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    :				int							     =	logging.get_logger(__name__)







class __snake_case       :




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



     """simple docstring"""






     if not conversation_id:
       SCREAMING_SNAKE_CASE__    							=   uuid.uuida()
     if past_user_inputs is None:
       SCREAMING_SNAKE_CASE__    							=   []
     if generated_responses is None:
       SCREAMING_SNAKE_CASE__    							=   []

     SCREAMING_SNAKE_CASE__    							=   conversation_id
     SCREAMING_SNAKE_CASE__    							=   past_user_inputs
     SCREAMING_SNAKE_CASE__    							=   generated_responses
     SCREAMING_SNAKE_CASE__    							=   text




   def __eq__(					self    :       Union[str, Any] ,			_lowercase    :       Union[str, Any]   ):



     """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    :       List[Any] ,			_lowercase    :       str ,			_lowercase    :       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}\"."""   )
         SCREAMING_SNAKE_CASE__    							=   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:
       SCREAMING_SNAKE_CASE__    							=   text




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     if self.new_user_input:
       self.past_user_inputs.append(self.new_user_input   )
     SCREAMING_SNAKE_CASE__    							=   None




   def 			__a		(					self    :       str ,			_lowercase    :       str   ):



     """simple docstring"""






     self.generated_responses.append(_UpperCamelCase   )




   def 			__a		(					self    :       str   ):



     """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"""






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







@add_end_docstrings(
    __lowerCAmelCase     ,	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 __snake_case       (		__lowerCAmelCase	):




   def __init__(					self    :       Tuple ,			*_lowercase    :       str ,			**_lowercase    :       Dict   ):



     """simple docstring"""






     super().__init__(*_UpperCamelCase ,			**_UpperCamelCase   )
     if self.tokenizer.pad_token_id is None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer.eos_token




   def 			__a		(					self    :       int ,			_lowercase    :       Union[str, Any]=None ,			_lowercase    :       Optional[Any]=None ,			_lowercase    :       Union[str, Any]=None ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {}
     SCREAMING_SNAKE_CASE__    							=   {}
     SCREAMING_SNAKE_CASE__    							=   {}

     if min_length_for_response is not None:
       SCREAMING_SNAKE_CASE__    							=   min_length_for_response
     if minimum_tokens is not None:
       SCREAMING_SNAKE_CASE__    							=   minimum_tokens

     if "max_length" in generate_kwargs:
       SCREAMING_SNAKE_CASE__    							=   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:
       SCREAMING_SNAKE_CASE__    							=   clean_up_tokenization_spaces

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




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



     """simple docstring"""






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




   def 			__a		(					self    :       Tuple ,			_lowercase    :       Conversation ,			_lowercase    :       Tuple=32   ):



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

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




   def 			__a		(					self    :       Dict ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Tuple=10 ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   generate_kwargs.get("""max_length""" ,			self.model.config.max_length   )

     SCREAMING_SNAKE_CASE__    							=   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})"""   )
       SCREAMING_SNAKE_CASE__    							=   max_length - minimum_tokens
       SCREAMING_SNAKE_CASE__    							=   model_inputs["""input_ids"""][:, -trim:]
       if "attention_mask" in model_inputs:
         SCREAMING_SNAKE_CASE__    							=   model_inputs["""attention_mask"""][:, -trim:]
     SCREAMING_SNAKE_CASE__    							=   model_inputs.pop("""conversation"""   )
     SCREAMING_SNAKE_CASE__    							=   max_length
     SCREAMING_SNAKE_CASE__    							=   self.model.generate(**_UpperCamelCase ,			**_UpperCamelCase   )
     if self.model.config.is_encoder_decoder:
       SCREAMING_SNAKE_CASE__    							=   1
     else:
       SCREAMING_SNAKE_CASE__    							=   n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       str=True   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   model_outputs["""output_ids"""]
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer.decode(
         output_ids[0] ,			skip_special_tokens=_UpperCamelCase ,			clean_up_tokenization_spaces=_UpperCamelCase ,			)
     SCREAMING_SNAKE_CASE__    							=   model_outputs["""conversation"""]
     conversation.mark_processed()
     conversation.append_response(_UpperCamelCase   )
     return conversation




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Conversation   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.tokenizer.eos_token_id
     SCREAMING_SNAKE_CASE__    							=   []
     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:
       SCREAMING_SNAKE_CASE__    							=   input_ids[-self.tokenizer.model_max_length :]
     return input_ids

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__lowerCamelCase    :				List[str]							     =	TypeVar('''KEY''')
__lowerCamelCase    :				int							     =	TypeVar('''VAL''')







@dataclass(frozen=lowerCamelCase_     ,	slots=lowerCamelCase_	)
class __snake_case       (		Generic[KEY, VAL]	):
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42







class __snake_case       (		_Item	):




   def __init__(					self    :       List[Any]   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )




   def __bool__(					self    :       List[str]   ):



     """simple docstring"""






     return False





__lowerCamelCase    :				str							     =	_DeletedItem()







class __snake_case       (		MutableMapping[KEY, VAL]	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       int = 8 ,			_lowercase    :       float = 0.75   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   initial_block_size
     SCREAMING_SNAKE_CASE__    							=   [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     SCREAMING_SNAKE_CASE__    							=   capacity_factor
     SCREAMING_SNAKE_CASE__    							=   0




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       KEY   ):



     """simple docstring"""






     return hash(_lowercase   ) % len(self._buckets   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       int   ):



     """simple docstring"""






     return (ind + 1) % len(self._buckets   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       int ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
     if not stored:
       SCREAMING_SNAKE_CASE__    							=   _Item(_lowercase ,			_lowercase   )
       self._len += 1
       return True
     elif stored.key == key:
       SCREAMING_SNAKE_CASE__    							=   _Item(_lowercase ,			_lowercase   )
       return True
     else:
       return False




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   len(self._buckets   ) * self._capacity_factor
     return len(self   ) >= int(_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     if len(self._buckets   ) <= self._initial_block_size:
       return False
     SCREAMING_SNAKE_CASE__    							=   len(self._buckets   ) * self._capacity_factor / 2
     return len(self   ) < limit




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._buckets
     SCREAMING_SNAKE_CASE__    							=   [None] * new_size
     SCREAMING_SNAKE_CASE__    							=   0
     for item in old_buckets:
       if item:
         self._add_item(item.key ,			item.val   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self._resize(len(self._buckets   ) * 2   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self._resize(len(self._buckets   ) // 2   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       KEY   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._get_bucket_index(_lowercase   )
     for _ in range(len(self._buckets   )   ):
       yield ind
       SCREAMING_SNAKE_CASE__    							=   self._get_next_ind(_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       if self._try_set(_lowercase ,			_lowercase ,			_lowercase   ):
         break




   def __setitem__(					self    :       List[str] ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     if self._is_full():
       self._size_up()

     self._add_item(_lowercase ,			_lowercase   )




   def __delitem__(					self    :       Tuple ,			_lowercase    :       KEY   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
       if item is None:
         raise KeyError(_lowercase   )
       if item is _deleted:
         continue
       if item.key == key:
         SCREAMING_SNAKE_CASE__    							=   _deleted
         self._len -= 1
         break
     if self._is_sparse():
       self._size_down()




   def __getitem__(					self    :       Tuple ,			_lowercase    :       KEY   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
       if item is None:
         break
       if item is _deleted:
         continue
       if item.key == key:
         return item.val
     raise KeyError(_lowercase   )




   def __len__(					self    :       List[Any]   ):



     """simple docstring"""






     return self._len




   def __iter__(					self    :       List[str]   ):



     """simple docstring"""






     yield from (item.key for item in self._buckets if item)




   def __repr__(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """ ,""".join(
         f"""{item.key}: {item.val}""" for item in self._buckets if item   )
     return f"""HashMap({val_string})"""
