





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
       import tensorflow as tf

       from ..tf_utils import stable_softmax


if is_torch_available():
       import torch


snake_case__					:							Optional[Any]													=     logging.get_logger(__name__)





@add_end_docstrings(
    _lowerCamelCase ,      R"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """ ,      )
class A_					(			_lowerCamelCase   ):




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:GenericTensor )->					np.ndarray:
         if self.framework == "tf":
               __A          =				tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
         elif self.framework == "pt":
               __A          =				torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,   as_tuple=_UpperCamelCase )
         else:
               raise ValueError('''Unsupported framework''' )
         return masked_index




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:GenericTensor )->					np.ndarray:
         __A          =				self.get_masked_index(_UpperCamelCase )
         __A          =				np.prod(masked_index.shape )
         if numel < 1:
               raise PipelineException(
                   '''fill-mask'''    ,   self.model.base_model_prefix    ,   f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""    ,   )




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:GenericTensor )->					Optional[Any]:
         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ):
               for model_input in model_inputs:
                     self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0] )
         else:
               for input_ids in model_inputs["input_ids"]:
                     self._ensure_exactly_one_mask_token(_UpperCamelCase )




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:str=None    ,   **_UpperCamelCase	:int )->					Dict[str, GenericTensor]:
         if return_tensors is None:
               __A          =				self.framework
         __A          =				self.tokenizer(_UpperCamelCase    ,   return_tensors=_UpperCamelCase )
         self.ensure_exactly_one_mask_token(_UpperCamelCase )
         return model_inputs




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Optional[Any] )->					Optional[int]:
         __A          =				self.model(**_UpperCamelCase )
         __A          =				model_inputs['''input_ids''']
         return model_outputs




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:List[str]=5    ,   _UpperCamelCase	:str=None )->					Tuple:
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
               __A          =				target_ids.shape[0]
         __A          =				model_outputs['''input_ids'''][0]
         __A          =				model_outputs['''logits''']

         if self.framework == "tf":
               __A          =				tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]

               __A          =				outputs.numpy()

               __A          =				outputs[0, masked_index, :]
               __A          =				stable_softmax(_UpperCamelCase    ,   axis=-1 )
               if target_ids is not None:
                     __A          =				tf.gather_nd(tf.squeeze(_UpperCamelCase    ,   0 )    ,   target_ids.reshape(-1    ,   1 ) )
                     __A          =				tf.expand_dims(_UpperCamelCase    ,   0 )

               __A          =				tf.math.top_k(_UpperCamelCase    ,   k=_UpperCamelCase )
               __A   ,						__A          =				topk.values.numpy(), topk.indices.numpy()
         else:
               __A          =				torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,   as_tuple=_UpperCamelCase ).squeeze(-1 )
               # Fill mask pipeline supports only one ${mask_token} per sample

               __A          =				outputs[0, masked_index, :]
               __A          =				logits.softmax(dim=-1 )
               if target_ids is not None:
                     __A          =				probs[..., target_ids]

               __A   ,						__A          =				probs.topk(_UpperCamelCase )

         __A          =				[]
         __A          =				values.shape[0] == 1
         for i, (_values, _predictions) in enumerate(zip(values.tolist()    ,   predictions.tolist() ) ):
               __A          =				[]
               for v, p in zip(_values    ,   _predictions ):
                     # Copy is important since we're going to modify this array in place
                     __A          =				input_ids.numpy().copy()
                     if target_ids is not None:
                           __A          =				target_ids[p].tolist()

                     __A          =				p
                     # Filter padding out:
                     __A          =				tokens[np.where(tokens != self.tokenizer.pad_token_id )]
                     # Originally we skip special tokens to give readable output.
                     # For multi masks though, the other [MASK] would be removed otherwise
                     # making the output look odd, so we add them back
                     __A          =				self.tokenizer.decode(_UpperCamelCase    ,   skip_special_tokens=_UpperCamelCase )
                     __A          =				{'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p] ), '''sequence''': sequence}
                     row.append(_UpperCamelCase )
               result.append(_UpperCamelCase )
         if single_mask:
               return result[0]
         return result




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Any=None )->					Tuple:
         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ):
               __A          =				[targets]
         try:
               __A          =				self.tokenizer.get_vocab()
         except Exception:
               __A          =				{}
         __A          =				[]
         for target in targets:
               __A          =				vocab.get(_UpperCamelCase    ,   _UpperCamelCase )
               if id_ is None:
                     __A          =				self.tokenizer(
                         _UpperCamelCase    ,   add_special_tokens=_UpperCamelCase    ,   return_attention_mask=_UpperCamelCase    ,   return_token_type_ids=_UpperCamelCase    ,   max_length=1    ,   truncation=_UpperCamelCase    ,   )['''input_ids''']
                     if len(_UpperCamelCase ) == 0:
                           logger.warning(
                               f"""The specified target token `{target}` does not exist in the model vocabulary. """
                               '''We cannot replace it with anything meaningful, ignoring it''' )
                           continue
                     __A          =				input_ids[0]
                     # XXX: If users encounter this pass
                     # it becomes pretty slow, so let's make sure
                     # The warning enables them to fix the input to
                     # get faster performance.
                     logger.warning(
                         f"""The specified target token `{target}` does not exist in the model vocabulary. """
                         f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.""" )
               target_ids.append(id_ )
         __A          =				list(set(_UpperCamelCase ) )
         if len(_UpperCamelCase ) == 0:
               raise ValueError('''At least one target must be provided when passed.''' )
         __A          =				np.array(_UpperCamelCase )
         return target_ids




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:str=None    ,   _UpperCamelCase	:Optional[Any]=None )->					Tuple:
         __A          =				{}

         if targets is not None:
               __A          =				self.get_target_ids(_UpperCamelCase    ,   _UpperCamelCase )
               __A          =				target_ids

         if top_k is not None:
               __A          =				top_k

         if self.tokenizer.mask_token_id is None:
               raise PipelineException(
                   '''fill-mask'''    ,   self.model.base_model_prefix    ,   '''The tokenizer does not define a `mask_token`.''' )
         return {}, {}, postprocess_params






   def __call__(self	:Tuple    ,   _UpperCamelCase	:Any    ,   *_UpperCamelCase	:List[Any]    ,   **_UpperCamelCase	:List[str] )->					Optional[int]:
         __A          =				super().__call__(_UpperCamelCase    ,   **_UpperCamelCase )
         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) == 1:
               return outputs[0]
         return outputs












import os

snake_case__					:							Any													=     {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value



def _a				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

      __A          =				num // 10_00
      numerals += m_count * "M"
      num %= 10_00

      __A          =				num // 1_00
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 1_00

      __A          =				num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals



def _a				(	lowerCamelCase:   str = "/p089_roman.txt" )	->						int:







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

      for line in lines:
            __A          =				line.strip()
            __A          =				parse_roman_numerals(lowerCamelCase )
            __A          =				generate_roman_numerals(lowerCamelCase )
            savings += len(lowerCamelCase ) - len(lowerCamelCase )

      return savings


if __name__ == "__main__":
       print(f'{solution() = }')





