

"""simple docstring"""



import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


SCREAMING_SNAKE_CASE_					:	Any							  =						'http://www.mocksite.com/file1.txt'
SCREAMING_SNAKE_CASE_					:	Dict							  =						'"text": ["foo", "foo"]'
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						'6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'



class 					a						:



    """simple docstring"""



    UpperCAmelCase						 =					2_0_0
    UpperCAmelCase						 =					{"Content-Length": "100"}
    UpperCAmelCase						 =					{}

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






         """simple docstring"""


         return [bytes(UpperCamelCase    ,						"""utf-8"""       )]





def  _snake_case     (    *UpperCAmelCase_  :					int					,   **UpperCAmelCase_  :					int			):
     return MockResponse()





@pytest.mark.parametrize("""urls_type"""					,   [str, list, dict]			)
def  _snake_case     (    UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					Union[str, Any]			):
     import requests

     monkeypatch.setattr(UpperCAmelCase_					,   """request"""					,   UpperCAmelCase_			)

     A__										=  URL
     if issubclass(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  url
     elif issubclass(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  [url]
     elif issubclass(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  {"""train""": url}
     A__										=  """dummy"""
     A__										=  """downloads"""
     A__										=  tmp_path
     A__										=  DownloadConfig(
         cache_dir=os.path.join(UpperCAmelCase_					,   UpperCAmelCase_			)					,   use_etag=UpperCAmelCase_					,   )
     A__										=  DownloadManager(dataset_name=UpperCAmelCase_					,   download_config=UpperCAmelCase_			)
     A__										=  dl_manager.download(UpperCAmelCase_			)
     A__										=  urls
     for downloaded_paths in [downloaded_paths]:
          if isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
               A__										=  [downloaded_paths]
               A__										=  [urls]
          elif isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
               assert "train" in downloaded_paths.keys()
               A__										=  downloaded_paths.values()
               A__										=  urls.values()
          assert downloaded_paths
          for downloaded_path, input_url in zip(UpperCAmelCase_					,   UpperCAmelCase_			):
               assert downloaded_path == dl_manager.downloaded_paths[input_url]
               A__										=  Path(UpperCAmelCase_			)
               A__										=  downloaded_path.parts
               assert parts[-1] == HASH
               assert parts[-2] == cache_subdir
               assert downloaded_path.exists()
               A__										=  downloaded_path.read_text()
               assert content == CONTENT
               A__										=  downloaded_path.with_suffix(""".json"""			)
               assert metadata_downloaded_path.exists()
               A__										=  json.loads(metadata_downloaded_path.read_text()			)
               assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize("""paths_type"""					,   [str, list, dict]			)
def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Any			):
     A__										=  str(UpperCAmelCase_			)
     if issubclass(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  filename
     elif issubclass(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  [filename]
     elif issubclass(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  {"""train""": filename}
     A__										=  """dummy"""
     A__										=  xz_file.parent
     A__										=  """extracted"""
     A__										=  DownloadConfig(
         cache_dir=UpperCAmelCase_					,   use_etag=UpperCAmelCase_					,   )
     A__										=  DownloadManager(dataset_name=UpperCAmelCase_					,   download_config=UpperCAmelCase_			)
     A__										=  dl_manager.extract(UpperCAmelCase_			)
     A__										=  paths
     for extracted_paths in [extracted_paths]:
          if isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
               A__										=  [extracted_paths]
               A__										=  [paths]
          elif isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
               assert "train" in extracted_paths.keys()
               A__										=  extracted_paths.values()
               A__										=  paths.values()
          assert extracted_paths
          for extracted_path, input_path in zip(UpperCAmelCase_					,   UpperCAmelCase_			):
               assert extracted_path == dl_manager.extracted_paths[input_path]
               A__										=  Path(UpperCAmelCase_			)
               A__										=  extracted_path.parts
               assert parts[-1] == hash_url_to_filename(UpperCAmelCase_					,   etag=UpperCAmelCase_			)
               assert parts[-2] == extracted_subdir
               assert extracted_path.exists()
               A__										=  extracted_path.read_text()
               A__										=  text_file.read_text()
               assert extracted_file_content == expected_file_content





def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					Any			):
     assert path.endswith(""".jsonl"""			)
     for num_items, line in enumerate(UpperCAmelCase_					,   start=1			):
          A__										=  json.loads(line.decode("""utf-8"""			)			)
          assert item.keys() == {"col_1", "col_2", "col_3"}
     assert num_items == 4





@pytest.mark.parametrize("""archive_jsonl"""					,   ["""tar_jsonl_path""", """zip_jsonl_path"""]			)
def  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]					,   UpperCAmelCase_  :					List[str]			):
     A__										=  request.getfixturevalue(UpperCAmelCase_			)
     A__										=  DownloadManager()
     for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(UpperCAmelCase_			)					,   start=1			):
          _test_jsonl(UpperCAmelCase_					,   UpperCAmelCase_			)
     assert num_jsonl == 2





@pytest.mark.parametrize("""archive_nested_jsonl"""					,   ["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]			)
def  _snake_case     (    UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Any			):
     A__										=  request.getfixturevalue(UpperCAmelCase_			)
     A__										=  DownloadManager()
     for num_tar, (path, file) in enumerate(dl_manager.iter_archive(UpperCAmelCase_			)					,   start=1			):
          for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(UpperCAmelCase_			)					,   start=1			):
               _test_jsonl(UpperCAmelCase_					,   UpperCAmelCase_			)
     assert num_tar == 1
     assert num_jsonl == 2





def  _snake_case     (    UpperCAmelCase_  :					List[Any]			):
     A__										=  DownloadManager()
     for num_file, file in enumerate(dl_manager.iter_files(UpperCAmelCase_			)					,   start=1			):
          assert os.path.basename(UpperCAmelCase_			) == ("test.txt" if num_file == 1 else "train.txt")
     assert num_file == 2









"""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)






