






"""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()
_lowerCAmelCase	:	Dict				=      logging.get_logger(__name__)


_lowerCAmelCase	:	Tuple				=      ["""model.decoder.embed_positions.weights"""]


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		Optional[int]:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : OrderedDict			,					snake_case       : int				)->		Tuple[Dict, Dict]:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str				)->		MusicgenDecoderConfig:






     '''simple docstring'''



     if checkpoint == "small":
          # default config values
          UpperCAmelCase__						:    List[str]							=							1024
          UpperCAmelCase__						:    Any							=							24
          UpperCAmelCase__						:    Dict							=							16
     elif checkpoint == "medium":
          UpperCAmelCase__						:    Any							=							1536
          UpperCAmelCase__						:    int							=							48
          UpperCAmelCase__						:    Any							=							24
     elif checkpoint == "large":
          UpperCAmelCase__						:    List[Any]							=							2048
          UpperCAmelCase__						:    Optional[int]							=							48
          UpperCAmelCase__						:    Any							=							32
     else:
          raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'				)
     UpperCAmelCase__						:    Optional[int]							=							MusicgenDecoderConfig(
         hidden_size=snake_case			,					ffn_dim=hidden_size * 4			,					num_hidden_layers=snake_case			,					num_attention_heads=snake_case			,					)
     return config


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : Dict=None			,					snake_case       : List[Any]=None			,					snake_case       : Union[str, Any]="cpu"				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							MusicGen.get_pretrained(snake_case			,					device=snake_case				)
     UpperCAmelCase__						:    List[Any]							=							decoder_config_from_checkpoint(snake_case				)

     UpperCAmelCase__						:    str							=							fairseq_model.lm.state_dict()
     UpperCAmelCase__							,				UpperCAmelCase__						:    Tuple							=							rename_state_dict(
         snake_case			,					hidden_size=decoder_config.hidden_size				)

     UpperCAmelCase__						:    Dict							=							TaEncoderModel.from_pretrained("t5-base"				)
     UpperCAmelCase__						:    List[str]							=							EncodecModel.from_pretrained("facebook/encodec_32khz"				)
     UpperCAmelCase__						:    Tuple							=							MusicgenForCausalLM(snake_case				).eval()

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

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

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

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

     # init the composite model
     UpperCAmelCase__						:    Optional[int]							=							MusicgenForConditionalGeneration(text_encoder=snake_case			,					audio_encoder=snake_case			,					decoder=snake_case				)

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

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

     with torch.no_grad():
          UpperCAmelCase__						:    Union[str, Any]							=							model(input_ids=snake_case			,					decoder_input_ids=snake_case				).logits

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

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

     UpperCAmelCase__						:    str							=							MusicgenProcessor(feature_extractor=snake_case			,					tokenizer=snake_case				)

     # set the appropriate bos/pad token ids
     UpperCAmelCase__						:    Tuple							=							2048
     UpperCAmelCase__						:    List[str]							=							2048

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

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

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


if __name__ == "__main__":
 _lowerCAmelCase	:	List[str]				=      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."""
 )

 _lowerCAmelCase	:	str				=      parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


