


'''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


__A	=logging.get_logger(__name__)


class 		_snake_case					:




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

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




					def __eq__(      self							,							_lowerCamelCase):
								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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = 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[Any]        =     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__  :					Dict        =     text




					def 				snake_case__						(      self):
								if self.new_user_input:
											self.past_user_inputs.append(self.new_user_input)
								UpperCAmelCase__  :					Optional[Any]        =     None




					def 				snake_case__						(      self							,							_lowerCamelCase):
								self.generated_responses.append(_lowerCamelCase)




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


@add_end_docstrings(
    a__       ,   R'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''       ,   )
class 		_snake_case					(     a__	):




					def __init__(      self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(*_lowerCamelCase							,							**_lowerCamelCase)
								if self.tokenizer.pad_token_id is None:
											UpperCAmelCase__  :					Any        =     self.tokenizer.eos_token




					def 				snake_case__						(      self							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							**_lowerCamelCase):
								UpperCAmelCase__  :					List[Any]        =     {}
								UpperCAmelCase__  :					Tuple        =     {}
								UpperCAmelCase__  :					Any        =     {}

								if min_length_for_response is not None:
											UpperCAmelCase__  :					Optional[Any]        =     min_length_for_response
								if minimum_tokens is not None:
											UpperCAmelCase__  :					Optional[Any]        =     minimum_tokens

								if "max_length" in generate_kwargs:
											UpperCAmelCase__  :					Dict        =     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__  :					Dict        =     clean_up_tokenization_spaces

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




					def __call__(      self							,							_lowerCamelCase							,							_lowerCamelCase=0							,							**_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     super().__call__(_lowerCamelCase							,							num_workers=_lowerCamelCase							,							**_lowerCamelCase)
								if isinstance(_lowerCamelCase							,							_lowerCamelCase) and len(_lowerCamelCase) == 1:
											return outputs[0]
								return outputs




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=32):
								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[int]        =     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__  :					Optional[int]        =     torch.LongTensor([input_ids])
								elif self.framework == "tf":
											UpperCAmelCase__  :					int        =     tf.constant([input_ids])
								return {"input_ids": input_ids, "conversation": conversation}




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=10							,							**_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     generate_kwargs.get("""max_length"""							,							self.model.config.max_length)

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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=True):
								UpperCAmelCase__  :					List[str]        =     model_outputs["""output_ids"""]
								UpperCAmelCase__  :					Tuple        =     self.tokenizer.decode(
								    output_ids[0]							,							skip_special_tokens=_lowerCamelCase							,							clean_up_tokenization_spaces=_lowerCamelCase							,							)
								UpperCAmelCase__  :					Any        =     model_outputs["""conversation"""]
								conversation.mark_processed()
								conversation.append_response(_lowerCamelCase)
								return conversation




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     self.tokenizer.eos_token_id
								UpperCAmelCase__  :					Optional[int]        =     []
								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__  :					Any        =     input_ids[-self.tokenizer.model_max_length :]
								return input_ids



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()