






'''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        _UpperCAmelCase						:




     def __init__(			self  :       List[Any]  ,     a  :       str = None  ,     a  :       uuid.UUID = None  ,     a  :       List[Any]=None  ,     a  :       int=None			):






        '''simple docstring'''






        if not conversation_id:
           lowercase_	:    Any           =     uuid.uuida()
        if past_user_inputs is None:
           lowercase_	:    List[str]           =     []
        if generated_responses is None:
           lowercase_	:    Optional[Any]           =     []

        lowercase_	:    Tuple           =     conversation_id
        lowercase_	:    str           =     past_user_inputs
        lowercase_	:    Optional[Any]           =     generated_responses
        lowercase_	:    Any           =     text




     def __eq__(			self  :       Optional[Any]  ,     a  :       Optional[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 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       str  ,     a  :       bool = False			):






        '''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}\"."""			)
              lowercase_	:    Tuple           =     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:
           lowercase_	:    Any           =     text




     def 				lowerCAmelCase__				(			self  :       Optional[int]			):






        '''simple docstring'''






        if self.new_user_input:
           self.past_user_inputs.append(self.new_user_input			)
        lowercase_	:    List[Any]           =     None




     def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       str			):






        '''simple docstring'''






        self.generated_responses.append(lowercase_			)




     def 				lowerCAmelCase__				(			self  :       List[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'''






        lowercase_	:    Union[str, Any]           =     f"""Conversation id: {self.uuid} \n"""
        for is_user, text in self.iter_texts():
           lowercase_	:    Any           =     "user" if is_user else "bot"
           output += f"""{name} >> {text} \n"""
        return output


@add_end_docstrings(
    snake_case				,					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        _UpperCAmelCase						(		snake_case			):




     def __init__(			self  :       List[Any]  ,     *a  :       Optional[Any]  ,     **a  :       str			):






        '''simple docstring'''






        super().__init__(*lowercase_  ,     **lowercase_			)
        if self.tokenizer.pad_token_id is None:
           lowercase_	:    List[Any]           =     self.tokenizer.eos_token




     def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Union[str, Any]=None  ,     a  :       int=None  ,     a  :       str=None  ,     **a  :       str			):






        '''simple docstring'''






        lowercase_	:    Union[str, Any]           =     {}
        lowercase_	:    Union[str, Any]           =     {}
        lowercase_	:    List[Any]           =     {}

        if min_length_for_response is not None:
           lowercase_	:    List[str]           =     min_length_for_response
        if minimum_tokens is not None:
           lowercase_	:    Optional[int]           =     minimum_tokens

        if "max_length" in generate_kwargs:
           lowercase_	:    Union[str, Any]           =     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:
           lowercase_	:    Union[str, Any]           =     clean_up_tokenization_spaces

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




     def __call__(			self  :       Any  ,     a  :       Union[Conversation, List[Conversation]]  ,     a  :       str=0  ,     **a  :       Union[str, Any]			):






        '''simple docstring'''






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




     def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Conversation  ,     a  :       Any=3_2			):






        '''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"			):
           lowercase_	:    Dict           =     self.tokenizer._build_conversation_input_ids(lowercase_			)
        else:
           # If the tokenizer cannot handle conversations, we default to only the old version
           lowercase_	:    Dict           =     self._legacy_parse_and_tokenize(lowercase_			)

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




     def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Any  ,     a  :       Optional[int]=1_0  ,     **a  :       Dict			):






        '''simple docstring'''






        lowercase_	:    Any           =     generate_kwargs.get("max_length"  ,     self.model.config.max_length			)

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




     def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       Union[str, Any]  ,     a  :       int=True			):






        '''simple docstring'''






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





     def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Conversation			):






        '''simple docstring'''






        lowercase_	:    Union[str, Any]           =     self.tokenizer.eos_token_id
        lowercase_	:    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:
           lowercase_	:    List[str]           =     input_ids[-self.tokenizer.model_max_length :]
        return input_ids












'''simple docstring'''
import heapq




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    list[list]           =     []

   # for each node and his adjacency list add them and the rank of the node to queue
   # using heapq module the queue will be filled like a Priority Queue
   # heapq works with a min priority queue, so I used -1*len(v) to build it
   for key, value in graph.items():
      # O(log(n))
      heapq.heappush(_UpperCamelCase						,		[-1 * len(_UpperCamelCase							), (key, value)]							)

   # chosen_vertices = set of chosen vertices
   lowercase_	:    Optional[Any]           =     set()

   # while queue isn't empty and there are still edges
   #   (queue[0][0] is the rank of the node with max rank)
   while queue and queue[0][0] != 0:
      # extract vertex with max rank from queue and add it to chosen_vertices
      lowercase_	:    Any           =     heapq.heappop(_UpperCamelCase							)[1][0]
      chosen_vertices.add(_UpperCamelCase							)

      # Remove all arcs adjacent to argmax
      for elem in queue:
         # if v haven't adjacent node, skip
         if elem[0] == 0:
            continue
         # if argmax is reachable from elem
         # remove argmax from elem's adjacent list and update his rank
         if argmax in elem[1][1]:
            lowercase_	:    str           =     elem[1][1].index(_UpperCamelCase							)
            del elem[1][1][index]
            elem[0] += 1
        # re-order the queue
      heapq.heapify(_UpperCamelCase							)
   return chosen_vertices


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCamelCase__ =			{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




