


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        =     logging.get_logger(__name__)


__lowerCamelCase        =     ['''model.decoder.embed_positions.weights''']



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



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



def  _a			(						__UpperCamelCase ):
     if checkpoint == "small":
          # default config values
          a_    :     List[Any]										=    1_0_2_4
          a_    :     Optional[Any]										=    2_4
          a_    :     Optional[Any]										=    1_6
     elif checkpoint == "medium":
          a_    :     Any										=    1_5_3_6
          a_    :     int										=    4_8
          a_    :     int										=    2_4
     elif checkpoint == "large":
          a_    :     Any										=    2_0_4_8
          a_    :     List[str]										=    4_8
          a_    :     Union[str, Any]										=    3_2
     else:
          raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
     a_    :     List[Any]										=    MusicgenDecoderConfig(
         hidden_size=__UpperCamelCase  ,  ffn_dim=hidden_size * 4  ,  num_hidden_layers=__UpperCamelCase  ,  num_attention_heads=__UpperCamelCase  ,  )
     return config



@torch.no_grad()
def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase="cpu" ):
     a_    :     Optional[Any]										=    MusicGen.get_pretrained(__UpperCamelCase  ,  device=__UpperCamelCase )
     a_    :     List[Any]										=    decoder_config_from_checkpoint(__UpperCamelCase )

     a_    :     List[Any]										=    fairseq_model.lm.state_dict()
     a_			,			a_    :     Dict										=    rename_state_dict(
         __UpperCamelCase  ,  hidden_size=decoder_config.hidden_size )

     a_    :     Any										=    TaEncoderModel.from_pretrained("""t5-base""" )
     a_    :     Union[str, Any]										=    EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
     a_    :     str										=    MusicgenForCausalLM(__UpperCamelCase ).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     a_			,			a_    :     Optional[Any]										=    decoder.load_state_dict(__UpperCamelCase  ,  strict=__UpperCamelCase )

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

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

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

     # init the composite model
     a_    :     Tuple										=    MusicgenForConditionalGeneration(text_encoder=__UpperCamelCase  ,  audio_encoder=__UpperCamelCase  ,  decoder=__UpperCamelCase )

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

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

     with torch.no_grad():
          a_    :     Optional[Any]										=    model(input_ids=__UpperCamelCase  ,  decoder_input_ids=__UpperCamelCase ).logits

     if logits.shape != (8, 1, 2_0_4_8):
          raise ValueError("""Incorrect shape for logits""" )

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

     a_    :     List[str]										=    MusicgenProcessor(feature_extractor=__UpperCamelCase  ,  tokenizer=__UpperCamelCase )

     # set the appropriate bos/pad token ids
     a_    :     Optional[Any]										=    2_0_4_8
     a_    :     List[Any]										=    2_0_4_8

     # set other default generation config params
     a_    :     List[str]										=    int(3_0 * audio_encoder.config.frame_rate )
     a_    :     str										=    True
     a_    :     Tuple										=    3.0

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

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


if __name__ == "__main__":
       __lowerCamelCase        =     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        =     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_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase        =     {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''ViTFeatureExtractor''']
       __lowerCamelCase        =     ['''ViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTForImageClassification''',
           '''ViTForMaskedImageModeling''',
           '''ViTModel''',
           '''ViTPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TFViTForImageClassification''',
           '''TFViTModel''',
           '''TFViTPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FlaxViTForImageClassification''',
           '''FlaxViTModel''',
           '''FlaxViTPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vit import ViTFeatureExtractor
              from .image_processing_vit import ViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit import (
                  VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTForImageClassification,
                  ViTForMaskedImageModeling,
                  ViTModel,
                  ViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
       import sys

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



