


'''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


__UpperCAmelCase       =							logging.get_logger(__name__)


if is_vision_available():
		import PIL




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				["pixel_values"]







							def __init__(		self ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = PILImageResampling.BICUBIC ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   lowerCamelCase_ = 1 / 2_55 ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   **lowerCamelCase_ ,   )	->							None:
														super().__init__(**lowerCamelCase_       )
														lowerCAmelCase__	       =     size if size is not None else {'''shortest_edge''': 2_24}
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_ ,   default_to_square=lowerCamelCase_       )
														lowerCAmelCase__	       =     crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_ ,   default_to_square=lowerCamelCase_ ,   param_name='''crop_size'''       )

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = PILImageResampling.BICUBIC ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_ ,   default_to_square=lowerCamelCase_       )
														if "shortest_edge" not in size:
																					raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
														lowerCAmelCase__	       =     get_resize_output_image_size(lowerCamelCase_ ,   size=size['''shortest_edge'''] ,   default_to_square=lowerCamelCase_       )
														return resize(lowerCamelCase_ ,   size=lowerCamelCase_ ,   resample=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														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(lowerCamelCase_ ,   size=(size['''height'''], size['''width''']) ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							List[Any]:
														return rescale(lowerCamelCase_ ,   scale=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														return normalize(lowerCamelCase_ ,   mean=lowerCamelCase_ ,   std=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = ChannelDimension.FIRST ,   **lowerCamelCase_ ,   )	->							PIL.Image.Image:
														lowerCAmelCase__	       =     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase__	       =     size if size is not None else self.size
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_ ,   param_name='''size''' ,   default_to_square=lowerCamelCase_       )
														lowerCAmelCase__	       =     resample if resample is not None else self.resample
														lowerCAmelCase__	       =     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase__	       =     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_ ,   param_name='''crop_size''' ,   default_to_square=lowerCamelCase_       )
														lowerCAmelCase__	       =     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase__	       =     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase__	       =     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase__	       =     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase__	       =     image_std if image_std is not None else self.image_std
														lowerCAmelCase__	       =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														lowerCAmelCase__	       =     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:
																					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:
																					lowerCAmelCase__	       =     [convert_to_rgb(lowerCamelCase_       ) for image in images]

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

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

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

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

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

														lowerCAmelCase__	       =     [to_channel_dimension_format(lowerCamelCase_ ,   lowerCamelCase_       ) for image in images]

														lowerCAmelCase__	       =     {'''pixel_values''': images}
														return BatchFeature(data=lowerCamelCase_ ,   tensor_type=lowerCamelCase_       )



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     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


__UpperCAmelCase       =							CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase       =							CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__UpperCAmelCase       =							'''Le camembert est <mask> :)'''






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