


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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


snake_case__     							= logging.get_logger(__name__)


if is_vision_available():
			import PIL



class     snake_case__  (      lowerCamelCase__  ):
				__lowerCAmelCase		=      ['''pixel_values''']
				def __init__(  self     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = PILImageResampling.BICUBIC     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = 1 / 255     ,      _UpperCAmelCase = True     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = True     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										super().__init__(**SCREAMING_SNAKE_CASE_      )
										__magic_name__				:							List[str]				=      size if size is not None else {'shortest_edge': 224}
										__magic_name__				:							Optional[Any]				=      get_size_dict(SCREAMING_SNAKE_CASE_     ,      default_to_square=SCREAMING_SNAKE_CASE_      )
										__magic_name__				:							str				=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
										__magic_name__				:							List[str]				=      get_size_dict(SCREAMING_SNAKE_CASE_      )

										__magic_name__				:							Tuple				=      do_resize
										__magic_name__				:							Any				=      size
										__magic_name__				:							Union[str, Any]				=      resample
										__magic_name__				:							List[Any]				=      do_center_crop
										__magic_name__				:							Union[str, Any]				=      crop_size
										__magic_name__				:							str				=      do_rescale
										__magic_name__				:							Dict				=      rescale_factor
										__magic_name__				:							int				=      do_normalize
										__magic_name__				:							Optional[int]				=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										__magic_name__				:							List[Any]				=      image_std if image_std is not None else OPENAI_CLIP_STD
										__magic_name__				:							List[Any]				=      do_convert_rgb
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = PILImageResampling.BICUBIC     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      get_size_dict(SCREAMING_SNAKE_CASE_     ,      default_to_square=SCREAMING_SNAKE_CASE_      )
										__magic_name__				:							Union[str, Any]				=      get_resize_output_image_size(
										    SCREAMING_SNAKE_CASE_     ,      size=(size['height'], size['width'])     ,      default_to_square=SCREAMING_SNAKE_CASE_      )
										return resize(SCREAMING_SNAKE_CASE_     ,      size=SCREAMING_SNAKE_CASE_     ,      resample=SCREAMING_SNAKE_CASE_     ,      data_format=SCREAMING_SNAKE_CASE_     ,      **SCREAMING_SNAKE_CASE_      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      get_size_dict(SCREAMING_SNAKE_CASE_      )
										return center_crop(SCREAMING_SNAKE_CASE_     ,      size=(size['height'], size['width'])     ,      data_format=SCREAMING_SNAKE_CASE_     ,      **SCREAMING_SNAKE_CASE_      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										return rescale(SCREAMING_SNAKE_CASE_     ,      scale=SCREAMING_SNAKE_CASE_     ,      data_format=SCREAMING_SNAKE_CASE_     ,      **SCREAMING_SNAKE_CASE_      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										return normalize(SCREAMING_SNAKE_CASE_     ,      mean=SCREAMING_SNAKE_CASE_     ,      std=SCREAMING_SNAKE_CASE_     ,      data_format=SCREAMING_SNAKE_CASE_     ,      **SCREAMING_SNAKE_CASE_      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = ChannelDimension.FIRST     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      do_resize if do_resize is not None else self.do_resize
										__magic_name__				:							Optional[Any]				=      size if size is not None else self.size
										__magic_name__				:							Union[str, Any]				=      get_size_dict(SCREAMING_SNAKE_CASE_     ,      default_to_square=SCREAMING_SNAKE_CASE_      )
										__magic_name__				:							Optional[Any]				=      resample if resample is not None else self.resample
										__magic_name__				:							Tuple				=      do_center_crop if do_center_crop is not None else self.do_center_crop
										__magic_name__				:							Any				=      crop_size if crop_size is not None else self.crop_size
										__magic_name__				:							Tuple				=      get_size_dict(SCREAMING_SNAKE_CASE_      )
										__magic_name__				:							List[Any]				=      do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__				:							Dict				=      rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__				:							Dict				=      do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__				:							List[str]				=      image_mean if image_mean is not None else self.image_mean
										__magic_name__				:							List[str]				=      image_std if image_std is not None else self.image_std
										__magic_name__				:							List[str]				=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										__magic_name__				:							int				=      make_list_of_images(SCREAMING_SNAKE_CASE_      )

										if not valid_images(SCREAMING_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:
																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.'      )

										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:
																__magic_name__				:							Dict				=      [convert_to_rgb(SCREAMING_SNAKE_CASE_      ) for image in images]

										# All transformations expect numpy arrays.
										__magic_name__				:							Tuple				=      [to_numpy_array(SCREAMING_SNAKE_CASE_      ) for image in images]

										if do_resize:
																__magic_name__				:							Any				=      [self.resize(image=SCREAMING_SNAKE_CASE_     ,      size=SCREAMING_SNAKE_CASE_     ,      resample=SCREAMING_SNAKE_CASE_      ) for image in images]

										if do_center_crop:
																__magic_name__				:							str				=      [self.center_crop(image=SCREAMING_SNAKE_CASE_     ,      size=SCREAMING_SNAKE_CASE_      ) for image in images]

										if do_rescale:
																__magic_name__				:							Any				=      [self.rescale(image=SCREAMING_SNAKE_CASE_     ,      scale=SCREAMING_SNAKE_CASE_      ) for image in images]

										if do_normalize:
																__magic_name__				:							Optional[Any]				=      [self.normalize(image=SCREAMING_SNAKE_CASE_     ,      mean=SCREAMING_SNAKE_CASE_     ,      std=SCREAMING_SNAKE_CASE_      ) for image in images]

										__magic_name__				:							List[str]				=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,      SCREAMING_SNAKE_CASE_      ) for image in images]

										__magic_name__				:							str				=      {'pixel_values': images}
										return BatchFeature(data=SCREAMING_SNAKE_CASE_     ,      tensor_type=SCREAMING_SNAKE_CASE_      )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

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


