

'''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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


lowerCamelCase			:				Dict 			=					logging.get_logger(__name__)







class 							A__   (	A__  ):
  A__					=    ['pixel_values']





  def __init__(		self				:     Tuple							, _a				:     bool = True							, _a				:     Dict[str, int] = None							, _a				:     int = 0.9							, _a				:     PILImageResampling = PILImageResampling.BICUBIC							, _a				:     bool = True							, _a				:     Dict[str, int] = None							, _a				:     Union[int, float] = 1 / 255							, _a				:     bool = True							, _a				:     bool = True							, _a				:     Optional[Union[float, List[float]]] = None							, _a				:     Optional[Union[float, List[float]]] = None							, **_a				:     List[str]							, )     ->   None:





       '''simple docstring'''






       super().__init__(**_a			)
       _SCREAMING_SNAKE_CASE							   =size if size is not None else {'shortest_edge': 224}
       _SCREAMING_SNAKE_CASE							   =get_size_dict(_a							, default_to_square=_a			)
       _SCREAMING_SNAKE_CASE							   =crop_size if crop_size is not None else {'height': 224, 'width': 224}
       _SCREAMING_SNAKE_CASE							   =get_size_dict(_a							, param_name='crop_size'			)

       _SCREAMING_SNAKE_CASE							   =do_resize
       _SCREAMING_SNAKE_CASE							   =size
       _SCREAMING_SNAKE_CASE							   =crop_pct
       _SCREAMING_SNAKE_CASE							   =resample
       _SCREAMING_SNAKE_CASE							   =do_center_crop
       _SCREAMING_SNAKE_CASE							   =crop_size
       _SCREAMING_SNAKE_CASE							   =do_rescale
       _SCREAMING_SNAKE_CASE							   =rescale_factor
       _SCREAMING_SNAKE_CASE							   =do_normalize
       _SCREAMING_SNAKE_CASE							   =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       _SCREAMING_SNAKE_CASE							   =image_std if image_std is not None else IMAGENET_DEFAULT_STD





  def 						A  (		self				:     Optional[Any]							, _a				:     np.ndarray							, _a				:     Dict[str, int]							, _a				:     Optional[float] = None							, _a				:     PILImageResampling = PILImageResampling.BICUBIC							, _a				:     Optional[Union[str, ChannelDimension]] = None							, **_a				:     Tuple							, )     ->   np.ndarray:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =get_size_dict(_a							, default_to_square=_a			)
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
            raise ValueError(f"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"			)
       if crop_pct is not None:
            if "shortest_edge" in size:
                 _SCREAMING_SNAKE_CASE							   =int(size['shortest_edge'] / crop_pct			)
            elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                      _SCREAMING_SNAKE_CASE							   =int(size['height'] / crop_pct			)
                 else:
                      _SCREAMING_SNAKE_CASE							   =(int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
            else:
                 raise ValueError('Invalid size for resize: {}'.format(_a			)			)

            _SCREAMING_SNAKE_CASE							   =get_resize_output_image_size(_a							, size=_a							, default_to_square=_a			)
       else:
            if "shortest_edge" in size:
                 _SCREAMING_SNAKE_CASE							   =get_resize_output_image_size(_a							, size=size['shortest_edge']							, default_to_square=_a			)
            elif "height" in size and "width" in size:
                 _SCREAMING_SNAKE_CASE							   =(size['height'], size['width'])
            else:
                 raise ValueError('Invalid size for resize: {}'.format(_a			)			)

       return resize(_a							, size=_a							, resample=_a							, data_format=_a							, **_a			)





  def 						A  (		self				:     Any							, _a				:     np.ndarray							, _a				:     Dict[str, int]							, _a				:     Optional[Union[str, ChannelDimension]] = None							, **_a				:     List[Any]							, )     ->   np.ndarray:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =get_size_dict(_a			)
       if "height" not in size or "width" not in size:
            raise ValueError(f"size must contain 'height' and 'width' as keys. Got {size.keys()}"			)
       return center_crop(_a							, size=(size['height'], size['width'])							, data_format=_a							, **_a			)





  def 						A  (		self				:     List[str]							, _a				:     np.ndarray							, _a				:     Union[int, float]							, _a				:     Optional[Union[str, ChannelDimension]] = None							, **_a				:     List[Any]							, )     ->   List[Any]:





       '''simple docstring'''






       return rescale(_a							, scale=_a							, data_format=_a							, **_a			)





  def 						A  (		self				:     Dict							, _a				:     np.ndarray							, _a				:     Union[float, List[float]]							, _a				:     Union[float, List[float]]							, _a				:     Optional[Union[str, ChannelDimension]] = None							, **_a				:     Tuple							, )     ->   np.ndarray:





       '''simple docstring'''






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





  def 						A  (		self				:     Tuple							, _a				:     ImageInput							, _a				:     bool = None							, _a				:     Dict[str, int] = None							, _a				:     int = None							, _a				:     PILImageResampling = None							, _a				:     bool = None							, _a				:     Dict[str, int] = None							, _a				:     bool = None							, _a				:     float = None							, _a				:     bool = None							, _a				:     Optional[Union[float, List[float]]] = None							, _a				:     Optional[Union[float, List[float]]] = None							, _a				:     Optional[Union[str, TensorType]] = None							, _a				:     ChannelDimension = ChannelDimension.FIRST							, **_a				:     List[Any]							, )     ->   PIL.Image.Image:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =do_resize if do_resize is not None else self.do_resize
       _SCREAMING_SNAKE_CASE							   =crop_pct if crop_pct is not None else self.crop_pct
       _SCREAMING_SNAKE_CASE							   =resample if resample is not None else self.resample
       _SCREAMING_SNAKE_CASE							   =do_center_crop if do_center_crop is not None else self.do_center_crop
       _SCREAMING_SNAKE_CASE							   =do_rescale if do_rescale is not None else self.do_rescale
       _SCREAMING_SNAKE_CASE							   =rescale_factor if rescale_factor is not None else self.rescale_factor
       _SCREAMING_SNAKE_CASE							   =do_normalize if do_normalize is not None else self.do_normalize
       _SCREAMING_SNAKE_CASE							   =image_mean if image_mean is not None else self.image_mean
       _SCREAMING_SNAKE_CASE							   =image_std if image_std is not None else self.image_std

       _SCREAMING_SNAKE_CASE							   =size if size is not None else self.size
       _SCREAMING_SNAKE_CASE							   =get_size_dict(_a							, default_to_square=_a			)
       _SCREAMING_SNAKE_CASE							   =crop_size if crop_size is not None else self.crop_size
       _SCREAMING_SNAKE_CASE							   =get_size_dict(_a							, param_name='crop_size'			)

       _SCREAMING_SNAKE_CASE							   =make_list_of_images(_a			)

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

       if do_center_crop and crop_pct is None:
            raise ValueError('Crop_pct 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.'			)

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

       if do_resize:
            _SCREAMING_SNAKE_CASE							   =[self.resize(image=_a							, size=_a							, crop_pct=_a							, resample=_a			) for image in images]

       if do_center_crop:
            _SCREAMING_SNAKE_CASE							   =[self.center_crop(image=_a							, size=_a			) for image in images]

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

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

       _SCREAMING_SNAKE_CASE							   =[to_channel_dimension_format(_a							, _a			) for image in images]

       _SCREAMING_SNAKE_CASE							   ={'pixel_values': images}
       return BatchFeature(data=_a							, tensor_type=_a			)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
