






'''simple docstring'''

import argparse
from pathlib import Path

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






def 	_snake_case						(      A_       :    Optional[int]     ,		A_       :    List[Any]     ,		A_       :    Optional[int]     ,		A_       :    int     ,		A_       :    str = None     ,		A_       :    Optional[Any] = None     ,		A_       :    Optional[Any] = None     ,		):
  """simple docstring"""







  if config_name_or_path is None:
    a_      :	Union[str, Any]      =							"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

  if generator_tokenizer_name_or_path is None:
    a_      :	int      =							generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    a_      :	Optional[Any]      =							question_encoder_name_or_path

  a_      :	Optional[Any]      =							RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

  # Save model.
  a_      :	Tuple      =							RagConfig.from_pretrained(UpperCamelCase__   )
  a_      :	str      =							AutoConfig.from_pretrained(UpperCamelCase__   )
  a_      :	Union[str, Any]      =							AutoConfig.from_pretrained(UpperCamelCase__   )

  a_      :	Any      =							gen_config
  a_      :	List[str]      =							question_encoder_config

  a_      :	Tuple      =							model_class.from_pretrained_question_encoder_generator(
      UpperCamelCase__     ,		UpperCamelCase__     ,		config=UpperCamelCase__   )
  rag_model.save_pretrained(UpperCamelCase__   )

  # Sanity check.
  model_class.from_pretrained(UpperCamelCase__   )

  # Save tokenizers.
  a_      :	Optional[Any]      =							AutoTokenizer.from_pretrained(UpperCamelCase__   )
  gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""   )
  a_      :	Union[str, Any]      =							AutoTokenizer.from_pretrained(UpperCamelCase__   )
  question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""   )


if __name__ == "__main__":
    __snake_case:  List[Any]								=			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``"
        ),
    )

    __snake_case:  Optional[Any]								=			parser.parse_args()

    __snake_case:  Tuple								=			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,
    )




'''simple docstring'''

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""ylacombe/bark-small"""
       a_      :	Dict      =							tempfile.mkdtemp()
       a_      :	Union[str, Any]      =							"""en_speaker_1"""
       a_      :	Dict      =							"""This is a test string"""
       a_      :	Optional[int]      =							"""speaker_embeddings_path.json"""
       a_      :	int      =							"""speaker_embeddings"""


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       return AutoTokenizer.from_pretrained(self.checkpoint							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_tokenizer()

       a_      :	Optional[Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       processor.save_pretrained(self.tmpdirname )
       a_      :	List[str]      =							BarkProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab() )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)
       processor.save_pretrained(
           self.tmpdirname							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		speaker_embeddings_directory=self.speaker_embeddings_directory							,		)

       a_      :	List[str]      =							self.get_tokenizer(bos_token="""(BOS)"""							,		eos_token="""(EOS)""" )

       a_      :	Any      =							BarkProcessor.from_pretrained(
           self.tmpdirname							,		self.speaker_embeddings_dict_path							,		bos_token="""(BOS)"""							,		eos_token="""(EOS)"""							,		)

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab() )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)

       a_      :	Dict      =							35
       a_      :	List[Any]      =							2
       a_      :	Optional[int]      =							8

       a_      :	int      =							{
           """semantic_prompt""": np.ones(lowerCAmelCase_ ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
       }

       # test providing already loaded voice_preset
       a_      :	Optional[Any]      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )

       a_      :	Tuple      =							inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from npz file
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""file.npz""" )
       np.savez(lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )
       a_      :	List[Any]      =							inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from the hub
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=self.voice_preset )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_tokenizer()

       a_      :	Union[str, Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       a_      :	Optional[int]      =							processor(text=self.input_string )

       a_      :	Optional[int]      =							tokenizer(
           self.input_string							,		padding="""max_length"""							,		max_length=2_56							,		add_special_tokens=lowerCAmelCase_							,		return_attention_mask=lowerCAmelCase_							,		return_token_type_ids=lowerCAmelCase_							,		)

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]							,		encoded_processor[key].squeeze().tolist() )


