



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_	       =			logging.get_logger(__name__)






class 						__a :



	def __init__(						self							:    List[Any]				,	snake_case_							:    str = None				,	snake_case_							:    uuid.UUID = None				,	snake_case_							:    List[Any]=None				,	snake_case_							:    int=None)->							Dict:







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

						__lowerCAmelCase	   =conversation_id
						__lowerCAmelCase	   =past_user_inputs
						__lowerCAmelCase	   =generated_responses
						__lowerCAmelCase	   =text



	def __eq__(						self							:    Optional[Any]				,	snake_case_							:    Optional[Any])->							List[Any]:







						if not isinstance(lowercase_				,	lowercase_):
											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							:    List[Any]				,	snake_case_							:    str				,	snake_case_							:    bool = False)->							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}\".""")
																__lowerCAmelCase	   =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:
											__lowerCAmelCase	   =text



	def 		UpperCamelCase							(						self							:    Optional[int])->							List[Any]:







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



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    str)->							Optional[Any]:
						self.generated_responses.append(lowercase_)



	def 		UpperCamelCase							(						self							:    List[Any])->							Optional[int]:







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







@add_end_docstrings(
    SCREAMING_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 						__a (     SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    List[Any]				,	*snake_case_							:    Optional[Any]				,	**snake_case_							:    str)->							List[str]:
						super().__init__(*lowercase_				,	**lowercase_)
						if self.tokenizer.pad_token_id is None:
											__lowerCAmelCase	   =self.tokenizer.eos_token



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Union[str, Any]=None				,	snake_case_							:    int=None				,	snake_case_							:    str=None				,	**snake_case_							:    str)->							Tuple:
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   ={}

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

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

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



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



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    Conversation				,	snake_case_							:    Any=32)->							Dict[str, Any]:







						if not isinstance(lowercase_				,	lowercase_):
											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"""):
											__lowerCAmelCase	   =self.tokenizer._build_conversation_input_ids(lowercase_)
						else:
											# If the tokenizer cannot handle conversations, we default to only the old version
											__lowerCAmelCase	   =self._legacy_parse_and_tokenize(lowercase_)

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



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Any				,	snake_case_							:    Optional[int]=10				,	**snake_case_							:    Dict)->							List[str]:
						__lowerCAmelCase	   =generate_kwargs.get("""max_length"""				,	self.model.config.max_length)

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



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    int=True)->							List[Any]:
						__lowerCAmelCase	   =model_outputs["""output_ids"""]
						__lowerCAmelCase	   =self.tokenizer.decode(
						    output_ids[0]				,	skip_special_tokens=lowercase_				,	clean_up_tokenization_spaces=lowercase_				,	)
						__lowerCAmelCase	   =model_outputs["""conversation"""]
						conversation.mark_processed()
						conversation.append_response(lowercase_)
						return conversation



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Conversation)->							Dict:
						__lowerCAmelCase	   =self.tokenizer.eos_token_id
						__lowerCAmelCase	   =[]
						for is_user, text in conversation.iter_texts():
											if eos_token_id is not None:
																input_ids.extend(self.tokenizer.encode(lowercase_				,	add_special_tokens=lowercase_) + [eos_token_id])
											else:
																input_ids.extend(self.tokenizer.encode(lowercase_				,	add_special_tokens=lowercase_))

						if len(lowercase_) > self.tokenizer.model_max_length:
											__lowerCAmelCase	   =input_ids[-self.tokenizer.model_max_length :]
						return input_ids









def 					__lowerCAmelCase		(  )  ->						Tuple:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =1

					while len(__lowerCamelCase	) < 1E6:
										constant.append(str(__lowerCamelCase	)	)
										i += 1

					__lowerCAmelCase	   ="""""".join(__lowerCamelCase	)

					return (
					    int(constant[0]	)
					    * int(constant[9]	)
					    * int(constant[99]	)
					    * int(constant[999]	)
					    * int(constant[9999]	)
					    * int(constant[99999]	)
					    * int(constant[999999]	)
					)


if __name__ == "__main__":
				print(solution())




