



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


__snake_case     :Dict			       =logging.get_logger(__name__)



class        lowerCAmelCase__					:


				def __init__(							self      :  str						, __UpperCamelCase      :  str = None						, __UpperCamelCase      :  uuid.UUID = None						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  str=None       )		->     Optional[int]:
											if not conversation_id:
																		A      						=    uuid.uuida()
											if past_user_inputs is None:
																		A      						=    []
											if generated_responses is None:
																		A      						=    []

											A      						=    conversation_id
											A      						=    past_user_inputs
											A      						=    generated_responses
											A      						=    text


				def __eq__(							self      :  Tuple						, __UpperCamelCase      :  int       )		->     Union[str, Any]:
											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 __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  bool = False       )		->     Union[str, Any]:
											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}".'''       )
																									A      						=    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:
																		A      						=    text


				def __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input       )
											A      						=    None


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  str       )		->     List[Any]:
											self.generated_responses.append(__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     Union[str, Any]:
											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      :  Dict       )		->     Dict:
											A      						=    f'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		A      						=    '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        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  Tuple						, *__UpperCamelCase      :  Tuple						, **__UpperCamelCase      :  Tuple       )		->     str:
											super().__init__(*__UpperCamelCase						, **__UpperCamelCase       )
											if self.tokenizer.pad_token_id is None:
																		A      						=    self.tokenizer.eos_token


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  Optional[int]=None						, __UpperCamelCase      :  Optional[Any]=None						, **__UpperCamelCase      :  int       )		->     Optional[Any]:
											A      						=    {}
											A      						=    {}
											A      						=    {}

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

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

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


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


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Conversation						, __UpperCamelCase      :  Union[str, Any]=32       )		->     Dict[str, Any]:
											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'       ):
																		A      						=    self.tokenizer._build_conversation_input_ids(__UpperCamelCase       )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		A      						=    self._legacy_parse_and_tokenize(__UpperCamelCase       )

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


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Union[str, Any]=10						, **__UpperCamelCase      :  Dict       )		->     Any:
											A      						=    generate_kwargs.get('max_length'						, self.model.config.max_length       )

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


				def __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Any=True       )		->     int:
											A      						=    model_outputs['output_ids']
											A      						=    self.tokenizer.decode(
											    output_ids[0]						, skip_special_tokens=__UpperCamelCase						, clean_up_tokenization_spaces=__UpperCamelCase						, )
											A      						=    model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(__UpperCamelCase       )
											return conversation


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  Conversation       )		->     Dict:
											A      						=    self.tokenizer.eos_token_id
											A      						=    []
											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:
																		A      						=    input_ids[-self.tokenizer.model_max_length :]
											return input_ids

"""simple docstring"""




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  stooge(_UpperCamelCase			,					0			,					len(_UpperCamelCase    ) - 1    )
  return arr




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _lowercase			,					_lowercase:      Optional[Any]   			=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _lowercase:      Tuple   			=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )

    # Recursively sort last 2/3 elements
    stooge(_UpperCamelCase			,					i + t			,					(_UpperCamelCase)    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )


if __name__ == "__main__":
  A__ :       Dict				= input('Enter numbers separated by a comma:\n').strip()
  A__ :       Optional[Any]				= [int(item) for item in user_input.split(',')]
  print(stooge_sort(unsorted))
