






from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


__snake_case    :    Dict         =     [
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

__snake_case    :    Dict         =     [
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]







def 	_UpperCamelCase   (    )  ->   Dict:

						"""simple docstring"""



						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       bootstrap_aggregation=UpperCamelCase_    ,       rouge_keys=['rouge2', 'rougeL']		)
						assert isinstance(UpperCamelCase_    ,       UpperCamelCase_		)
						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       bootstrap_aggregation=UpperCamelCase_    ,       rouge_keys=['rouge2']		)
						assert (
						    pd.DataFrame(no_aggregation['rouge2']		).fmeasure.mean()
						    == pd.DataFrame(no_aggregation_just_ra['rouge2']		).fmeasure.mean()
						)







def 	_UpperCamelCase   (    )  ->   Tuple:

						"""simple docstring"""



						lowerCAmelCase__    						=						'rougeLsum'
						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       newline_sep=UpperCamelCase_    ,       rouge_keys=[k]		)[k]
						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       newline_sep=UpperCamelCase_    ,       rouge_keys=[k]		)[k]
						assert score > score_no_sep







def 	_UpperCamelCase   (    )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						['rouge1', 'rouge2', 'rougeL']
						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       newline_sep=UpperCamelCase_    ,       rouge_keys=UpperCamelCase_		)
						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       newline_sep=UpperCamelCase_    ,       rouge_keys=UpperCamelCase_		)
						assert score_sep == score_no_sep







def 	_UpperCamelCase   (    )  ->   List[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[
						    'Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.',
						    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .',
						]
						lowerCAmelCase__    						=						[
						    'Margot Frank, died in 1945, a month earlier than previously thought.',
						    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'
						    ' the final seconds on board Flight 9525.',
						]
						assert calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       newline_sep=UpperCamelCase_		) == calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       newline_sep=UpperCamelCase_		)







def 	_UpperCamelCase   (    )  ->   Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[
						    '" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '
						]
						lowerCAmelCase__    						=						[
						    ' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'
						]

						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       rouge_keys=['rougeLsum']    ,       newline_sep=UpperCamelCase_		)['rougeLsum']
						lowerCAmelCase__    						=						calculate_rouge(UpperCamelCase_    ,       UpperCamelCase_    ,       rouge_keys=['rougeLsum']		)['rougeLsum']
						assert new_score > prev_score







def 	_UpperCamelCase   (    )  ->   Tuple:

						"""simple docstring"""



						lowerCAmelCase__    						=						Path('examples/seq2seq/test_data/wmt_en_ro'		)
						lowerCAmelCase__    						=						calculate_rouge_path(data_dir.joinpath('test.source'		)    ,       data_dir.joinpath('test.target'		)		)
						assert isinstance(UpperCamelCase_    ,       UpperCamelCase_		)
						lowerCAmelCase__    						=						calculate_rouge_path(
						    data_dir.joinpath('test.source'		)    ,       data_dir.joinpath('test.target'		)    ,       bootstrap_aggregation=UpperCamelCase_		)
						assert isinstance(UpperCamelCase_    ,       UpperCamelCase_		)













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,
					)





