






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class     __SCREAMING_SNAKE_CASE					(   _lowerCamelCase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = False     ,						_UpperCamelCase = False     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(
												    A__     ,						split=A__     ,						features=A__     ,						cache_dir=A__     ,						keep_in_memory=A__     ,						streaming=A__     ,						num_proc=A__     ,						**A__     ,						)
												lowerCAmelCase__    						=						field
												lowerCAmelCase__    						=						path_or_paths if isinstance(A__     ,						A__   ) else {self.split: path_or_paths}
												lowerCAmelCase__    						=						Json(
												    cache_dir=A__     ,						data_files=A__     ,						features=A__     ,						field=A__     ,						**A__     ,						)







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# Build iterable dataset
												if self.streaming:
																		lowerCAmelCase__    						=						self.builder.as_streaming_dataset(split=self.split   )
												# Build regular (map-style) dataset
												else:
																		lowerCAmelCase__    						=						None
																		lowerCAmelCase__    						=						None
																		lowerCAmelCase__    						=						None
																		lowerCAmelCase__    						=						None

																		self.builder.download_and_prepare(
																		    download_config=A__     ,						download_mode=A__     ,						verification_mode=A__     ,						base_path=A__     ,						num_proc=self.num_proc     ,						)
																		lowerCAmelCase__    						=						self.builder.as_dataset(
																		    split=self.split     ,						verification_mode=A__     ,						in_memory=self.keep_in_memory   )
												return dataset




class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												if num_proc is not None and num_proc <= 0:
																		raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

												lowerCAmelCase__    						=						dataset
												lowerCAmelCase__    						=						path_or_buf
												lowerCAmelCase__    						=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
												lowerCAmelCase__    						=						num_proc
												lowerCAmelCase__    						=						'utf-8'
												lowerCAmelCase__    						=						to_json_kwargs






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.to_json_kwargs.pop('path_or_buf'     ,						A__   )
												lowerCAmelCase__    						=						self.to_json_kwargs.pop('orient'     ,						'records'   )
												lowerCAmelCase__    						=						self.to_json_kwargs.pop('lines'     ,						True if orient == 'records' else False   )
												lowerCAmelCase__    						=						self.to_json_kwargs.pop('index'     ,						False if orient in ['split', 'table'] else True   )
												lowerCAmelCase__    						=						self.to_json_kwargs.pop('compression'     ,						A__   )

												if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																		raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

												if isinstance(self.path_or_buf     ,						(str, bytes, os.PathLike)   ):
																		with fsspec.open(self.path_or_buf     ,						'wb'     ,						compression=A__   ) as buffer:
																								lowerCAmelCase__    						=						self._write(file_obj=A__     ,						orient=A__     ,						lines=A__     ,						index=A__     ,						**self.to_json_kwargs   )
												else:
																		if compression:
																								raise NotImplementedError(
																								    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																								    ' was passed. Please provide a local path instead.'   )
																		lowerCAmelCase__    						=						self._write(
																		    file_obj=self.path_or_buf     ,						orient=A__     ,						lines=A__     ,						index=A__     ,						**self.to_json_kwargs   )
												return written






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						args

												lowerCAmelCase__    						=						query_table(
												    table=self.dataset.data     ,						key=slice(A__     ,						offset + self.batch_size   )     ,						indices=self.dataset._indices     ,						)
												lowerCAmelCase__    						=						batch.to_pandas().to_json(
												    path_or_buf=A__     ,						orient=A__     ,						lines=A__     ,						index=A__     ,						**A__   )
												if not json_str.endswith('\n'   ):
																		json_str += "\n"
												return json_str.encode(self.encoding   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						0

												if self.num_proc is None or self.num_proc == 1:
																		for offset in logging.tqdm(
																		    range(0     ,						len(self.dataset   )     ,						self.batch_size   )     ,						unit='ba'     ,						disable=not logging.is_progress_bar_enabled()     ,						desc='Creating json from Arrow format'     ,						):
																								lowerCAmelCase__    						=						self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																								written += file_obj.write(A__   )
												else:
																		lowerCAmelCase__						,       lowerCAmelCase__    						=						len(self.dataset   ), self.batch_size
																		with multiprocessing.Pool(self.num_proc   ) as pool:
																								for json_str in logging.tqdm(
																								    pool.imap(
																								        self._batch_json     ,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0     ,						A__     ,						A__   )]     ,						)     ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,						unit='ba'     ,						disable=not logging.is_progress_bar_enabled()     ,						desc='Creating json from Arrow format'     ,						):
																														written += file_obj.write(A__   )

												return written













import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__snake_case    :    int         =     logging.get_logger("""transformers.models.encodec""")

__snake_case    :    Tuple         =     {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
__snake_case    :    List[Any]         =     {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
__snake_case    :    str         =     {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
__snake_case    :    str         =     {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
__snake_case    :    Any         =     {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
__snake_case    :    int         =     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case    :    int         =     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case    :    Union[str, Any]         =     []
__snake_case    :    Tuple         =     []







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Optional[int]		)  ->   List[str]:

						"""simple docstring"""



						for attribute in key.split('.'		):
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       UpperCamelCase_		)

						if weight_type is not None:
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       UpperCamelCase_		).shape
						else:
												lowerCAmelCase__    						=						hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
												    F" {value.shape} for {full_name}"		)

						if weight_type == "weight":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_g":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_v":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias":
												lowerCAmelCase__    						=						value
						elif weight_type == "running_mean":
												lowerCAmelCase__    						=						value
						elif weight_type == "running_var":
												lowerCAmelCase__    						=						value
						elif weight_type == "num_batches_tracked":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_ih_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_hh_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_ih_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_hh_l0":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_ih_l1":
												lowerCAmelCase__    						=						value
						elif weight_type == "weight_hh_l1":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_ih_l1":
												lowerCAmelCase__    						=						value
						elif weight_type == "bias_hh_l1":
												lowerCAmelCase__    						=						value
						else:
												lowerCAmelCase__    						=						value

						logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[str]		)  ->   Tuple:

						"""simple docstring"""



						for key in ignore_keys:
												if key.endswith('.*'		):
																		if name.startswith(key[:-1]		):
																								return True
												elif ".*." in key:
																		lowerCAmelCase__						,       lowerCAmelCase__    						=						key.split('.*.'		)
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					str		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]

						if model_name == "encodec_24khz" or "encodec_32khz":
												lowerCAmelCase__    						=						MAPPING_24K
						elif model_name == "encodec_48khz":
												lowerCAmelCase__    						=						MAPPING_48K
						else:
												raise ValueError(F"Unsupported model: {model_name}"		)

						for name, value in orig_dict.items():
												if should_ignore(UpperCamelCase_    ,       UpperCamelCase_		):
																		logger.info(F"{name} was ignored"		)
																		continue

												lowerCAmelCase__    						=						False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								lowerCAmelCase__						,       lowerCAmelCase__    						=						key.split('.*.'		)
																								if prefix in name and suffix in name:
																														lowerCAmelCase__    						=						suffix

																		if key in name:
																								# HACK otherwise .embed gets initialized with .embed_avg too
																								if key.endswith('embed'		) and name.endswith('embed_avg'		):
																														continue

																								lowerCAmelCase__    						=						True
																								if "*" in mapped_key:
																														lowerCAmelCase__    						=						name.split(UpperCamelCase_		)[0].split('.'		)[-2]
																														lowerCAmelCase__    						=						mapped_key.replace('*'    ,       UpperCamelCase_		)
																								if "weight_g" in name:
																														lowerCAmelCase__    						=						'weight_g'
																								elif "weight_v" in name:
																														lowerCAmelCase__    						=						'weight_v'
																								elif "weight_ih_l0" in name:
																														lowerCAmelCase__    						=						'weight_ih_l0'
																								elif "weight_hh_l0" in name:
																														lowerCAmelCase__    						=						'weight_hh_l0'
																								elif "bias_ih_l0" in name:
																														lowerCAmelCase__    						=						'bias_ih_l0'
																								elif "bias_hh_l0" in name:
																														lowerCAmelCase__    						=						'bias_hh_l0'
																								elif "weight_ih_l1" in name:
																														lowerCAmelCase__    						=						'weight_ih_l1'
																								elif "weight_hh_l1" in name:
																														lowerCAmelCase__    						=						'weight_hh_l1'
																								elif "bias_ih_l1" in name:
																														lowerCAmelCase__    						=						'bias_ih_l1'
																								elif "bias_hh_l1" in name:
																														lowerCAmelCase__    						=						'bias_hh_l1'
																								elif "bias" in name:
																														lowerCAmelCase__    						=						'bias'
																								elif "weight" in name:
																														lowerCAmelCase__    						=						'weight'
																								elif "running_mean" in name:
																														lowerCAmelCase__    						=						'running_mean'
																								elif "running_var" in name:
																														lowerCAmelCase__    						=						'running_var'
																								elif "num_batches_tracked" in name:
																														lowerCAmelCase__    						=						'num_batches_tracked'
																								else:
																														lowerCAmelCase__    						=						None
																								set_recursively(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																		continue
												if not is_used:
																		unused_weights.append(UpperCamelCase_		)

						logger.warning(F"Unused weights: {unused_weights}"		)







@torch.no_grad()
def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Optional[int]=None    ,       UpperCamelCase_       :					List[Any]=None    ,       )  ->   Dict:

						"""simple docstring"""



						if config_path is not None:
												lowerCAmelCase__    						=						EncodecConfig.from_pretrained(UpperCamelCase_		)
						else:
												lowerCAmelCase__    						=						EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												lowerCAmelCase__    						=						[8, 5, 4, 4]
												lowerCAmelCase__    						=						[2.2]
												lowerCAmelCase__    						=						64
												lowerCAmelCase__    						=						3_2000
												lowerCAmelCase__    						=						2048
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						False
						elif model_name == "encodec_48khz":
												lowerCAmelCase__    						=						[8, 5, 4, 2]
												lowerCAmelCase__    						=						[3.0, 6.0, 12.0, 24.0]
												lowerCAmelCase__    						=						4_8000
												lowerCAmelCase__    						=						2
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						'time_group_norm'
												lowerCAmelCase__    						=						True
												lowerCAmelCase__    						=						1.0
												lowerCAmelCase__    						=						0.01
						else:
												raise ValueError(F"Unknown model name: {model_name}"		)

						lowerCAmelCase__    						=						EncodecModel(UpperCamelCase_		)

						lowerCAmelCase__    						=						EncodecFeatureExtractor(
						    feature_size=config.audio_channels    ,       sampling_rate=config.sampling_rate    ,       chunk_length_s=config.chunk_length_s    ,       overlap=config.overlap    ,       )
						feature_extractor.save_pretrained(UpperCamelCase_		)

						lowerCAmelCase__    						=						torch.load(UpperCamelCase_		)
						if "best_state" in original_checkpoint:
												# we might have a training state saved, in which case discard the yaml results and just retain the weights
												lowerCAmelCase__    						=						original_checkpoint['best_state']
						recursively_load_weights(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
						model.save_pretrained(UpperCamelCase_		)

						if repo_id:
												print('Pushing to the hub...'		)
												feature_extractor.push_to_hub(UpperCamelCase_		)
												model.push_to_hub(UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Optional[int]         =     argparse.ArgumentParser()
					parser.add_argument(
					    """--model""",
					    default="""encodec_24khz""",
					    type=str,
					    help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
					)
					parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
					)

					__snake_case    :    Dict         =     parser.parse_args()
					convert_checkpoint(
					    args.model,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)





