

"""simple docstring"""



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					a						(       _lowerCamelCase,	unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					DebertaTokenizer
    UpperCAmelCase						 =					True
    UpperCAmelCase						 =					DebertaTokenizerFast

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         A__										=  [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """[UNK]""",
         ]
         A__										=  dict(zip(UpperCamelCase    ,						range(len(UpperCamelCase       )       )       )       )
         A__										=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         A__										=  {"""unk_token""": """[UNK]"""}

         A__										=  os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]       )
         A__										=  os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]       )
         with open(self.vocab_file    ,						"""w"""    ,						encoding="""utf-8"""       ) as fp:
              fp.write(json.dumps(UpperCamelCase       ) + """\n"""       )
         with open(self.merges_file    ,						"""w"""    ,						encoding="""utf-8"""       ) as fp:
              fp.write("""\n""".join(UpperCamelCase       )       )

    def 				UpperCamelCase	(     self: str    ,						**UpperCamelCase: Tuple       ):






         """simple docstring"""


         kwargs.update(self.special_tokens_map       )
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**UpperCamelCase       )

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: List[Any]       ):






         """simple docstring"""


         A__										=  """lower newer"""
         A__										=  """lower newer"""
         return input_text, output_text

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.get_tokenizer()
         A__										=  """lower newer"""
         A__										=  ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         A__										=  tokenizer.tokenize(UpperCamelCase       )
         self.assertListEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  tokens + [tokenizer.unk_token]
         A__										=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase       )    ,						UpperCamelCase       )

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






         """simple docstring"""


         A__										=  self.get_tokenizer()
         A__										=  tokenizer("""Hello"""    ,						"""World"""       )
         A__										=  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd["""token_type_ids"""]    ,						UpperCamelCase       )

    @slow
    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         A__										=  self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""       )

         A__										=  tokenizer.encode("""sequence builders"""    ,						add_special_tokens=UpperCamelCase       )
         A__										=  tokenizer.encode("""multi-sequence build"""    ,						add_special_tokens=UpperCamelCase       )

         A__										=  tokenizer.encode(
             """sequence builders"""    ,						add_special_tokens=UpperCamelCase    ,						add_prefix_space=UpperCamelCase       )
         A__										=  tokenizer.encode(
             """sequence builders"""    ,						"""multi-sequence build"""    ,						add_special_tokens=UpperCamelCase    ,						add_prefix_space=UpperCamelCase       )

         A__										=  tokenizer.build_inputs_with_special_tokens(UpperCamelCase       )
         A__										=  tokenizer.build_inputs_with_special_tokens(UpperCamelCase    ,						UpperCamelCase       )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





    @slow
    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         A__										=  [self.tokenizer_class]
         if self.test_rust_tokenizer:
              tokenizer_classes.append(self.rust_tokenizer_class       )

         for tokenizer_class in tokenizer_classes:
              A__										=  tokenizer_class.from_pretrained("""microsoft/deberta-base"""       )

              A__										=  [
                  """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                  """ALBERT incorporates two parameter reduction techniques""",
                  """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                  """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                  """ vocabulary embedding.""",
              ]

              A__										=  tokenizer(UpperCamelCase    ,						padding=UpperCamelCase       )
              A__										=  [tokenizer.decode(UpperCamelCase    ,						skip_special_tokens=UpperCamelCase       ) for seq in encoding["""input_ids"""]]

              # fmt: off
              A__										=  {
                  """input_ids""": [
                      [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                  ],
                  """token_type_ids""": [
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                  ],
                  """attention_mask""": [
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                  ]
              }
              # fmt: on

              A__										=  [
                  """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                  """ALBERT incorporates two parameter reduction techniques""",
                  """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                  """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                  """ vocabulary embedding.""",
              ]

              self.assertDictEqual(encoding.data    ,						UpperCamelCase       )

              for expected, decoded in zip(UpperCamelCase    ,						UpperCamelCase       ):
                   self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )









"""simple docstring"""



import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_					:	Any							  =						['model.decoder.embed_positions.weights']





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]			):
     if "emb" in name:
          A__										=  name.replace("""emb"""					,   """model.decoder.embed_tokens"""			)
     if "transformer" in name:
          A__										=  name.replace("""transformer"""					,   """model.decoder"""			)
     if "cross_attention" in name:
          A__										=  name.replace("""cross_attention"""					,   """encoder_attn"""			)
     if "linear1" in name:
          A__										=  name.replace("""linear1"""					,   """fc1"""			)
     if "linear2" in name:
          A__										=  name.replace("""linear2"""					,   """fc2"""			)
     if "norm1" in name:
          A__										=  name.replace("""norm1"""					,   """self_attn_layer_norm"""			)
     if "norm_cross" in name:
          A__										=  name.replace("""norm_cross"""					,   """encoder_attn_layer_norm"""			)
     if "norm2" in name:
          A__										=  name.replace("""norm2"""					,   """final_layer_norm"""			)
     if "out_norm" in name:
          A__										=  name.replace("""out_norm"""					,   """model.decoder.layer_norm"""			)
     if "linears" in name:
          A__										=  name.replace("""linears"""					,   """lm_heads"""			)
     if "condition_provider.conditioners.description.output_proj" in name:
          A__										=  name.replace("""condition_provider.conditioners.description.output_proj"""					,   """enc_to_dec_proj"""			)
     return name





def  _snake_case     (    UpperCAmelCase_  :					OrderedDict					,   UpperCAmelCase_  :					int			):
     A__										=  list(state_dict.keys()			)
     A__										=  {}
     for key in keys:
          A__										=  state_dict.pop(UpperCAmelCase_			)
          A__										=  rename_keys(UpperCAmelCase_			)
          if "in_proj_weight" in key:
               # split fused qkv proj
               A__										=  val[:hidden_size, :]
               A__										=  val[hidden_size : 2 * hidden_size, :]
               A__										=  val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               A__										=  val
          else:
               A__										=  val
     return state_dict, enc_dec_proj_state_dict





def  _snake_case     (    UpperCAmelCase_  :					str			):
     if checkpoint == "small":
          # default config values
          A__										=  1024
          A__										=  24
          A__										=  16
     elif checkpoint == "medium":
          A__										=  1536
          A__										=  48
          A__										=  24
     elif checkpoint == "large":
          A__										=  2048
          A__										=  48
          A__										=  32
     else:
          raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""			)
     A__										=  MusicgenDecoderConfig(
         hidden_size=UpperCAmelCase_					,   ffn_dim=hidden_size * 4					,   num_hidden_layers=UpperCAmelCase_					,   num_attention_heads=UpperCAmelCase_					,   )
     return config





@torch.no_grad()
def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[Any]=None					,   UpperCAmelCase_  :					Tuple=None					,   UpperCAmelCase_  :					Any="cpu"			):
     A__										=  MusicGen.get_pretrained(UpperCAmelCase_					,   device=UpperCAmelCase_			)
     A__										=  decoder_config_from_checkpoint(UpperCAmelCase_			)

     A__										=  fairseq_model.lm.state_dict()
     A__			,				A__										=  rename_state_dict(
         UpperCAmelCase_					,   hidden_size=decoder_config.hidden_size			)

     A__										=  TaEncoderModel.from_pretrained("""t5-base"""			)
     A__										=  EncodecModel.from_pretrained("""facebook/encodec_32khz"""			)
     A__										=  MusicgenForCausalLM(UpperCAmelCase_			).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     A__			,				A__										=  decoder.load_state_dict(UpperCAmelCase_					,   strict=UpperCAmelCase_			)

     for key in missing_keys.copy():
          if key.startswith(("""text_encoder""", """audio_encoder""")			) or key in EXPECTED_MISSING_KEYS:
               missing_keys.remove(UpperCAmelCase_			)

     if len(UpperCAmelCase_			) > 0:
          raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}"""			)

     if len(UpperCAmelCase_			) > 0:
          raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}"""			)

     # init the composite model
     A__										=  MusicgenForConditionalGeneration(text_encoder=UpperCAmelCase_					,   audio_encoder=UpperCAmelCase_					,   decoder=UpperCAmelCase_			)

     # load the pre-trained enc-dec projection (from the decoder state dict)
     model.enc_to_dec_proj.load_state_dict(UpperCAmelCase_			)

     # check we can do a forward pass
     A__										=  torch.arange(0					,   8					,   dtype=torch.long			).reshape(2					,   -1			)
     A__										=  input_ids.reshape(2 * 4					,   -1			)

     with torch.no_grad():
          A__										=  model(input_ids=UpperCAmelCase_					,   decoder_input_ids=UpperCAmelCase_			).logits

     if logits.shape != (8, 1, 2048):
          raise ValueError("""Incorrect shape for logits"""			)

     # now construct the processor
     A__										=  AutoTokenizer.from_pretrained("""t5-base"""			)
     A__										=  AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""					,   padding_side="""left"""			)

     A__										=  MusicgenProcessor(feature_extractor=UpperCAmelCase_					,   tokenizer=UpperCAmelCase_			)

     # set the appropriate bos/pad token ids
     A__										=  2048
     A__										=  2048

     # set other default generation config params
     A__										=  int(30 * audio_encoder.config.frame_rate			)
     A__										=  True
     A__										=  3.0

     if pytorch_dump_folder is not None:
          Path(UpperCAmelCase_			).mkdir(exist_ok=UpperCAmelCase_			)
          logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}"""			)
          model.save_pretrained(UpperCAmelCase_			)
          processor.save_pretrained(UpperCAmelCase_			)

     if repo_id:
          logger.info(F"""Pushing model {checkpoint} to {repo_id}"""			)
          model.push_to_hub(UpperCAmelCase_			)
          processor.push_to_hub(UpperCAmelCase_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	int							  =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint',
     default='small',
     type=str,
     help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
 )
 parser.add_argument(
     '--pytorch_dump_folder',
     required=True,
     default=None,
     type=str,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument(
     '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
 )
 parser.add_argument(
     '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
 )

 SCREAMING_SNAKE_CASE_					:	Tuple							  =						parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






