



'''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


_UpperCamelCase							 =	logging.get_logger(__name__)



class _A     :





		def __init__(   self       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase=None       ,				__UpperCAmelCase=None )				->   Any:

					'''simple docstring'''
					if not conversation_id:
								__UpperCAmelCase      :					Optional[int]   =   uuid.uuida()
					if past_user_inputs is None:
								__UpperCAmelCase      :					Any   =   []
					if generated_responses is None:
								__UpperCAmelCase      :					Any   =   []

					__UpperCAmelCase      :					List[Any]   =   conversation_id
					__UpperCAmelCase      :					Tuple   =   past_user_inputs
					__UpperCAmelCase      :					Dict   =   generated_responses
					__UpperCAmelCase      :					Optional[Any]   =   text





		def __eq__(   self       ,				__UpperCAmelCase )				->   Optional[int]:

					'''simple docstring'''
					if not isinstance(__UpperCAmelCase       ,				__UpperCAmelCase ):
								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 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase = False )				->   Optional[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}".' )
											__UpperCAmelCase      :					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:
								__UpperCAmelCase      :					int   =   text





		def 							__A				(   self )				->   str:

					'''simple docstring'''
					if self.new_user_input:
								self.past_user_inputs.append(self.new_user_input )
					__UpperCAmelCase      :					str   =   None





		def 							__A				(   self       ,				__UpperCAmelCase )				->   Any:

					'''simple docstring'''
					self.generated_responses.append(__UpperCAmelCase )





		def 							__A				(   self )				->   Optional[int]:

					'''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 )				->   Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Dict   =   f'Conversation id: {self.uuid} \n'
					for is_user, text in self.iter_texts():
								__UpperCAmelCase      :					List[str]   =   """user""" if is_user else """bot"""
								output += f'{name} >> {text} \n'
					return output



@add_end_docstrings(
    __A		, R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "		, )
class _A     (			__A			):





		def __init__(   self       ,				*__UpperCAmelCase       ,				**__UpperCAmelCase )				->   Union[str, Any]:

					'''simple docstring'''
					super().__init__(*__UpperCAmelCase       ,				**__UpperCAmelCase )
					if self.tokenizer.pad_token_id is None:
								__UpperCAmelCase      :					Union[str, Any]   =   self.tokenizer.eos_token





		def 							__A				(   self       ,				__UpperCAmelCase=None       ,				__UpperCAmelCase=None       ,				__UpperCAmelCase=None       ,				**__UpperCAmelCase )				->   Any:

					'''simple docstring'''
					__UpperCAmelCase      :					str   =   {}
					__UpperCAmelCase      :					Optional[Any]   =   {}
					__UpperCAmelCase      :					Optional[int]   =   {}

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

					if "max_length" in generate_kwargs:
								__UpperCAmelCase      :					Dict   =   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:
								__UpperCAmelCase      :					int   =   clean_up_tokenization_spaces

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





		def __call__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=0       ,				**__UpperCAmelCase )				->   Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   super().__call__(__UpperCAmelCase       ,				num_workers=__UpperCAmelCase       ,				**__UpperCAmelCase )
					if isinstance(__UpperCAmelCase       ,				__UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
								return outputs[0]
					return outputs





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=32 )				->   Dict[str, Any]:

					'''simple docstring'''
					if not isinstance(__UpperCAmelCase       ,				__UpperCAmelCase ):
								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""" ):
								__UpperCAmelCase      :					int   =   self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
					else:
								# If the tokenizer cannot handle conversations, we default to only the old version
								__UpperCAmelCase      :					Optional[Any]   =   self._legacy_parse_and_tokenize(__UpperCAmelCase )

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





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=10       ,				**__UpperCAmelCase )				->   List[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   generate_kwargs.get("""max_length"""       ,				self.model.config.max_length )

					__UpperCAmelCase      :					Any   =   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})' )
								__UpperCAmelCase      :					str   =   max_length - minimum_tokens
								__UpperCAmelCase      :					List[str]   =   model_inputs["""input_ids"""][:, -trim:]
								if "attention_mask" in model_inputs:
											__UpperCAmelCase      :					List[Any]   =   model_inputs["""attention_mask"""][:, -trim:]
					__UpperCAmelCase      :					str   =   model_inputs.pop("""conversation""" )
					__UpperCAmelCase      :					int   =   max_length
					__UpperCAmelCase      :					Union[str, Any]   =   self.model.generate(**__UpperCAmelCase       ,				**__UpperCAmelCase )
					if self.model.config.is_encoder_decoder:
								__UpperCAmelCase      :					str   =   1
					else:
								__UpperCAmelCase      :					Union[str, Any]   =   n
					return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=True )				->   Any:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   model_outputs["""output_ids"""]
					__UpperCAmelCase      :					str   =   self.tokenizer.decode(
					    output_ids[0]       ,				skip_special_tokens=__UpperCAmelCase       ,				clean_up_tokenization_spaces=__UpperCAmelCase       ,				)
					__UpperCAmelCase      :					Optional[Any]   =   model_outputs["""conversation"""]
					conversation.mark_processed()
					conversation.append_response(__UpperCAmelCase )
					return conversation





		def 							__A				(   self       ,				__UpperCAmelCase )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					Tuple   =   self.tokenizer.eos_token_id
					__UpperCAmelCase      :					Optional[Any]   =   []
					for is_user, text in conversation.iter_texts():
								if eos_token_id is not None:
											input_ids.extend(self.tokenizer.encode(__UpperCAmelCase       ,				add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
								else:
											input_ids.extend(self.tokenizer.encode(__UpperCAmelCase       ,				add_special_tokens=__UpperCAmelCase ) )

					if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
								__UpperCAmelCase      :					List[str]   =   input_ids[-self.tokenizer.model_max_length :]
					return input_ids





"""simple docstring"""




from __future__ import annotations

from typing import Generic, TypeVar

__lowerCamelCase							 =     TypeVar("T")
class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self						,__UpperCAmelCase   )    -> None:
													A__		   =							data
													A__		   =							self
													A__		   =							0






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# map from node name to the node object
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# create a new set with x as its member
													A__		   =							DisjointSetTreeNode(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> DisjointSetTreeNode[T]:
													# find the set x belongs to (with path-compression)
													A__		   =							self.map[data]
													if elem_ref != elem_ref.parent:
																				A__		   =							self.find_set(elem_ref.parent.data   )
													return elem_ref.parent





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# helper function for union operation
													if nodea.rank > nodea.rank:
																				A__		   =							nodea
													else:
																				A__		   =							nodea
																				if nodea.rank == nodea.rank:
																											nodea.rank += 1





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# merge 2 disjoint sets
													self.link(self.find_set(__UpperCAmelCase   )						,self.find_set(__UpperCAmelCase   )   )






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# connections: map from the node to the neighbouring nodes (with weights)
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# add a node ONLY if its not present in the graph
													if node not in self.connections:
																				A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# add an edge with the given weight
													self.add_node(__UpperCAmelCase   )
													self.add_node(__UpperCAmelCase   )
													A__		   =							weight
													A__		   =							weight





						def  snake_case__		(				self   )    -> GraphUndirectedWeighted[T]:
													A__		   =							[]
													A__		   =							set()
													for start in self.connections:
																				for end in self.connections[start]:
																											if (start, end) not in seen:
																																		seen.add((end, start)   )
																																		edges.append((start, end, self.connections[start][end])   )
													edges.sort(key=lambda __UpperCAmelCase   : x[2]   )

													# creating the disjoint set
													A__		   =							DisjointSetTree[T]()
													for node in self.connections:
																				disjoint_set.make_set(__UpperCAmelCase   )

													# MST generation
													A__		   =							0
													A__		   =							0
													A__		   =							GraphUndirectedWeighted[T]()
													while num_edges < len(self.connections   ) - 1:
																				A__   ,     A__   ,     A__		   =							edges[index]
																				index += 1
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				if parent_u != parent_v:
																											num_edges += 1
																											graph.add_edge(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
																											disjoint_set.union(__UpperCAmelCase						,__UpperCAmelCase   )
													return graph





