

from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
						from ..models.auto import AutoTokenizer

class 				__UpperCamelCase				:







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Optional[int]      ):

					'''simple docstring'''





					raise NotImplementedError()







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

					'''simple docstring'''





					raise NotImplementedError()




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __init__(   self: List[Any]  , __UpperCamelCase: "AutoTokenizer"  , __UpperCamelCase: bool = False  , **__UpperCamelCase: int      ):

					'''simple docstring'''





					__magic_name__							=    tokenizer
					__magic_name__							=    skip_prompt
					__magic_name__							=    decode_kwargs

					# variables used in the streaming process
					__magic_name__							=    []
					__magic_name__							=    0
					__magic_name__							=    True







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Optional[Any]      ):

					'''simple docstring'''





					if len(value.shape      ) > 1 and value.shape[0] > 1:
									raise ValueError('TextStreamer only supports batch size 1'      )
					elif len(value.shape      ) > 1:
									__magic_name__							=    value[0]

					if self.skip_prompt and self.next_tokens_are_prompt:
									__magic_name__							=    False
									return

					# Add the new token to the cache and decodes the entire thing.
					self.token_cache.extend(value.tolist()      )
					__magic_name__							=    self.tokenizer.decode(self.token_cache  , **self.decode_kwargs      )

					# After the symbol for a new line, we flush the cache.
					if text.endswith('\n'      ):
									__magic_name__							=    text[self.print_len :]
									__magic_name__							=    []
									__magic_name__							=    0
					# If the last token is a CJK character, we print the characters.
					elif len(__UpperCamelCase      ) > 0 and self._is_chinese_char(ord(text[-1]      )      ):
									__magic_name__							=    text[self.print_len :]
									self.print_len += len(__UpperCamelCase      )
					# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
					# which may change with the subsequent token -- there are probably smarter ways to do this!)
					else:
									__magic_name__							=    text[self.print_len : text.rfind(' '      ) + 1]
									self.print_len += len(__UpperCamelCase      )

					self.on_finalized_text(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

					'''simple docstring'''





					if len(self.token_cache      ) > 0:
									__magic_name__							=    self.tokenizer.decode(self.token_cache  , **self.decode_kwargs      )
									__magic_name__							=    text[self.print_len :]
									__magic_name__							=    []
									__magic_name__							=    0
					else:
									__magic_name__							=    ''

					__magic_name__							=    True
					self.on_finalized_text(__UpperCamelCase  , stream_end=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: str  , __UpperCamelCase: bool = False      ):

					'''simple docstring'''





					print(__UpperCamelCase  , flush=__UpperCamelCase  , end='' if not stream_end else None      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Optional[int]      ):

					'''simple docstring'''





					if (
					    (cp >= 0X4e00 and cp <= 0X9fff)
					    or (cp >= 0X3400 and cp <= 0X4dbf)  #
					    or (cp >= 0X20000 and cp <= 0X2a6df)  #
					    or (cp >= 0X2a700 and cp <= 0X2b73f)  #
					    or (cp >= 0X2b740 and cp <= 0X2b81f)  #
					    or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
					    or (cp >= 0Xf900 and cp <= 0Xfaff)
					    or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
					):  #
									return True

					return False




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __init__(   self: List[Any]  , __UpperCamelCase: "AutoTokenizer"  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional[float] = None  , **__UpperCamelCase: Optional[Any]      ):

					'''simple docstring'''





					super().__init__(__UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      )
					__magic_name__							=    Queue()
					__magic_name__							=    None
					__magic_name__							=    timeout







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: str  , __UpperCamelCase: bool = False      ):

					'''simple docstring'''





					self.text_queue.put(__UpperCamelCase  , timeout=self.timeout      )
					if stream_end:
									self.text_queue.put(self.stop_signal  , timeout=self.timeout      )







	def __iter__(   self: Optional[Any]      ):

					'''simple docstring'''





					return self







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

					'''simple docstring'''





					__magic_name__							=    self.text_queue.get(timeout=self.timeout      )
					if value == self.stop_signal:
									raise StopIteration()
					else:
									return value







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,
						)




