





'''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()
lowercase__       : Tuple         =       logging.get_logger(__name__)


lowercase__       : str         =       ['''model.decoder.embed_positions.weights''']






def 			_lowerCAmelCase     (    __snake_case    :					Any     )	->	Dict:

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






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]     )	->	Tuple[Dict, Dict]:
     __A		:					List[Any]						=			list(state_dict.keys()     )
     __A		:					int						=			{}
     for key in keys:
          __A		:					List[str]						=			state_dict.pop(lowercase_     )
          __A		:					List[Any]						=			rename_keys(lowercase_     )
          if "in_proj_weight" in key:
               # split fused qkv proj
               __A		:					List[Any]						=			val[:hidden_size, :]
               __A		:					List[Any]						=			val[hidden_size : 2 * hidden_size, :]
               __A		:					Tuple						=			val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               __A		:					Optional[Any]						=			val
          else:
               __A		:					str						=			val
     return state_dict, enc_dec_proj_state_dict






def 			_lowerCAmelCase     (    __snake_case    :					Tuple     )	->	MusicgenDecoderConfig:

     if checkpoint == "small":
          # default config values
          __A		:					Optional[Any]						=			10_24
          __A		:					List[str]						=			24
          __A		:					Optional[int]						=			16
     elif checkpoint == "medium":
          __A		:					int						=			15_36
          __A		:					Any						=			48
          __A		:					Optional[Any]						=			24
     elif checkpoint == "large":
          __A		:					int						=			20_48
          __A		:					Dict						=			48
          __A		:					Dict						=			32
     else:
          raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'     )
     __A		:					List[Any]						=			MusicgenDecoderConfig(
         hidden_size=lowercase_					,				ffn_dim=hidden_size * 4					,				num_hidden_layers=lowercase_					,				num_attention_heads=lowercase_					,				)
     return config






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Tuple					,				__snake_case    :					Dict=None					,				__snake_case    :					str=None					,				__snake_case    :					Optional[int]="cpu"     )	->	Union[str, Any]:
     __A		:					int						=			MusicGen.get_pretrained(lowercase_					,				device=lowercase_     )
     __A		:					int						=			decoder_config_from_checkpoint(lowercase_     )

     __A		:					int						=			fairseq_model.lm.state_dict()
     __A		:					Tuple						=			rename_state_dict(
         lowercase_					,				hidden_size=decoder_config.hidden_size     )

     __A		:					int						=			TaEncoderModel.from_pretrained('t5-base'     )
     __A		:					str						=			EncodecModel.from_pretrained('facebook/encodec_32khz'     )
     __A		:					Dict						=			MusicgenForCausalLM(lowercase_     ).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     __A		:					List[str]						=			decoder.load_state_dict(lowercase_					,				strict=lowercase_     )

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

     if len(lowercase_     ) > 0:
          raise ValueError(f'Missing key(s) in state_dict: {missing_keys}'     )

     if len(lowercase_     ) > 0:
          raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}'     )

     # init the composite model
     __A		:					Tuple						=			MusicgenForConditionalGeneration(text_encoder=lowercase_					,				audio_encoder=lowercase_					,				decoder=lowercase_     )

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

     # check we can do a forward pass
     __A		:					Any						=			torch.arange(0					,				8					,				dtype=torch.long     ).reshape(2					,				-1     )
     __A		:					List[Any]						=			input_ids.reshape(2 * 4					,				-1     )

     with torch.no_grad():
          __A		:					List[str]						=			model(input_ids=lowercase_					,				decoder_input_ids=lowercase_     ).logits

     if logits.shape != (8, 1, 20_48):
          raise ValueError('Incorrect shape for logits'     )

     # now construct the processor
     __A		:					str						=			AutoTokenizer.from_pretrained('t5-base'     )
     __A		:					Union[str, Any]						=			AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'					,				padding_side='left'     )

     __A		:					Any						=			MusicgenProcessor(feature_extractor=lowercase_					,				tokenizer=lowercase_     )

     # set the appropriate bos/pad token ids
     __A		:					Optional[Any]						=			20_48
     __A		:					str						=			20_48

     # set other default generation config params
     __A		:					Optional[int]						=			int(30 * audio_encoder.config.frame_rate     )
     __A		:					Optional[Any]						=			True
     __A		:					Any						=			3.0

     if pytorch_dump_folder is not None:
          Path(lowercase_     ).mkdir(exist_ok=lowercase_     )
          logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}'     )
          model.save_pretrained(lowercase_     )
          processor.save_pretrained(lowercase_     )

     if repo_id:
          logger.info(f'Pushing model {checkpoint} to {repo_id}'     )
          model.push_to_hub(lowercase_     )
          processor.push_to_hub(lowercase_     )


if __name__ == "__main__":
  lowercase__       : Dict         =       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.'''
  )

  lowercase__       : Optional[int]         =       parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






"""simple docstring"""


import random
from typing import Any




def        lowercase__     (       lowercase_      )       ->		list[Any]:




      """simple docstring"""

      for _ in range(len(lowercase_      )      ):
            _UpperCamelCase   :						Dict    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase   :						str    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							data[b], data[a]
      return data


if __name__ == "__main__":
  lowerCamelCase__						= [0, 1, 2, 3, 4, 5, 6, 7]
  lowerCamelCase__						= ["python", "says", "hello", "!"]
  print("Fisher-Yates Shuffle:")
  print("List", integers, strings)
  print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
