



"""simple docstring"""





import uuid
from typing import Any, Dict, List, Optional, Union

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


if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch


__lowerCAmelCase							:       Optional[int]          =       logging.get_logger(__name__)



class  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase = None				,     _lowercase = None				,     _lowercase=None				,     _lowercase=None )			->    Optional[Any]:







								'''simple docstring'''

								if not conversation_id:
														snake_case_ :			Tuple            =      uuid.uuida()
								if past_user_inputs is None:
														snake_case_ :			List[str]            =      []
								if generated_responses is None:
														snake_case_ :			List[str]            =      []

								snake_case_ :			uuid.UUID            =      conversation_id
								snake_case_ :			List[str]            =      past_user_inputs
								snake_case_ :			List[str]            =      generated_responses
								snake_case_ :			Optional[str]            =      text





		def __eq__(       self				,     _lowercase )			->    Any:







								'''simple docstring'''

								if not isinstance(_lowercase				,     _lowercase ):
														return False
								if self.uuid == other.uuid:
														return True
								return (
								    self.new_user_input == other.new_user_input
								    and self.past_user_inputs == other.past_user_inputs
								    and self.generated_responses == other.generated_responses
								)





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = False )			->    int:







								'''simple docstring'''

								if self.new_user_input:
														if overwrite:
																				logger.warning(
																				    f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
																				    f'with: "{text}".' )
																				snake_case_ :			Union[str, Any]            =      text
														else:
																				logger.warning(
																				    f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
																				    f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input' )
								else:
														snake_case_ :			Union[str, Any]            =      text





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								if self.new_user_input:
														self.past_user_inputs.append(self.new_user_input )
								snake_case_ :			int            =      None





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Optional[int]:







								'''simple docstring'''

								self.generated_responses.append(_lowercase )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								for user_input, generated_response in zip(self.past_user_inputs				,     self.generated_responses ):
														yield True, user_input
														yield False, generated_response
								if self.new_user_input:
														yield True, self.new_user_input





		def __repr__(       self )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			int            =      f'Conversation id: {self.uuid} \n'
								for is_user, text in self.iter_texts():
														snake_case_ :			Tuple            =      """user""" if is_user else """bot"""
														output += f'{name} >> {text} \n'
								return output




@add_end_docstrings(
    SCREAMING_SNAKE_CASE__	,					r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''	,					)
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     *_lowercase				,     **_lowercase )			->    int:







								'''simple docstring'''

								super().__init__(*_lowercase				,     **_lowercase )
								if self.tokenizer.pad_token_id is None:
														snake_case_ :			str            =      self.tokenizer.eos_token





		def  UpperCAmelCase__				(       self				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     **_lowercase )			->    str:







								'''simple docstring'''

								snake_case_ :			int            =      {}
								snake_case_ :			Tuple            =      {}
								snake_case_ :			Optional[Any]            =      {}

								if min_length_for_response is not None:
														snake_case_ :			str            =      min_length_for_response
								if minimum_tokens is not None:
														snake_case_ :			Union[str, Any]            =      minimum_tokens

								if "max_length" in generate_kwargs:
														snake_case_ :			Tuple            =      generate_kwargs["""max_length"""]
														# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
								if clean_up_tokenization_spaces is not None:
														snake_case_ :			Dict            =      clean_up_tokenization_spaces

								if generate_kwargs:
														forward_params.update(_lowercase )
								return preprocess_params, forward_params, postprocess_params





		def __call__(       self				,     _lowercase				,     _lowercase=0				,     **_lowercase )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			str            =      super().__call__(_lowercase				,     num_workers=_lowercase				,     **_lowercase )
								if isinstance(_lowercase				,     _lowercase ) and len(_lowercase ) == 1:
														return outputs[0]
								return outputs





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=3_2 )			->    Dict[str, Any]:







								'''simple docstring'''

								if not isinstance(_lowercase				,     _lowercase ):
														raise ValueError("""ConversationalPipeline, expects Conversation as inputs""" )
								if conversation.new_user_input is None:
														raise ValueError(
														    f'Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '
														    """Add user inputs with the conversation's `add_user_input` method""" )
								if hasattr(self.tokenizer				,     """_build_conversation_input_ids""" ):
														snake_case_ :			Union[str, Any]            =      self.tokenizer._build_conversation_input_ids(_lowercase )
								else:
														# If the tokenizer cannot handle conversations, we default to only the old version
														snake_case_ :			List[str]            =      self._legacy_parse_and_tokenize(_lowercase )

								if self.framework == "pt":
														snake_case_ :			int            =      torch.LongTensor([input_ids] )
								elif self.framework == "tf":
														snake_case_ :			Optional[int]            =      tf.constant([input_ids] )
								return {"input_ids": input_ids, "conversation": conversation}





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=1_0				,     **_lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			Dict            =      generate_kwargs.get("""max_length"""				,     self.model.config.max_length )

								snake_case_ :			List[str]            =      model_inputs["""input_ids"""].shape[1]
								if max_length - minimum_tokens < n:
														logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})' )
														snake_case_ :			List[str]            =      max_length - minimum_tokens
														snake_case_ :			Dict            =      model_inputs["""input_ids"""][:, -trim:]
														if "attention_mask" in model_inputs:
																				snake_case_ :			List[Any]            =      model_inputs["""attention_mask"""][:, -trim:]
								snake_case_ :			List[Any]            =      model_inputs.pop("""conversation""" )
								snake_case_ :			Optional[Any]            =      max_length
								snake_case_ :			Optional[Any]            =      self.model.generate(**_lowercase				,     **_lowercase )
								if self.model.config.is_encoder_decoder:
														snake_case_ :			int            =      1
								else:
														snake_case_ :			str            =      n
								return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=True )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      model_outputs["""output_ids"""]
								snake_case_ :			List[Any]            =      self.tokenizer.decode(
								    output_ids[0]				,     skip_special_tokens=_lowercase				,     clean_up_tokenization_spaces=_lowercase				,     )
								snake_case_ :			List[Any]            =      model_outputs["""conversation"""]
								conversation.mark_processed()
								conversation.append_response(_lowercase )
								return conversation





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Any            =      self.tokenizer.eos_token_id
								snake_case_ :			List[Any]            =      []
								for is_user, text in conversation.iter_texts():
														if eos_token_id is not None:
																				input_ids.extend(self.tokenizer.encode(_lowercase				,     add_special_tokens=_lowercase ) + [eos_token_id] )
														else:
																				input_ids.extend(self.tokenizer.encode(_lowercase				,     add_special_tokens=_lowercase ) )

								if len(_lowercase ) > self.tokenizer.model_max_length:
														snake_case_ :			List[Any]            =      input_ids[-self.tokenizer.model_max_length :]
								return input_ids





'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





