

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def __UpperCAmelCase       (		__A			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     [
           "encoder.version",
           "decoder.version",
           "model.encoder.version",
           "model.decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "encoder.embed_positions._float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(_lowerCamelCase ,						_lowerCamelCase			)





def __UpperCAmelCase       (		__A			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__						,			UpperCAmelCase__										=     emb.weight.shape
       UpperCAmelCase__										=     nn.Linear(_lowerCamelCase ,						_lowerCamelCase ,						bias=_lowerCamelCase			)
       UpperCAmelCase__										=     emb.weight.data
       return lin_layer





def __UpperCAmelCase       (		__A			)						->					Any:



       '''simple docstring'''





       UpperCAmelCase__										=     torch.load(_lowerCamelCase ,						map_location="cpu"			)
       UpperCAmelCase__										=     mam_aaa["args"] or mam_aaa["cfg"]["model"]
       UpperCAmelCase__										=     mam_aaa["model"]
       remove_ignore_keys_(_lowerCamelCase			)
       UpperCAmelCase__										=     state_dict["encoder.embed_tokens.weight"].shape[0]

       UpperCAmelCase__										=     MaMaaaConfig(
           vocab_size=_lowerCamelCase ,						max_position_embeddings=1_0_2_4 ,						encoder_layers=args.encoder_layers ,						decoder_layers=args.decoder_layers ,						encoder_attention_heads=args.encoder_attention_heads ,						decoder_attention_heads=args.decoder_attention_heads ,						encoder_ffn_dim=args.encoder_ffn_embed_dim ,						decoder_ffn_dim=args.decoder_ffn_embed_dim ,						d_model=args.encoder_embed_dim ,						encoder_layerdrop=args.encoder_layerdrop ,						decoder_layerdrop=args.decoder_layerdrop ,						dropout=args.dropout ,						attention_dropout=args.attention_dropout ,						activation_dropout=args.activation_dropout ,						activation_function="relu" ,						)

       UpperCAmelCase__										=     state_dict["decoder.embed_tokens.weight"]
       UpperCAmelCase__										=     MaMaaaForConditionalGeneration(_lowerCamelCase			)
       model.model.load_state_dict(_lowerCamelCase ,						strict=_lowerCamelCase			)
       UpperCAmelCase__										=     make_linear_from_emb(model.model.shared			)

       return model


if __name__ == "__main__":
    A      = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
    parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    A      = parser.parse_args()
    A      = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)









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     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     "ylacombe/bark-small"
         UpperCAmelCase__										=     tempfile.mkdtemp()
         UpperCAmelCase__										=     "en_speaker_1"
         UpperCAmelCase__										=     "This is a test string"
         UpperCAmelCase__										=     "speaker_embeddings_path.json"
         UpperCAmelCase__										=     "speaker_embeddings"






  def 			_UpperCAmelCase  (					self      :					List[str]		,						**_lowercase      :					str ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         shutil.rmtree(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         processor.save_pretrained(self.tmpdirname )
         UpperCAmelCase__										=     BarkProcessor.from_pretrained(self.tmpdirname )

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






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     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		,						)

         UpperCAmelCase__										=     self.get_tokenizer(bos_token="(BOS)"		,						eos_token="(EOS)" )

         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)

         UpperCAmelCase__										=     35
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     8

         UpperCAmelCase__										=     {
             "semantic_prompt": np.ones(_lowercase ),
             "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
             "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
         }

         # test providing already loaded voice_preset
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )

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

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

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

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






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




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         UpperCAmelCase__										=     processor(text=self.input_string )

         UpperCAmelCase__										=     tokenizer(
             self.input_string		,						padding="max_length"		,						max_length=2_56		,						add_special_tokens=_lowercase		,						return_attention_mask=_lowercase		,						return_token_type_ids=_lowercase		,						)

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





