



'''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


lowerCAmelCase_     :       str						=						logging.get_logger(__name__)
class 	lowerCamelCase_							:







						def __init__(  self   :      Dict				,    lowerCAmelCase__   :      Any = None				,    lowerCAmelCase__   :      List[str] = None				,    lowerCAmelCase__   :      List[str]=None				,    lowerCAmelCase__   :      Optional[int]=None				):

													"""simple docstring"""
													if not conversation_id:
																				SCREAMING_SNAKE_CASE					:      Any     					=			uuid.uuida()
													if past_user_inputs is None:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			[]
													if generated_responses is None:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			[]

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			conversation_id
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			past_user_inputs
													SCREAMING_SNAKE_CASE					:      Dict     					=			generated_responses
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			text







						def __eq__(  self   :      int				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													if not isinstance(A_				,    A_				):
																				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 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[Any] = 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					:      Union[str, 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:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			text







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													if self.new_user_input:
																				self.past_user_inputs.append(self.new_user_input				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													self.generated_responses.append(A_				)







						def 					__lowercase    (  self   :      Tuple				):

													"""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   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			F"""Conversation id: {self.uuid} \n"""
													for is_user, text in self.iter_texts():
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''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 	lowerCamelCase_							(					_SCREAMING_SNAKE_CASE			):







						def __init__(  self   :      Optional[int]				,    *lowerCAmelCase__   :      Any				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													super().__init__(*A_				,    **A_				)
													if self.tokenizer.pad_token_id is None:
																				SCREAMING_SNAKE_CASE					:      int     					=			self.tokenizer.eos_token







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      List[Any]=None				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      Optional[int]=None				,    **lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			{}
													SCREAMING_SNAKE_CASE					:      int     					=			{}
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{}

													if min_length_for_response is not None:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			min_length_for_response
													if minimum_tokens is not None:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			minimum_tokens

													if "max_length" in generate_kwargs:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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					:      Optional[Any]     					=			clean_up_tokenization_spaces

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







						def __call__(  self   :      Any				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict=0				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			super().__call__(A_				,    num_workers=A_				,    **A_				)
													if isinstance(A_				,    A_				) and len(A_				) == 1:
																				return outputs[0]
													return outputs







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]=32				):

													"""simple docstring"""
													if not isinstance(A_				,    A_				):
																				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					:      List[str]     					=			self.tokenizer._build_conversation_input_ids(A_				)
													else:
																				# If the tokenizer cannot handle conversations, we default to only the old version
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self._legacy_parse_and_tokenize(A_				)

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







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Any=10				,    **lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			generate_kwargs.get('''max_length'''				,    self.model.config.max_length				)

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







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Tuple=True				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			model_outputs['''output_ids''']
													SCREAMING_SNAKE_CASE					:      Any     					=			self.tokenizer.decode(
													    output_ids[0]				,    skip_special_tokens=A_				,    clean_up_tokenization_spaces=A_				,    )
													SCREAMING_SNAKE_CASE					:      Any     					=			model_outputs['''conversation''']
													conversation.mark_processed()
													conversation.append_response(A_				)
													return conversation







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.tokenizer.eos_token_id
													SCREAMING_SNAKE_CASE					:      List[str]     					=			[]
													for is_user, text in conversation.iter_texts():
																				if eos_token_id is not None:
																											input_ids.extend(self.tokenizer.encode(A_				,    add_special_tokens=A_				) + [eos_token_id]				)
																				else:
																											input_ids.extend(self.tokenizer.encode(A_				,    add_special_tokens=A_				)				)

													if len(A_				) > self.tokenizer.model_max_length:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			input_ids[-self.tokenizer.model_max_length :]
													return input_ids









'''simple docstring'''



from bisect import bisect
from itertools import accumulate
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Optional[Any]					,		A  :				Dict					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			sorted(zip(A					,		A				)					,		key=lambda A				: x[0] / x[1]					,		reverse=A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			[i[0] for i in r], [i[1] for i in r]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			list(accumulate(A				)				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			bisect(A					,		A				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




