




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


a_   		=     datasets.utils.logging.get_logger(__name__)

@dataclass
class  _lowerCamelCase						(			datasets.BuilderConfig   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[datasets.Features]    =					None
      lowerCAmelCase__			: str    =					"utf-8"
      lowerCAmelCase__			: Optional[str]    =					None
      lowerCAmelCase__			: Optional[str]    =					None
      lowerCAmelCase__			: bool    =					True  # deprecated
      lowerCAmelCase__			: Optional[int]    =					None  # deprecated
      lowerCAmelCase__			: int    =					10 << 20  # 10MB
      lowerCAmelCase__			: Optional[bool]    =					None



class  _lowerCamelCase						(			datasets.ArrowBasedBuilder   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					JsonConfig







      def       snake_case	(     self					: Tuple				):
          if self.config.block_size is not None:
              logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''				)
              __UpperCamelCase  		=						self.config.block_size
          if self.config.use_threads is not True:
              logger.warning(
                  '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''				)
          if self.config.newlines_in_values is not None:
              raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''				)
          return datasets.DatasetInfo(features=self.config.features				)







      def       snake_case	(     self					: str ,       snake_case					: Optional[int]				):
          if not self.config.data_files:
              raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"				)
          __UpperCamelCase  		=						dl_manager.download_and_extract(self.config.data_files				)
          if isinstance(snake_case ,       (str, list, tuple)				):
              __UpperCamelCase  		=						data_files
              if isinstance(snake_case ,       snake_case				):
                  __UpperCamelCase  		=						[files]
              __UpperCamelCase  		=						[dl_manager.iter_files(snake_case				) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,       gen_kwargs={'''files''': files}				)]
          __UpperCamelCase  		=						[]
          for split_name, files in data_files.items():
              if isinstance(snake_case ,       snake_case				):
                  __UpperCamelCase  		=						[files]
              __UpperCamelCase  		=						[dl_manager.iter_files(snake_case				) for file in files]
              splits.append(datasets.SplitGenerator(name=snake_case ,       gen_kwargs={'''files''': files}				)				)
          return splits







      def       snake_case	(     self					: int ,       snake_case					: pa.Table				):
          if self.config.features is not None:
              # adding missing columns
              for column_name in set(self.config.features				) - set(pa_table.column_names				):
                  __UpperCamelCase  		=						self.config.features.arrow_schema.field(snake_case				).type
                  __UpperCamelCase  		=						pa_table.append_column(snake_case ,       pa.array([None] * len(snake_case				) ,       type=snake_case				)				)
              # more expensive cast to support nested structures with keys in a different order
              # allows str <-> int/float or str to Audio for example
              __UpperCamelCase  		=						table_cast(snake_case ,       self.config.features.arrow_schema				)
          return pa_table







      def       snake_case	(     self					: Optional[int] ,       snake_case					: List[str]				):
          for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case				)				):
              # If the file is one json object and if we need to look at the list of items in one specific field
              if self.config.field is not None:
                  with open(snake_case ,       encoding=self.config.encoding ,       errors=self.config.encoding_errors				) as f:
                      __UpperCamelCase  		=						json.load(snake_case				)

                  # We keep only the field we are interested in
                  __UpperCamelCase  		=						dataset[self.config.field]

                  # We accept two format: a list of dicts or a dict of lists
                  if isinstance(snake_case ,       (list, tuple)				):
                      __UpperCamelCase  		=						set().union(*[row.keys() for row in dataset]				)
                      __UpperCamelCase  		=						{col: [row.get(snake_case				) for row in dataset] for col in keys}
                  else:
                      __UpperCamelCase  		=						dataset
                  __UpperCamelCase  		=						pa.Table.from_pydict(snake_case				)
                  yield file_idx, self._cast_table(snake_case				)

              # If the file has one json object per line
              else:
                  with open(snake_case ,       '''rb'''				) as f:
                      __UpperCamelCase  		=						0
                      # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                      # Set a default minimum value of 16kB if the chunk size is really small
                      __UpperCamelCase  		=						max(self.config.chunksize // 32 ,       16 << 10				)
                      __UpperCamelCase  		=						(
                          self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                      )
                      while True:
                          __UpperCamelCase  		=						f.read(self.config.chunksize				)
                          if not batch:
                              break
                          # Finish current line
                          try:
                              batch += f.readline()
                          except (AttributeError, io.UnsupportedOperation):
                              batch += readline(snake_case				)
                          # PyArrow only accepts utf-8 encoded bytes
                          if self.config.encoding != "utf-8":
                              __UpperCamelCase  		=						batch.decode(self.config.encoding ,       errors=snake_case				).encode('''utf-8'''				)
                          try:
                              while True:
                                  try:
                                      __UpperCamelCase  		=						paj.read_json(
                                          io.BytesIO(snake_case				) ,       read_options=paj.ReadOptions(block_size=snake_case				)				)
                                      break
                                  except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                      if (
                                          isinstance(snake_case ,       pa.ArrowInvalid				)
                                          and "straddling" not in str(snake_case				)
                                          or block_size > len(snake_case				)
                                      ):
                                          raise
                                      else:
                                          # Increase the block size in case it was too small.
                                          # The block size will be reset for the next file.
                                          logger.debug(
                                              F"Batch of {len(snake_case				)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."				)
                                          block_size *= 2
                          except pa.ArrowInvalid as e:
                              try:
                                  with open(
                                      snake_case ,       encoding=self.config.encoding ,       errors=self.config.encoding_errors				) as f:
                                      __UpperCamelCase  		=						json.load(snake_case				)
                              except json.JSONDecodeError:
                                  logger.error(F"Failed to read file '{file}' with error {type(snake_case				)}: {e}"				)
                                  raise e
                              # If possible, parse the file as a list of json objects and exit the loop
                              if isinstance(snake_case ,       snake_case				):  # list is the only sequence type supported in JSON
                                  try:
                                      __UpperCamelCase  		=						set().union(*[row.keys() for row in dataset]				)
                                      __UpperCamelCase  		=						{col: [row.get(snake_case				) for row in dataset] for col in keys}
                                      __UpperCamelCase  		=						pa.Table.from_pydict(snake_case				)
                                  except (pa.ArrowInvalid, AttributeError) as e:
                                      logger.error(F"Failed to read file '{file}' with error {type(snake_case				)}: {e}"				)
                                      raise ValueError(F"Not able to read records in the JSON file at {file}."				) from None
                                  yield file_idx, self._cast_table(snake_case				)
                                  break
                              else:
                                  logger.error(F"Failed to read file '{file}' with error {type(snake_case				)}: {e}"				)
                                  raise ValueError(
                                      F"Not able to read records in the JSON file at {file}. "
                                      F"You should probably indicate the field of the JSON file containing your records. "
                                      F"This JSON file contain the following fields: {str(list(dataset.keys()				)				)}. "
                                      F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "				) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                          yield (file_idx, batch_idx), self._cast_table(snake_case				)
                          batch_idx += 1












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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple[Dict, Dict]:

    """simple docstring"""


    __UpperCamelCase  		=						list(state_dict.keys()      )
    __UpperCamelCase  		=						{}
    for key in keys:
        __UpperCamelCase  		=						state_dict.pop(lowercase_      )
        __UpperCamelCase  		=						rename_keys(lowercase_      )
        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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	MusicgenDecoderConfig:

    """simple docstring"""


    if checkpoint == "small":
        # default config values
        __UpperCamelCase  		=						10_24
        __UpperCamelCase  		=						24
        __UpperCamelCase  		=						16
    elif checkpoint == "medium":
        __UpperCamelCase  		=						15_36
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						24
    elif checkpoint == "large":
        __UpperCamelCase  		=						20_48
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						32
    else:
        raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."      )
    __UpperCamelCase  		=						MusicgenDecoderConfig(
        hidden_size=lowercase_					,     ffn_dim=hidden_size * 4					,     num_hidden_layers=lowercase_					,     num_attention_heads=lowercase_					,     )
    return config




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_="cpu"      )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						MusicGen.get_pretrained(lowercase_					,     device=lowercase_      )
    __UpperCamelCase  		=						decoder_config_from_checkpoint(lowercase_      )

    __UpperCamelCase  		=						fairseq_model.lm.state_dict()
    __UpperCamelCase		,      __UpperCamelCase  		=						rename_state_dict(
        lowercase_					,     hidden_size=decoder_config.hidden_size      )

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

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

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

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

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

    # init the composite model
    __UpperCamelCase  		=						MusicgenForConditionalGeneration(text_encoder=lowercase_					,     audio_encoder=lowercase_					,     decoder=lowercase_      )

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

    # 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=lowercase_					,     decoder_input_ids=lowercase_      ).logits

    if logits.shape != (8, 1, 20_48):
        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=lowercase_					,     tokenizer=lowercase_      )

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

    # 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(lowercase_      ).mkdir(exist_ok=lowercase_      )
        logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}"      )
        model.save_pretrained(lowercase_      )
        processor.save_pretrained(lowercase_      )

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


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)






