





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_						       =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_						       =	["""model.decoder.embed_positions.weights"""]



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Tuple[Dict, Dict]:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       MusicgenDecoderConfig:


      '''simple docstring'''


      if checkpoint == "small":
            # default config values
            SCREAMING_SNAKE_CASE		=						10_24
            SCREAMING_SNAKE_CASE		=						24
            SCREAMING_SNAKE_CASE		=						16
      elif checkpoint == "medium":
            SCREAMING_SNAKE_CASE		=						15_36
            SCREAMING_SNAKE_CASE		=						48
            SCREAMING_SNAKE_CASE		=						24
      elif checkpoint == "large":
            SCREAMING_SNAKE_CASE		=						20_48
            SCREAMING_SNAKE_CASE		=						48
            SCREAMING_SNAKE_CASE		=						32
      else:
            raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""     )
      SCREAMING_SNAKE_CASE		=						MusicgenDecoderConfig(
          hidden_size=_SCREAMING_SNAKE_CASE			,						ffn_dim=hidden_size * 4			,						num_hidden_layers=_SCREAMING_SNAKE_CASE			,						num_attention_heads=_SCREAMING_SNAKE_CASE			,						)
      return config



@torch.no_grad()
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE="cpu"     )			->       List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE			,						device=_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						fairseq_model.lm.state_dict()
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						rename_state_dict(
          _SCREAMING_SNAKE_CASE			,						hidden_size=decoder_config.hidden_size     )

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

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

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

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

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

      # init the composite model
      SCREAMING_SNAKE_CASE		=						MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE			,						audio_encoder=_SCREAMING_SNAKE_CASE			,						decoder=_SCREAMING_SNAKE_CASE     )

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

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

      with torch.no_grad():
            SCREAMING_SNAKE_CASE		=						model(input_ids=_SCREAMING_SNAKE_CASE			,						decoder_input_ids=_SCREAMING_SNAKE_CASE     ).logits

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

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

      SCREAMING_SNAKE_CASE		=						MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE			,						tokenizer=_SCREAMING_SNAKE_CASE     )

      # set the appropriate bos/pad token ids
      SCREAMING_SNAKE_CASE		=						20_48
      SCREAMING_SNAKE_CASE		=						20_48

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

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	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_						       =	parser.parse_args()
      convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)









import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict

@dataclass
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      ComputeEnvironment.AMAZON_SAGEMAKER
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     Optional[int]		     =      "ml.p3.2xlarge"
     __snake_case   :     List[str]		     =      "accelerate_sagemaker_execution_role"
     __snake_case   :     Tuple		     =      "hf-sm"
     __snake_case   :     Any		     =      "us-east-1"
     __snake_case   :     Union[str, Any]		     =      1
     __snake_case   :     Dict		     =      "accelerate-sagemaker-1"
     __snake_case   :     Tuple		     =      "1.6"
     __snake_case   :     List[str]		     =      "4.4"
     __snake_case   :     str		     =      "train.py"
     __snake_case   :     List[str]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "False",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]
     __snake_case   :     Optional[int]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "--do_test",
         "False",
         "--do_predict",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
           assert isinstance(converted_args["""model_name_or_path"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""do_train"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""epochs"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""learning_rate"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""max_steps"""]						,lowerCamelCase__	)

           with pytest.raises(lowerCamelCase__	):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)


