

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  :      int			 =logging.get_logger(__name__)

class __UpperCamelCase     :






					'''simple docstring'''



					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__:	Tuple									= []

						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    _UpperCAmelCase    (				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__:	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:
							UpperCAmelCase__:	List[Any]									= text





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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						self.generated_responses.append(lowerCamelCase__						)





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

@add_end_docstrings(
    _a					,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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      **lowerCamelCase__						):
						UpperCAmelCase__:	Optional[Any]									= {}
						UpperCAmelCase__:	List[Any]									= {}
						UpperCAmelCase__:	Optional[int]									= {}

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

						if "max_length" in generate_kwargs:
							UpperCAmelCase__:	List[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:
							UpperCAmelCase__:	Union[str, Any]									= 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__:	Optional[int]									= super().__call__(lowerCamelCase__				,      num_workers=lowerCamelCase__				,      **lowerCamelCase__						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						) and len(lowerCamelCase__						) == 1:
							return outputs[0]
						return outputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__=3_2						):
						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[Any]									= 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__:	Union[str, Any]									= torch.LongTensor([input_ids]						)
						elif self.framework == "tf":
							UpperCAmelCase__:	Tuple									= tf.constant([input_ids]						)
						return {"input_ids": input_ids, "conversation": conversation}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__=1_0				,      **lowerCamelCase__						):
						UpperCAmelCase__:	List[Any]									= 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__:	str									= model_inputs["input_ids"][:, -trim:]
							if "attention_mask" in model_inputs:
								UpperCAmelCase__:	Optional[int]									= model_inputs["attention_mask"][:, -trim:]
						UpperCAmelCase__:	Any									= model_inputs.pop("conversation"						)
						UpperCAmelCase__:	Tuple									= max_length
						UpperCAmelCase__:	List[Any]									= self.model.generate(**lowerCamelCase__				,      **lowerCamelCase__						)
						if self.model.config.is_encoder_decoder:
							UpperCAmelCase__:	List[Any]									= 1
						else:
							UpperCAmelCase__:	Dict									= n
						return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__=True						):
						UpperCAmelCase__:	Optional[Any]									= model_outputs["output_ids"]
						UpperCAmelCase__:	Any									= self.tokenizer.decode(
						    output_ids[0]				,      skip_special_tokens=lowerCamelCase__				,      clean_up_tokenization_spaces=lowerCamelCase__				,      )
						UpperCAmelCase__:	int									= model_outputs["conversation"]
						conversation.mark_processed()
						conversation.append_response(lowerCamelCase__						)
						return conversation





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= self.tokenizer.eos_token_id
						UpperCAmelCase__:	List[Any]									= []
						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__:	Tuple									= input_ids[-self.tokenizer.model_max_length :]
						return input_ids


import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_lowerCAmelCase  :      int			 ="""scheduler_config.json"""

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   1
					__magic_name__        =   2
					__magic_name__        =   3
					__magic_name__        =   4
					__magic_name__        =   5
					__magic_name__        =   6
					__magic_name__        =   7
					__magic_name__        =   8
					__magic_name__        =   9
					__magic_name__        =   1_0
					__magic_name__        =   1_1
					__magic_name__        =   1_2
					__magic_name__        =   1_3
					__magic_name__        =   1_4

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   SCHEDULER_CONFIG_NAME
					__magic_name__        =   []
					__magic_name__        =   True





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__				,      subfolder=lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      return_commit_hash=lowerCamelCase__				,      **lowerCamelCase__				,      )
						return cls.from_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = False				,      **lowerCamelCase__						):
						self.save_config(save_directory=lowerCamelCase__				,      push_to_hub=lowerCamelCase__				,      **lowerCamelCase__						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._get_compatibles()





					@classmethod
					def    _UpperCAmelCase    (				cls						):
						UpperCAmelCase__:	List[str]									= list(set([cls.__name__] + cls._compatibles						)						)
						UpperCAmelCase__:	Union[str, Any]									= importlib.import_module(__name__.split("."						)[0]						)
						UpperCAmelCase__:	int									= [
						    getattr(lowerCamelCase__				,      lowerCamelCase__						) for c in compatible_classes_str if hasattr(lowerCamelCase__				,      lowerCamelCase__						)
						]
						return compatible_classes