


'''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												=						logging.get_logger(__name__)




class 				UpperCAmelCase			:


 def __init__(      self							:	Dict			,       __lowerCamelCase							:	str = None			,       __lowerCamelCase							:	uuid.UUID = None			,       __lowerCamelCase							:	Tuple=None			,       __lowerCamelCase							:	Union[str, Any]=None ):
    if not conversation_id:
       UpperCAmelCase__	:int      =      uuid.uuida()
    if past_user_inputs is None:
       UpperCAmelCase__	:Tuple      =      []
    if generated_responses is None:
       UpperCAmelCase__	:Dict      =      []

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


 def __eq__(      self							:	Optional[int]			,       __lowerCamelCase							:	List[Any] ):
    if not isinstance(__lowerCamelCase			,       __lowerCamelCase ):
       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        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	bool = False ):
    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__	:Optional[int]      =      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__	:Union[str, Any]      =      text


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    if self.new_user_input:
       self.past_user_inputs.append(self.new_user_input )
    UpperCAmelCase__	:Tuple      =      None


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	str ):
    self.generated_responses.append(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    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] ):
    UpperCAmelCase__	:str      =      f'''Conversation id: {self.uuid} \n'''
    for is_user, text in self.iter_texts():
       UpperCAmelCase__	: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[str]			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Optional[int] ):
    super().__init__(*__lowerCamelCase			,       **__lowerCamelCase )
    if self.tokenizer.pad_token_id is None:
       UpperCAmelCase__	:Optional[Any]      =      self.tokenizer.eos_token


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	List[Any]=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	str=None			,       **__lowerCamelCase							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      {}
    UpperCAmelCase__	:str      =      {}
    UpperCAmelCase__	:Optional[Any]      =      {}

    if min_length_for_response is not None:
       UpperCAmelCase__	:Dict      =      min_length_for_response
    if minimum_tokens is not None:
       UpperCAmelCase__	:int      =      minimum_tokens

    if "max_length" in generate_kwargs:
       UpperCAmelCase__	:Optional[int]      =      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__	:str      =      clean_up_tokenization_spaces

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


 def __call__(      self							:	List[Any]			,       __lowerCamelCase							:	Union[Conversation, List[Conversation]]			,       __lowerCamelCase							:	Any=0			,       **__lowerCamelCase							:	str ):
    UpperCAmelCase__	:List[Any]      =      super().__call__(__lowerCamelCase			,       num_workers=__lowerCamelCase			,       **__lowerCamelCase )
    if isinstance(__lowerCamelCase			,       __lowerCamelCase ) and len(__lowerCamelCase ) == 1:
       return outputs[0]
    return outputs


 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Conversation			,       __lowerCamelCase							:	str=3_2 ):
    if not isinstance(__lowerCamelCase			,       __lowerCamelCase ):
       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__	:Optional[Any]      =      self.tokenizer._build_conversation_input_ids(__lowerCamelCase )
    else:
       # If the tokenizer cannot handle conversations, we default to only the old version
       UpperCAmelCase__	:Dict      =      self._legacy_parse_and_tokenize(__lowerCamelCase )

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Union[str, Any]=1_0			,       **__lowerCamelCase							:	Any ):
    UpperCAmelCase__	:int      =      generate_kwargs.get('''max_length'''			,       self.model.config.max_length )

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	int=True ):
    UpperCAmelCase__	:Dict      =      model_outputs['''output_ids''']
    UpperCAmelCase__	:Optional[Any]      =      self.tokenizer.decode(
        output_ids[0]			,       skip_special_tokens=__lowerCamelCase			,       clean_up_tokenization_spaces=__lowerCamelCase			,       )
    UpperCAmelCase__	:Optional[Any]      =      model_outputs['''conversation''']
    conversation.mark_processed()
    conversation.append_response(__lowerCamelCase )
    return conversation





 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       __lowerCamelCase							:	Conversation ):
    UpperCAmelCase__	:Optional[Any]      =      self.tokenizer.eos_token_id
    UpperCAmelCase__	:List[str]      =      []
    for is_user, text in conversation.iter_texts():
       if eos_token_id is not None:
          input_ids.extend(self.tokenizer.encode(__lowerCamelCase			,       add_special_tokens=__lowerCamelCase ) + [eos_token_id] )
       else:
          input_ids.extend(self.tokenizer.encode(__lowerCamelCase			,       add_special_tokens=__lowerCamelCase ) )

    if len(__lowerCamelCase ) > self.tokenizer.model_max_length:
       UpperCAmelCase__	:Optional[Any]      =      input_ids[-self.tokenizer.model_max_length :]
    return input_ids







'''simple docstring'''





class 				UpperCAmelCase			:


 def __init__(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:dict[str, TrieNode]      =      {}  # Mapping from char to TrieNode
    UpperCAmelCase__	:Union[str, Any]      =      False


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	list[str] ):
    for word in words:
       self.insert(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Tuple      =      self
    for char in word:
       if char not in curr.nodes:
          UpperCAmelCase__	:Optional[int]      =      TrieNode()
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    UpperCAmelCase__	:Optional[int]      =      True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:str      =      self
    for char in word:
       if char not in curr.nodes:
          return False
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    return curr.is_leaf





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):

    def _delete(__lowerCamelCase							:	TrieNode			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int ) -> bool:
       if index == len(__lowerCamelCase ):
          # If word does not exist
          if not curr.is_leaf:
             return False
          UpperCAmelCase__	:int      =      False
          return len(curr.nodes ) == 0
       UpperCAmelCase__	:str      =      word[index]
       UpperCAmelCase__	:Optional[Any]      =      curr.nodes.get(__lowerCamelCase )
       # If char not in current trie node
       if not char_node:
          return False
       # Flag to check if node can be deleted
       UpperCAmelCase__	:Any      =      _delete(__lowerCamelCase			,       __lowerCamelCase			,       index + 1 )
       if delete_curr:
          del curr.nodes[char]
          return len(curr.nodes ) == 0
       return delete_curr

    _delete(self			,       __lowerCamelCase			,       0 )


def        a__     (					UpperCamelCase_						:      TrieNode,   UpperCamelCase_						:      str						):
   if node.is_leaf:
      print(UpperCamelCase_,   end=''' '''						)

   for key, value in node.nodes.items():
      print_words(UpperCamelCase_,   word + key						)


def        a__     (					):
   UpperCAmelCase__	:Union[str, Any]      =      '''banana bananas bandana band apple all beast'''.split()
   UpperCAmelCase__	:Union[str, Any]      =      TrieNode()
   root.insert_many(UpperCamelCase_						)
   # print_words(root, "")
   assert all(root.find(UpperCamelCase_						) for word in words						)
   assert root.find('''banana'''						)
   assert not root.find('''bandanas'''						)
   assert not root.find('''apps'''						)
   assert root.find('''apple'''						)
   assert root.find('''all'''						)
   root.delete('''all'''						)
   assert not root.find('''all'''						)
   root.delete('''banana'''						)
   assert not root.find('''banana'''						)
   assert root.find('''bananas'''						)
   return True


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      bool						):
   print(str(UpperCamelCase_						),   '''works!''' if passes else '''doesn\'t work :('''						)


def        a__     (					):
   assert test_trie()


def        a__     (					):
   print_results('''Testing trie functionality''',   test_trie()						)


if __name__ == "__main__":
    main()



