"""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()
_UpperCamelCase   :						Union[str, Any]      =    logging.get_logger(__name__)


_UpperCamelCase   :						int      =    ["model.decoder.embed_positions.weights"]





def 				a_     (      _lowerCAmelCase  : Optional[Any]     ):





       '''simple docstring'''

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





def 				a_     (      _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : int     ):





       '''simple docstring'''

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





def 				a_     (      _lowerCAmelCase  : Dict     ):





       '''simple docstring'''

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





@torch.no_grad()
def 				a_     (      _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Optional[int]=None   , _lowerCAmelCase  : Dict=None   , _lowerCAmelCase  : int="cpu"     ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	MusicGen.get_pretrained(_lowerCAmelCase   , device=_lowerCAmelCase     )
       lowercase__			:		Any       =	decoder_config_from_checkpoint(_lowerCAmelCase     )

       lowercase__			:		int       =	fairseq_model.lm.state_dict()
       lowercase__			:		List[Any]       =	rename_state_dict(
           _lowerCAmelCase   , hidden_size=decoder_config.hidden_size     )

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

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

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

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

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

       # init the composite model
       lowercase__			:		Tuple       =	MusicgenForConditionalGeneration(text_encoder=_lowerCAmelCase   , audio_encoder=_lowerCAmelCase   , decoder=_lowerCAmelCase     )

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

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

       with torch.no_grad():
              lowercase__			:		Optional[int]       =	model(input_ids=_lowerCAmelCase   , decoder_input_ids=_lowerCAmelCase     ).logits

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

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

       lowercase__			:		Tuple       =	MusicgenProcessor(feature_extractor=_lowerCAmelCase   , tokenizer=_lowerCAmelCase     )

       # set the appropriate bos/pad token ids
       lowercase__			:		Dict       =	2048
       lowercase__			:		Optional[int]       =	2048

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

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

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


if __name__ == "__main__":
       _UpperCamelCase   :						Tuple      =    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."
       )

       _UpperCamelCase   :						Any      =    parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






'''simple docstring'''





import numpy as np

from transformers import Pipeline





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      np.max(_lowerCAmelCase     ,   axis=-1     ,   keepdims=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      np.exp(outputs - maxes		)
      return shifted_exp / shifted_exp.sum(axis=-1     ,   keepdims=_lowerCAmelCase		)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[Any] ,     **snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      {}
          if "second_text" in kwargs:
                snake_case__  :    Union[str, Any]    						=      kwargs["""second_text"""]
          return preprocess_kwargs, {}, {}




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any]=None						):
          return self.tokenizer(snake_case_ ,     text_pair=snake_case_ ,     return_tensors=self.framework						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Dict						):
          return self.model(**snake_case_						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     List[Any]						):
          snake_case__  :    Union[str, Any]    						=      model_outputs.logits[0].numpy()
          snake_case__  :    List[str]    						=      softmax(snake_case_						)

          snake_case__  :    List[str]    						=      np.argmax(snake_case_						)
          snake_case__  :    List[str]    						=      self.model.config.idalabel[best_class]
          snake_case__  :    Optional[int]    						=      probabilities[best_class].item()
          snake_case__  :    str    						=      logits.tolist()
          return {"label": label, "score": score, "logits": logits}
