


"""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,
    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


__lowercase    =				logging.get_logger(__name__)


if is_vision_available():
 import PIL






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	["""pixel_values"""]






    def __init__( self    :   Dict    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   Dict[str, int] = None    ,			__UpperCAmelCase    :   PILImageResampling = PILImageResampling.BICUBIC    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   Dict[str, int] = None    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   Union[int, float] = 1 / 255    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			__UpperCAmelCase    :   bool = True    ,			**__UpperCAmelCase    :   str    ,			):
      super().__init__(**__UpperCAmelCase)
      a						:		List[str]											=      size if size is not None else {"shortest_edge": 224}
      a						:		List[str]											=      get_size_dict(__UpperCAmelCase    ,			default_to_square=__UpperCAmelCase)
      a						:		Union[str, Any]											=      crop_size if crop_size is not None else {"height": 224, "width": 224}
      a						:		List[Any]											=      get_size_dict(__UpperCAmelCase    ,			default_to_square=__UpperCAmelCase    ,			param_name="crop_size")

      a						:		List[Any]											=      do_resize
      a						:		Any											=      size
      a						:		Union[str, Any]											=      resample
      a						:		Union[str, Any]											=      do_center_crop
      a						:		str											=      crop_size
      a						:		List[str]											=      do_rescale
      a						:		int											=      rescale_factor
      a						:		Optional[Any]											=      do_normalize
      a						:		Union[str, Any]											=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a						:		Tuple											=      image_std if image_std is not None else OPENAI_CLIP_STD
      a						:		Union[str, Any]											=      do_convert_rgb






    def      __snake_case							( self    :   Optional[Any]    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Dict[str, int]    ,			__UpperCAmelCase    :   PILImageResampling = PILImageResampling.BICUBIC    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   Any    ,			):
      a						:		Tuple											=      get_size_dict(__UpperCAmelCase    ,			default_to_square=__UpperCAmelCase)
      if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
      a						:		Optional[Any]											=      get_resize_output_image_size(__UpperCAmelCase    ,			size=size["shortest_edge"]    ,			default_to_square=__UpperCAmelCase)
      return resize(__UpperCAmelCase    ,			size=__UpperCAmelCase    ,			resample=__UpperCAmelCase    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Dict[str, int]    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   Any    ,			):
      a						:		Tuple											=      get_size_dict(__UpperCAmelCase)
      if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
      return center_crop(__UpperCAmelCase    ,			size=(size["height"], size["width"])    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Union[int, float]    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      return rescale(__UpperCAmelCase    ,			scale=__UpperCAmelCase    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Union[float, List[float]]    ,			__UpperCAmelCase    :   Union[float, List[float]]    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   str    ,			):
      return normalize(__UpperCAmelCase    ,			mean=__UpperCAmelCase    ,			std=__UpperCAmelCase    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   ImageInput    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   Dict[str, int] = None    ,			__UpperCAmelCase    :   PILImageResampling = None    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   int = None    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   float = None    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   Optional[Union[str, TensorType]] = None    ,			__UpperCAmelCase    :   Optional[ChannelDimension] = ChannelDimension.FIRST    ,			**__UpperCAmelCase    :   Union[str, Any]    ,			):
      a						:		str											=      do_resize if do_resize is not None else self.do_resize
      a						:		Dict											=      size if size is not None else self.size
      a						:		Tuple											=      get_size_dict(__UpperCAmelCase    ,			param_name="size"    ,			default_to_square=__UpperCAmelCase)
      a						:		Tuple											=      resample if resample is not None else self.resample
      a						:		Union[str, Any]											=      do_center_crop if do_center_crop is not None else self.do_center_crop
      a						:		Dict											=      crop_size if crop_size is not None else self.crop_size
      a						:		str											=      get_size_dict(__UpperCAmelCase    ,			param_name="crop_size"    ,			default_to_square=__UpperCAmelCase)
      a						:		Tuple											=      do_rescale if do_rescale is not None else self.do_rescale
      a						:		Optional[int]											=      rescale_factor if rescale_factor is not None else self.rescale_factor
      a						:		Tuple											=      do_normalize if do_normalize is not None else self.do_normalize
      a						:		List[str]											=      image_mean if image_mean is not None else self.image_mean
      a						:		List[Any]											=      image_std if image_std is not None else self.image_std
      a						:		Tuple											=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a						:		List[str]											=      make_list_of_images(__UpperCAmelCase)

      if not valid_images(__UpperCAmelCase):
        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:
        a						:		List[Any]											=      [convert_to_rgb(__UpperCAmelCase) for image in images]

      # All transformations expect numpy arrays.
      a						:		List[str]											=      [to_numpy_array(__UpperCAmelCase) for image in images]

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

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

      if do_rescale:
        a						:		Optional[int]											=      [self.rescale(image=__UpperCAmelCase    ,			scale=__UpperCAmelCase) for image in images]

      if do_normalize:
        a						:		List[Any]											=      [self.normalize(image=__UpperCAmelCase    ,			mean=__UpperCAmelCase    ,			std=__UpperCAmelCase) for image in images]

      a						:		Dict											=      [to_channel_dimension_format(__UpperCAmelCase    ,			__UpperCAmelCase) for image in images]

      a						:		Any											=      {"pixel_values": images}
      return BatchFeature(data=__UpperCAmelCase    ,			tensor_type=__UpperCAmelCase)










"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







  assert masked_input.count("<mask>"	) == 1
  a						:		List[str]											=      torch.tensor(tokenizer.encode(A_  ,       add_special_tokens=A_	)	).unsqueeze(0	)  # Batch size 1
  a						:		Dict											=      model(A_	)[0]  # The last hidden-state is the first element of the output tuple
  a						:		int											=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a						:		Optional[Any]											=      logits[0, masked_index, :]
  a						:		Dict											=      logits.softmax(dim=0	)
  a							,		a						:		Any											=      prob.topk(k=A_  ,       dim=0	)
  a						:		Optional[Any]											=      " ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A_	)	)]	)
  a						:		str											=      tokenizer.mask_token
  a						:		Any											=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "	)	):
    a						:		Dict											=      predicted_token_bpe.replace("\u2581"  ,       " "	)
    if " {0}".format(A_	) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(A_	)  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(A_  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
  return topk_filled_outputs


__lowercase    =				CamembertTokenizer.from_pretrained("""camembert-base""")
__lowercase    =				CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__lowercase    =				"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))






