




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


UpperCAmelCase_		:				List[str]									=     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 		__A      (			UpperCamelCase__			):

						def     A__						(		self   :Optional[int]    , __snake_case   :GenericTensor ):
										'''simple docstring'''

										if self.framework == "tf":
														__magic_name__	:	Dict								=tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
										elif self.framework == "pt":
														__magic_name__	:	Tuple								=torch.nonzero(input_ids == self.tokenizer.mask_token_id    , as_tuple=__snake_case )
										else:
														raise ValueError("""Unsupported framework""" )
										return masked_index

						def     A__						(		self   :Any    , __snake_case   :GenericTensor ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.get_masked_index(__snake_case )
										__magic_name__	:	Any								=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     A__						(		self   :Optional[Any]    , __snake_case   :GenericTensor ):
										'''simple docstring'''

										if isinstance(__snake_case    , __snake_case ):
														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(__snake_case )

						def     A__						(		self   :int    , __snake_case   :List[Any]    , __snake_case   :int=None    , **__snake_case   :Optional[Any] ):
										'''simple docstring'''

										if return_tensors is None:
														__magic_name__	:	int								=self.framework
										__magic_name__	:	List[str]								=self.tokenizer(__snake_case    , return_tensors=__snake_case )
										self.ensure_exactly_one_mask_token(__snake_case )
										return model_inputs

						def     A__						(		self   :Dict    , __snake_case   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.model(**__snake_case )
										__magic_name__	:	Union[str, Any]								=model_inputs["""input_ids"""]
										return model_outputs

						def     A__						(		self   :List[Any]    , __snake_case   :Dict    , __snake_case   :Dict=5    , __snake_case   :int=None ):
										'''simple docstring'''

										if target_ids is not None and target_ids.shape[0] < top_k:
														__magic_name__	:	int								=target_ids.shape[0]
										__magic_name__	:	Dict								=model_outputs["""input_ids"""][0]
										__magic_name__	:	str								=model_outputs["""logits"""]

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

														__magic_name__	:	Any								=outputs.numpy()

														__magic_name__	:	Optional[Any]								=outputs[0, masked_index, :]
														__magic_name__	:	Any								=stable_softmax(__snake_case    , axis=-1 )
														if target_ids is not None:
																		__magic_name__	:	List[Any]								=tf.gather_nd(tf.squeeze(__snake_case    , 0 )    , target_ids.reshape(-1    , 1 ) )
																		__magic_name__	:	Any								=tf.expand_dims(__snake_case    , 0 )

														__magic_name__	:	str								=tf.math.top_k(__snake_case    , k=__snake_case )
														__magic_name__				, __magic_name__	:	Optional[Any]								=topk.values.numpy(), topk.indices.numpy()
										else:
														__magic_name__	:	List[Any]								=torch.nonzero(input_ids == self.tokenizer.mask_token_id    , as_tuple=__snake_case ).squeeze(-1 )
														# Fill mask pipeline supports only one ${mask_token} per sample

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

														__magic_name__				, __magic_name__	:	Optional[int]								=probs.topk(__snake_case )

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

																		__magic_name__	:	str								=p
																		# Filter padding out:
																		__magic_name__	:	str								=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
																		__magic_name__	:	Tuple								=self.tokenizer.decode(__snake_case    , skip_special_tokens=__snake_case )
																		__magic_name__	:	Tuple								={"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p] ), """sequence""": sequence}
																		row.append(__snake_case )
														result.append(__snake_case )
										if single_mask:
														return result[0]
										return result

						def     A__						(		self   :Tuple    , __snake_case   :Any    , __snake_case   :str=None ):
										'''simple docstring'''

										if isinstance(__snake_case    , __snake_case ):
														__magic_name__	:	Optional[Any]								=[targets]
										try:
														__magic_name__	:	Union[str, Any]								=self.tokenizer.get_vocab()
										except Exception:
														__magic_name__	:	str								={}
										__magic_name__	:	Dict								=[]
										for target in targets:
														__magic_name__	:	Any								=vocab.get(__snake_case    , __snake_case )
														if id_ is None:
																		__magic_name__	:	Optional[Any]								=self.tokenizer(
																		    __snake_case    , add_special_tokens=__snake_case    , return_attention_mask=__snake_case    , return_token_type_ids=__snake_case    , max_length=1    , truncation=__snake_case    , )["""input_ids"""]
																		if len(__snake_case ) == 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
																		__magic_name__	:	Union[str, Any]								=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_ )
										__magic_name__	:	Any								=list(set(__snake_case ) )
										if len(__snake_case ) == 0:
														raise ValueError("""At least one target must be provided when passed.""" )
										__magic_name__	:	Tuple								=np.array(__snake_case )
										return target_ids

						def     A__						(		self   :Optional[Any]    , __snake_case   :Optional[Any]=None    , __snake_case   :Dict=None ):
										'''simple docstring'''

										__magic_name__	:	Any								={}

										if targets is not None:
														__magic_name__	:	List[str]								=self.get_target_ids(__snake_case    , __snake_case )
														__magic_name__	:	Optional[int]								=target_ids

										if top_k is not None:
														__magic_name__	:	Union[str, Any]								=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   :Union[str, Any]    , __snake_case   :Dict    , *__snake_case   :List[Any]    , **__snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=super().__call__(__snake_case    , **__snake_case )
										if isinstance(__snake_case    , __snake_case ) and len(__snake_case ) == 1:
														return outputs[0]
										return outputs










def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=[]
				__magic_name__	:	int								=[]
				__magic_name__	:	str								={
				    """^""": 3,
				    """*""": 2,
				    """/""": 2,
				    """%""": 2,
				    """+""": 1,
				    """-""": 1,
				}  # Priority of each operator
				__magic_name__	:	Dict								=len(lowerCamelCase   ) if (len(lowerCamelCase   ) > 7) else 7

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

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

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

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

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




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(lowerCamelCase   )   ):
								if infix[i] == "(":
												__magic_name__	:	str								=""")"""  # change "(" to ")"
								elif infix[i] == ")":
												__magic_name__	:	int								="""("""  # change ")" to "("

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


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




