import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=5	):
    '''simple docstring'''







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









import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


UpperCamelCase								=	None
UpperCamelCase								=	"""<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
UpperCamelCase								=	[
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]






@dataclass
class   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    True
       snake_case      =    None
       # Automatically constructed
       snake_case      =    "PIL.Image.Image"
       snake_case      =    pa.struct({"bytes": pa.binary(), "path": pa.string()}			)
       snake_case      =    field(default="Image"					,							init=UpperCamelCase					,							repr=UpperCamelCase			)



       def __call__(      self		)->int:




         '''simple docstring'''





         return self.pa_type



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->dict:




         '''simple docstring'''





         if config.PIL_AVAILABLE:
           import PIL.Image
         else:
           raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

         if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				Optional[int]		     =					np.array(_SCREAMING_SNAKE_CASE		)

         if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           return {"path": value, "bytes": None}
         elif isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           return {"path": None, "bytes": value}
         elif isinstance(_SCREAMING_SNAKE_CASE				,       np.ndarray		):
           # convert the image array to PNG/TIFF bytes
           return encode_np_array(_SCREAMING_SNAKE_CASE		)
         elif isinstance(_SCREAMING_SNAKE_CASE				,       PIL.Image.Image		):
           # convert the PIL image to bytes (default format is PNG/TIFF)
           return encode_pil_image(_SCREAMING_SNAKE_CASE		)
         elif value.get('''path'''		) is not None and os.path.isfile(value['''path''']		):
           # we set "bytes": None to not duplicate the data if they're already available locally
           return {"bytes": None, "path": value.get('''path'''		)}
         elif value.get('''bytes'''		) is not None or value.get('''path'''		) is not None:
           # store the image bytes, and path is used to infer the image format using the file extension
           return {"bytes": value.get('''bytes'''		), "path": value.get('''path'''		)}
         else:
           raise ValueError(
               F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None		)->"PIL.Image.Image":




         '''simple docstring'''





         if not self.decode:
           raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''		)

         if config.PIL_AVAILABLE:
           import PIL.Image
         else:
           raise ImportError('''To support decoding images, please install \'Pillow\'.'''		)

         if token_per_repo_id is None:
           A_      :				List[str]		     =					{}

         A_     ,       A_      :				str		     =					value['''path'''], value['''bytes''']
         if bytes_ is None:
           if path is None:
             raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''		)
           else:
             if is_local_path(_SCREAMING_SNAKE_CASE		):
               A_      :				List[str]		     =					PIL.Image.open(_SCREAMING_SNAKE_CASE		)
             else:
               A_      :				List[str]		     =					path.split('''::'''		)[-1]
               try:
                 A_      :				int		     =					string_to_dict(_SCREAMING_SNAKE_CASE				,       config.HUB_DATASETS_URL		)['''repo_id''']
                 A_      :				Optional[int]		     =					token_per_repo_id.get(_SCREAMING_SNAKE_CASE		)
               except ValueError:
                 A_      :				Any		     =					None
               with xopen(_SCREAMING_SNAKE_CASE				,       '''rb'''				,       use_auth_token=_SCREAMING_SNAKE_CASE		) as f:
                 A_      :				Optional[Any]		     =					BytesIO(f.read()		)
               A_      :				Dict		     =					PIL.Image.open(bytes_		)
         else:
           A_      :				Optional[int]		     =					PIL.Image.open(BytesIO(bytes_		)		)
         image.load()  # to avoid "Too many open files" errors
         return image



       def        _snake_case       (      self		)->Union["FeatureType", Dict[str, "FeatureType"]]:




         '''simple docstring'''





         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value('''binary'''		),
                 "path": Value('''string'''		),
             }
         )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->pa.StructArray:




         '''simple docstring'''





         if pa.types.is_string(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           A_      :				List[Any]		     =					pa.StructArray.from_arrays([bytes_array, storage]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[str]		     =					pa.StructArray.from_arrays([storage, path_array]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_struct(storage.type		):
           if storage.type.get_field_index('''bytes'''		) >= 0:
             A_      :				Tuple		     =					storage.field('''bytes'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           if storage.type.get_field_index('''path'''		) >= 0:
             A_      :				Optional[Any]		     =					storage.field('''path'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				Union[str, Any]		     =					pa.StructArray.from_arrays([bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
           A_      :				Optional[Any]		     =					pa.array(
               [encode_np_array(np.array(_SCREAMING_SNAKE_CASE		)		)['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]				,       type=pa.binary()				,       )
           A_      :				str		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[Any]		     =					pa.StructArray.from_arrays(
               [bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=bytes_array.is_null()		)
         return array_cast(_SCREAMING_SNAKE_CASE				,       self.pa_type		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->pa.StructArray:




         '''simple docstring'''





         @no_op_if_value_is_null
         def path_to_bytes(_SCREAMING_SNAKE_CASE		):
           with xopen(_SCREAMING_SNAKE_CASE				,       '''rb'''		) as f:
             A_      :				Any		     =					f.read()
           return bytes_

         A_      :				Dict		     =					pa.array(
             [
                 (path_to_bytes(x['''path''']		) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                 for x in storage.to_pylist()
             ]				,       type=pa.binary()				,       )
         A_      :				List[Any]		     =					pa.array(
             [os.path.basename(_SCREAMING_SNAKE_CASE		) if path is not None else None for path in storage.field('''path'''		).to_pylist()]				,       type=pa.string()				,       )
         A_      :				str		     =					pa.StructArray.from_arrays([bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=bytes_array.is_null()		)
         return array_cast(_SCREAMING_SNAKE_CASE				,       self.pa_type		)




def   _SCREAMING_SNAKE_CASE	( ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  global _IMAGE_COMPRESSION_FORMATS
  if _IMAGE_COMPRESSION_FORMATS is None:
    PIL.Image.init()
    A_      :				Dict		     =					list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
  return _IMAGE_COMPRESSION_FORMATS


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Dict		     =					BytesIO()
  if image.format in list_image_compression_formats():
    A_      :				Tuple		     =					image.format
  else:
    A_      :				List[str]		     =					'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
  image.save(SCREAMING_SNAKE_CASE ,			format=SCREAMING_SNAKE_CASE       )
  return buffer.getvalue()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if hasattr(SCREAMING_SNAKE_CASE ,			'''filename'''       ) and image.filename != "":
    return {"path": image.filename, "bytes": None}
  else:
    return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE       )}


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  A_      :				Union[str, Any]		     =					array.dtype
  A_      :				Dict		     =					dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
  A_      :				Any		     =					dtype.kind
  A_      :				Any		     =					dtype.itemsize

  A_      :				Dict		     =					None

  # Multi-channel array case (only np.dtype("|u1") is allowed)
  if array.shape[2:]:
    A_      :				List[Any]		     =					np.dtype('''|u1'''       )
    if dtype_kind not in ["u", "i"]:
      raise TypeError(
          f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''       )
    if dtype is not dest_dtype:
      warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''       )
    # Exact match
  elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
    A_      :				int		     =					dtype
  else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
    while dtype_itemsize >= 1:
      A_      :				Any		     =					dtype_byteorder + dtype_kind + str(SCREAMING_SNAKE_CASE       )
      A_      :				Optional[int]		     =					np.dtype(SCREAMING_SNAKE_CASE       )
      if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
        warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''       )
        break
      else:
        dtype_itemsize //= 2

  if dest_dtype is None:
    raise TypeError(
        f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''       )
  A_      :				Tuple		     =					PIL.Image.fromarray(array.astype(SCREAMING_SNAKE_CASE       )       )
  return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE       )}







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  if objs:
    A_     ,       A_      :				Union[str, Any]		     =					first_non_null_value(SCREAMING_SNAKE_CASE       )
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
    if isinstance(SCREAMING_SNAKE_CASE ,			np.ndarray       ):
      A_      :				Tuple		     =					no_op_if_value_is_null(SCREAMING_SNAKE_CASE       )
      return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE       ) for obj in objs]
    elif isinstance(SCREAMING_SNAKE_CASE ,			PIL.Image.Image       ):
      A_      :				List[str]		     =					no_op_if_value_is_null(SCREAMING_SNAKE_CASE       )
      return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE       ) for obj in objs]
    else:
      return objs
  else:
    return objs




