



'''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__:   Dict								=			logging.get_logger(__name__)


UpperCamelCase__:   Optional[Any]								=			["model.decoder.embed_positions.weights"]


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


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


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														UpperCAmelCase :       Optional[int] 							=		1024
														UpperCAmelCase :       List[Any] 							=		24
														UpperCAmelCase :       List[Any] 							=		16
							elif checkpoint == "medium":
														UpperCAmelCase :       Tuple 							=		1536
														UpperCAmelCase :       Union[str, Any] 							=		48
														UpperCAmelCase :       Tuple 							=		24
							elif checkpoint == "large":
														UpperCAmelCase :       Tuple 							=		2048
														UpperCAmelCase :       List[Any] 							=		48
														UpperCAmelCase :       List[Any] 							=		32
							else:
														raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""  )
							UpperCAmelCase :       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  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						List[Any]=None   ,      _lowerCAmelCase  :						Optional[Any]=None   ,      _lowerCAmelCase  :						Dict="cpu"  )      ->   str:
							UpperCAmelCase :       List[str] 							=		MusicGen.get_pretrained(_lowerCAmelCase   ,      device=_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		decoder_config_from_checkpoint(_lowerCAmelCase  )

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

							UpperCAmelCase :       Tuple 							=		TaEncoderModel.from_pretrained('''t5-base'''  )
							UpperCAmelCase :       Dict 							=		EncodecModel.from_pretrained('''facebook/encodec_32khz'''  )
							UpperCAmelCase :       Optional[int] 							=		MusicgenForCausalLM(_lowerCAmelCase  ).eval()

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		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
							UpperCAmelCase :       Any 							=		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
							UpperCAmelCase :       Union[str, Any] 							=		torch.arange(0   ,      8   ,      dtype=torch.long  ).reshape(2   ,      -1  )
							UpperCAmelCase :       List[Any] 							=		input_ids.reshape(2 * 4   ,      -1  )

							with torch.no_grad():
														UpperCAmelCase :       List[str] 							=		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
							UpperCAmelCase :       Any 							=		AutoTokenizer.from_pretrained('''t5-base'''  )
							UpperCAmelCase :       Optional[Any] 							=		AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''   ,      padding_side='''left'''  )

							UpperCAmelCase :       int 							=		MusicgenProcessor(feature_extractor=_lowerCAmelCase   ,      tokenizer=_lowerCAmelCase  )

							# set the appropriate bos/pad token ids
							UpperCAmelCase :       Optional[int] 							=		2048
							UpperCAmelCase :       List[Any] 							=		2048

							# set other default generation config params
							UpperCAmelCase :       Tuple 							=		int(30 * audio_encoder.config.frame_rate  )
							UpperCAmelCase :       Optional[Any] 							=		True
							UpperCAmelCase :       str 							=		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__:   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."
						)

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_    					=       {
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['EfficientFormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'EfficientFormerForImageClassification',
       'EfficientFormerForImageClassificationWithTeacher',
       'EfficientFormerModel',
       'EfficientFormerPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFEfficientFormerForImageClassification',
       'TFEfficientFormerForImageClassificationWithTeacher',
       'TFEfficientFormerModel',
       'TFEfficientFormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

   a_    					=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



