




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


_lowerCamelCase     :		Union[str, Any]								=       logging.get_logger(__name__)


@add_end_docstrings(
    UpperCAmelCase__		, 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     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     GenericTensor)						->np.ndarray:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     GenericTensor)						->np.ndarray:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     GenericTensor)						->Optional[Any]:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     List[Any]=None ,			**UpperCAmelCase__				:     Optional[Any])						->Dict[str, GenericTensor]:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   self.model(**UpperCAmelCase__)
           A__							=   model_inputs['''input_ids''']
           return model_outputs


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     str=5 ,			UpperCAmelCase__				:     Any=None)						->Any:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     int=None)						->List[Any]:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     str=None ,			UpperCAmelCase__				:     Union[str, Any]=None)						->str:





           '''simple docstring'''

           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				:     int ,			UpperCAmelCase__				:     Dict ,			*UpperCAmelCase__				:     Any ,			**UpperCAmelCase__				:     Union[str, Any])						->Any:





           '''simple docstring'''

           A__							=   super().__call__(UpperCAmelCase__ ,			**UpperCAmelCase__)
           if isinstance(UpperCAmelCase__ ,			UpperCAmelCase__) and len(UpperCAmelCase__) == 1:
                  return outputs[0]
           return outputs








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   []
       A__							=   []
       A__							=   {
           '''^''': 3,
           '''*''': 2,
           '''/''': 2,
           '''%''': 2,
           '''+''': 1,
           '''-''': 1,
       }  # Priority of each operator
       A__							=   len(lowercase_						) if (len(lowercase_						) > 7) else 7

       # Print table header for output
       print(
           '''Symbol'''.center(8						)			,    '''Stack'''.center(lowercase_						)			,    '''Postfix'''.center(lowercase_						)			,    sep=''' | '''			,    )
       print('''-''' * (print_width * 3 + 7)						)

       for x in infix:
              if x.isalpha() or x.isdigit():
                     post_fix.append(lowercase_						)  # if x is Alphabet / Digit, add it to Postfix
              elif x == "(":
                     stack.append(lowercase_						)  # if x is "(" push to Stack
              elif x == ")":  # if x is ")" pop stack until "(" is encountered
                     while stack[-1] != "(":
                            post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
                     stack.pop()
              else:
                     if len(lowercase_						) == 0:
                            stack.append(lowercase_						)  # If stack is empty, push x to stack
                     else:  # while priority of x is not > priority of element in the stack
                            while len(lowercase_						) > 0 and priority[x] <= priority[stack[-1]]:
                                   post_fix.append(stack.pop()						)  # pop stack & add to Postfix
                            stack.append(lowercase_						)  # push x to stack

              print(
                  x.center(8						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    sep=''' | '''			,    )  # Output in tabular format

       while len(lowercase_						) > 0:  # while stack is not empty
              post_fix.append(stack.pop()						)  # pop stack & add to Postfix
              print(
                  ''' '''.center(8						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    sep=''' | '''			,    )  # Output in tabular format

       return "".join(lowercase_						)  # return Postfix as str







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   list(infix[::-1]						)  # reverse the infix equation

       for i in range(len(lowercase_						)						):
              if infix[i] == "(":
                     A__							=   ''')'''  # change "(" to ")"
              elif infix[i] == ")":
                     A__							=   '''('''  # change ")" to "("

       return (infix_2_postfix(''''''.join(lowercase_						)						))[
           ::-1
       ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
  _lowerCamelCase     :		List[str]								=       input("""\nEnter an Infix Equation = """)  # Input an Infix equation
  _lowerCamelCase     :		Dict								=       """""".join(Infix.split())  # Remove spaces from the input
  print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")


