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__    							= logging.get_logger(__name__)


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




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A							)					->    Tuple[Dict, Dict]:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A							)					->    MusicgenDecoderConfig:







      '''simple docstring'''


      if checkpoint == "small":
            # default config values
            UpperCAmelCase__								=  1_024
            UpperCAmelCase__								=  24
            UpperCAmelCase__								=  16
      elif checkpoint == "medium":
            UpperCAmelCase__								=  1_536
            UpperCAmelCase__								=  48
            UpperCAmelCase__								=  24
      elif checkpoint == "large":
            UpperCAmelCase__								=  2_048
            UpperCAmelCase__								=  48
            UpperCAmelCase__								=  32
      else:
            raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""							)
      UpperCAmelCase__								=  MusicgenDecoderConfig(
          hidden_size=__A,  ffn_dim=hidden_size * 4,  num_hidden_layers=__A,  num_attention_heads=__A,  )
      return config




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A=None,  __A=None,  __A="cpu"							)					->    List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=  MusicGen.get_pretrained(__A,  device=__A							)
      UpperCAmelCase__								=  decoder_config_from_checkpoint(__A							)

      UpperCAmelCase__								=  fairseq_model.lm.state_dict()
      UpperCAmelCase__		,				UpperCAmelCase__								=  rename_state_dict(
          __A,  hidden_size=decoder_config.hidden_size							)

      UpperCAmelCase__								=  TaEncoderModel.from_pretrained("t5-base"							)
      UpperCAmelCase__								=  EncodecModel.from_pretrained("facebook/encodec_32khz"							)
      UpperCAmelCase__								=  MusicgenForCausalLM(__A							).eval()

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

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

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

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

      # init the composite model
      UpperCAmelCase__								=  MusicgenForConditionalGeneration(text_encoder=__A,  audio_encoder=__A,  decoder=__A							)

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

      # check we can do a forward pass
      UpperCAmelCase__								=  torch.arange(0,  8,  dtype=torch.long							).reshape(2,  -1							)
      UpperCAmelCase__								=  input_ids.reshape(2 * 4,  -1							)

      with torch.no_grad():
            UpperCAmelCase__								=  model(input_ids=__A,  decoder_input_ids=__A							).logits

      if logits.shape != (8, 1, 2_048):
            raise ValueError("Incorrect shape for logits"							)

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

      UpperCAmelCase__								=  MusicgenProcessor(feature_extractor=__A,  tokenizer=__A							)

      # set the appropriate bos/pad token ids
      UpperCAmelCase__								=  2_048
      UpperCAmelCase__								=  2_048

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

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

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


if __name__ == "__main__":
      UpperCamelCase__    							= 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__    							= parser.parse_args()
      convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  [self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase__								=  [tuple(__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else key for key in keys]
         UpperCAmelCase__								=  Counter(__UpperCAmelCase		)
         UpperCAmelCase__								=  [key for key in counter if counter[key] > 1]
         if duplicate_keys:
               raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''simple docstring'''


      UpperCAmelCase__								=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCAmelCase__								=  full_content[1:].index("---"							) + 1
            UpperCAmelCase__								=  "\n".join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

      return None, "\n".join(__A							)





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(readme_file.read()		)
         if yaml_string is not None:
               return cls.from_yaml_string(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



         if path.exists():
               with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
                     UpperCAmelCase__								=  readme_file.read()
         else:
               UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self._to_readme(__UpperCAmelCase		)
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"		) as readme_file:
               readme_file.write(__UpperCAmelCase		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



         if readme_content is not None:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(__UpperCAmelCase		)
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n" + content
         else:
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n"
         return full_content


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase__								=  {
             (key.replace("-"	,  "_"		) if key.replace("-"	,  "_"		) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         return yaml.safe_dump(
             {
                 (key.replace("_"	,  "-"		) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }	,  sort_keys=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)



