
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


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

class __snake_case		:
    def __init__(  self		,snake_case = None		,snake_case = None		,snake_case=None		,snake_case=None				):



           '''simple docstring'''







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

           lowercase							:					uuid.UUID	     =				conversation_id
           lowercase							:					List[str]	     =				past_user_inputs
           lowercase							:					List[str]	     =				generated_responses
           lowercase							:					Optional[str]	     =				text
    def __eq__(  self		,snake_case				):



           '''simple docstring'''







           if not isinstance(snake_case		,snake_case				):
                  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		,snake_case		,snake_case = 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							:					Dict	     =				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							:					List[Any]	     =				text
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.new_user_input:
                  self.past_user_inputs.append(self.new_user_input				)
           lowercase							:					Dict	     =				None
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           self.generated_responses.append(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''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				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				f"Conversation id: {self.uuid} \n"
           for is_user, text in self.iter_texts():
                  lowercase							:					int	     =				"""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		,*snake_case		,**snake_case				):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case				)
           if self.tokenizer.pad_token_id is None:
                  lowercase							:					Any	     =				self.tokenizer.eos_token
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=None		,snake_case=None		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				{}
           lowercase							:					Dict	     =				{}
           lowercase							:					Optional[int]	     =				{}

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

           if "max_length" in generate_kwargs:
                  lowercase							:					List[str]	     =				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							:					str	     =				clean_up_tokenization_spaces

           if generate_kwargs:
                  forward_params.update(snake_case				)
           return preprocess_params, forward_params, postprocess_params
    def __call__(  self		,snake_case		,snake_case=0		,**snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				super().__call__(snake_case		,num_workers=snake_case		,**snake_case				)
           if isinstance(snake_case		,snake_case				) and len(snake_case				) == 1:
                  return outputs[0]
           return outputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=32				):



           '''simple docstring'''







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

           if self.framework == "pt":
                  lowercase							:					str	     =				torch.LongTensor([input_ids]				)
           elif self.framework == "tf":
                  lowercase							:					Dict	     =				tf.constant([input_ids]				)
           return {"input_ids": input_ids, "conversation": conversation}
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=10		,**snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				generate_kwargs.get("""max_length"""		,self.model.config.max_length				)

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



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				model_outputs["""output_ids"""]
           lowercase							:					Tuple	     =				self.tokenizer.decode(
               output_ids[0]		,skip_special_tokens=snake_case		,clean_up_tokenization_spaces=snake_case		,)
           lowercase							:					List[str]	     =				model_outputs["""conversation"""]
           conversation.mark_processed()
           conversation.append_response(snake_case				)
           return conversation
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.tokenizer.eos_token_id
           lowercase							:					Dict	     =				[]
           for is_user, text in conversation.iter_texts():
                  if eos_token_id is not None:
                         input_ids.extend(self.tokenizer.encode(snake_case		,add_special_tokens=snake_case				) + [eos_token_id]				)
                  else:
                         input_ids.extend(self.tokenizer.encode(snake_case		,add_special_tokens=snake_case				)				)

           if len(snake_case				) > self.tokenizer.model_max_length:
                  lowercase							:					int	     =				input_ids[-self.tokenizer.model_max_length :]
           return input_ids


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 						_snake_case(  )			->   tuple[list[int], int]:
       lowercase							:					List[Any]	     =				[randint(-1_000 ,     1_000    ) for i in range(10    )]
       lowercase							:					Tuple	     =				randint(-5_000 ,     5_000    )
       return (arr, r)


lowercase  : List[Any]						   =							make_dataset()







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, ...]:
       for triplet in permutations(SCREAMING_SNAKE_CASE__ ,     3    ):
              if sum(SCREAMING_SNAKE_CASE__    ) == target:
                     return tuple(sorted(SCREAMING_SNAKE_CASE__    )    )
       return (0, 0, 0)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, int, int]:
       arr.sort()
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       for i in range(n - 1    ):
              lowercase     ,      lowercase							:					Optional[Any]	     =				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)







def 						_snake_case(  )			->   tuple[float, float]:
       lowercase							:					Dict	     =				"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
       lowercase							:					Tuple	     =				"""
triplet_sum1(*dataset)
"""
       lowercase							:					int	     =				"""
triplet_sum2(*dataset)
"""
       lowercase							:					str	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       lowercase							:					Dict	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       return (min(SCREAMING_SNAKE_CASE__    ), min(SCREAMING_SNAKE_CASE__    ))


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  lowercase  : Union[str, Any]						   =							solution_times()
  print(F'''The time for naive implementation is {times[0]}.''')
  print(F'''The time for optimized implementation is {times[1]}.''')
