

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 				__UpperCamelCase				:







	def __init__(   self: Dict  , __UpperCamelCase: str = None  , __UpperCamelCase: uuid.UUID = None  , __UpperCamelCase: Union[str, Any]=None  , __UpperCamelCase: List[Any]=None      ):

						'''simple docstring'''





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

						__magic_name__							=    conversation_id
						__magic_name__							=    past_user_inputs
						__magic_name__							=    generated_responses
						__magic_name__							=    text







	def __eq__(   self: List[Any]  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						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 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: str  , __UpperCamelCase: bool = 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}".'      )
																__magic_name__							=    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:
											__magic_name__							=    text







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: str      ):

						'''simple docstring'''





						self.generated_responses.append(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''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: str      ):

						'''simple docstring'''





						__magic_name__							=    F'Conversation id: {self.uuid} \n'
						for is_user, text in self.iter_texts():
											__magic_name__							=    '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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __init__(   self: Tuple  , *__UpperCamelCase: Any  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						super().__init__(*__UpperCamelCase  , **__UpperCamelCase      )
						if self.tokenizer.pad_token_id is None:
											__magic_name__							=    self.tokenizer.eos_token







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: Tuple=None  , __UpperCamelCase: str=None  , __UpperCamelCase: Optional[int]=None  , **__UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    {}
						__magic_name__							=    {}
						__magic_name__							=    {}

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

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

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







	def __call__(   self: str  , __UpperCamelCase: Union[Conversation, List[Conversation]]  , __UpperCamelCase: int=0  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    super().__call__(__UpperCamelCase  , num_workers=__UpperCamelCase  , **__UpperCamelCase      )
						if isinstance(__UpperCamelCase  , __UpperCamelCase      ) and len(__UpperCamelCase      ) == 1:
											return outputs[0]
						return outputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Conversation  , __UpperCamelCase: Tuple=32      ):

						'''simple docstring'''





						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'      ):
											__magic_name__							=    self.tokenizer._build_conversation_input_ids(__UpperCamelCase      )
						else:
											# If the tokenizer cannot handle conversations, we default to only the old version
											__magic_name__							=    self._legacy_parse_and_tokenize(__UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: List[Any]=10  , **__UpperCamelCase: int      ):

						'''simple docstring'''





						__magic_name__							=    generate_kwargs.get('max_length'  , self.model.config.max_length      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: Tuple  , __UpperCamelCase: Dict=True      ):

						'''simple docstring'''





						__magic_name__							=    model_outputs['output_ids']
						__magic_name__							=    self.tokenizer.decode(
						    output_ids[0]  , skip_special_tokens=__UpperCamelCase  , clean_up_tokenization_spaces=__UpperCamelCase  , )
						__magic_name__							=    model_outputs['conversation']
						conversation.mark_processed()
						conversation.append_response(__UpperCamelCase      )
						return conversation







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: Conversation      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer.eos_token_id
						__magic_name__							=    []
						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:
											__magic_name__							=    input_ids[-self.tokenizer.model_max_length :]
						return input_ids







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration




def 					_lowercase     (				a_				:					str					,a_				:					str					,a_				:					str					,a_				:					Path					,a_				:					str = None					,a_				:					str = None					,a_				:					str = None					,)				->	Tuple:




					'''simple docstring'''







					if config_name_or_path is None:
										__magic_name__							=    'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

					if generator_tokenizer_name_or_path is None:
										__magic_name__							=    generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										__magic_name__							=    question_encoder_name_or_path

					__magic_name__							=    RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

					# Save model.
					__magic_name__							=    RagConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)

					__magic_name__							=    gen_config
					__magic_name__							=    question_encoder_config

					__magic_name__							=    model_class.from_pretrained_question_encoder_generator(
					    a_					,a_					,config=a_				)
					rag_model.save_pretrained(a_				)

					# Sanity check.
					model_class.from_pretrained(a_				)

					# Save tokenizers.
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'				)
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'				)


if __name__ == "__main__":
						A__						=							argparse.ArgumentParser()
						parser.add_argument(
						    "--model_type",
						    choices=["rag_sequence", "rag_token"],
						    required=True,
						    type=str,
						    help="RAG model type: rag_sequence, rag_token",
						)
						parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
						parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
						parser.add_argument(
						    "--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
						)

						parser.add_argument(
						    "--generator_tokenizer_name_or_path",
						    type=str,
						    help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
						)
						parser.add_argument(
						    "--question_encoder_tokenizer_name_or_path",
						    type=str,
						    help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
						)
						parser.add_argument(
						    "--config_name_or_path",
						    type=str,
						    help=(
						        "Identifier of the model config to use, if not provided, resolves to a base config for a given"
						        " ``model_type``"
						    ),
						)

						A__						=							parser.parse_args()

						A__						=							Path(args.dest)
						dest_dir.mkdir(exist_ok=True)

						consolidate(
						    args.model_type,
						    args.generator_name_or_path,
						    args.question_encoder_name_or_path,
						    dest_dir,
						    args.config_name_or_path,
						    args.generator_tokenizer_name_or_path,
						    args.question_encoder_tokenizer_name_or_path,
						)




