKING

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


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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