



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()
a_								=    logging.get_logger(__name__)


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





def 						a__	(						_UpperCamelCase	:       Optional[int]							):







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





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





def 						a__	(						_UpperCamelCase	:       str							):







   if checkpoint == "small":
      # default config values
      __lowerCamelCase  							=			10_24
      __lowerCamelCase  							=			24
      __lowerCamelCase  							=			16
   elif checkpoint == "medium":
      __lowerCamelCase  							=			15_36
      __lowerCamelCase  							=			48
      __lowerCamelCase  							=			24
   elif checkpoint == "large":
      __lowerCamelCase  							=			20_48
      __lowerCamelCase  							=			48
      __lowerCamelCase  							=			32
   else:
      raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""							)
   __lowerCamelCase  							=			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 						a__	(						_UpperCamelCase	:       Tuple       ,_UpperCamelCase	:       List[Any]=None       ,_UpperCamelCase	:       Any=None       ,_UpperCamelCase	:       str="cpu"							):
   __lowerCamelCase  							=			MusicGen.get_pretrained(snake_case_       ,device=snake_case_							)
   __lowerCamelCase  							=			decoder_config_from_checkpoint(snake_case_							)

   __lowerCamelCase  							=			fairseq_model.lm.state_dict()
   __lowerCamelCase  							=			rename_state_dict(
       snake_case_       ,hidden_size=decoder_config.hidden_size							)

   __lowerCamelCase  							=			TaEncoderModel.from_pretrained('''t5-base'''							)
   __lowerCamelCase  							=			EncodecModel.from_pretrained('''facebook/encodec_32khz'''							)
   __lowerCamelCase  							=			MusicgenForCausalLM(snake_case_							).eval()

   # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
   __lowerCamelCase  							=			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
   __lowerCamelCase  							=			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
   __lowerCamelCase  							=			torch.arange(0       ,8       ,dtype=torch.long							).reshape(2       ,-1							)
   __lowerCamelCase  							=			input_ids.reshape(2 * 4       ,-1							)

   with torch.no_grad():
      __lowerCamelCase  							=			model(input_ids=snake_case_       ,decoder_input_ids=snake_case_							).logits

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

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

   __lowerCamelCase  							=			MusicgenProcessor(feature_extractor=snake_case_       ,tokenizer=snake_case_							)

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

   # set other default generation config params
   __lowerCamelCase  							=			int(30 * audio_encoder.config.frame_rate							)
   __lowerCamelCase  							=			True
   __lowerCamelCase  							=			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__":
  a_								=    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."""
  )

  a_								=    parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)













"""simple docstring"""



from typing import TYPE_CHECKING

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


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

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




