

import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs



@pytest.mark.parametrize(
    "kwargs, expected"	,[
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 1_0, "max_num_jobs": 1}, [range(1_0      )]),
        ({"num_shards": 1_0, "max_num_jobs": 1_0}, [range(SCREAMING_SNAKE_CASE	,i + 1      ) for i in range(1_0      )]),
        ({"num_shards": 1, "max_num_jobs": 1_0}, [range(1      )]),
        ({"num_shards": 1_0, "max_num_jobs": 3}, [range(0	,4      ), range(4	,7      ), range(7	,1_0      )]),
        ({"num_shards": 3, "max_num_jobs": 1_0}, [range(0	,1      ), range(1	,2      ), range(2	,3      )]),
    ]	,)
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= _distribute_shards(**SCREAMING_SNAKE_CASE      )
	assert out == expected



@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"	,[
        ({"foo": 0}, 1_0, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]	,)
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Dict									= _split_gen_kwargs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	assert out == expected



@pytest.mark.parametrize(
    "gen_kwargs, expected"	,[
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]	,)
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if expected is RuntimeError:
		with pytest.raises(SCREAMING_SNAKE_CASE      ):
			_number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	List[Any]									= _number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE      )
		assert out == expected


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_lowerCAmelCase  :      List[str]			 =logging.get_logger("""transformers.models.speecht5""")

_lowerCAmelCase  :      List[Any]			 ={
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_lowerCAmelCase  :      List[str]			 ={
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_lowerCAmelCase  :      Optional[Any]			 ={
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_lowerCAmelCase  :      int			 ={
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_lowerCAmelCase  :      List[Any]			 ={
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_lowerCAmelCase  :      Any			 ={
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_lowerCAmelCase  :      Dict			 ={
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_lowerCAmelCase  :      List[str]			 ={
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_lowerCAmelCase  :      List[Any]			 ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowerCAmelCase  :      int			 ={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase  :      Dict			 ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase  :      Optional[Any]			 =[]
_lowerCAmelCase  :      int			 =[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
_lowerCAmelCase  :      Any			 =IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_lowerCAmelCase  :      int			 =IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_lowerCAmelCase  :      Union[str, Any]			 =IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for attribute in key.split("."      ):
		UpperCAmelCase__:	List[Any]									= getattr(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	if weight_type is not None:
		UpperCAmelCase__:	Optional[int]									= getattr(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ).shape
	else:
		UpperCAmelCase__:	int									= 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":
		UpperCAmelCase__:	Union[str, Any]									= value
	elif weight_type == "weight_g":
		UpperCAmelCase__:	str									= value
	elif weight_type == "weight_v":
		UpperCAmelCase__:	Optional[int]									= value
	elif weight_type == "bias":
		UpperCAmelCase__:	Union[str, Any]									= value
	elif weight_type == "running_mean":
		UpperCAmelCase__:	Optional[Any]									= value
	elif weight_type == "running_var":
		UpperCAmelCase__:	Union[str, Any]									= value
	elif weight_type == "num_batches_tracked":
		UpperCAmelCase__:	Optional[int]									= value
	else:
		UpperCAmelCase__:	Union[str, Any]									= value

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for key in ignore_keys:
		if key.endswith(".*"      ):
			if name.startswith(key[:-1]      ):
				return True
		elif ".*." in key:
			UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= key.split(".*."      )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= []

	if task == "s2t":
		UpperCAmelCase__:	List[Any]									= hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase__:	Tuple									= MAPPING_S2T
		UpperCAmelCase__:	int									= IGNORE_KEYS_S2T
	elif task == "t2s":
		UpperCAmelCase__:	Dict									= None
		UpperCAmelCase__:	Any									= MAPPING_T2S
		UpperCAmelCase__:	List[Any]									= IGNORE_KEYS_T2S
	elif task == "s2s":
		UpperCAmelCase__:	List[Any]									= hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase__:	Union[str, Any]									= MAPPING_S2S
		UpperCAmelCase__:	Tuple									= IGNORE_KEYS_S2S
	else:
		raise ValueError(f"Unsupported task: {task}"      )

	for name, value in fairseq_dict.items():
		if should_ignore(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
			logger.info(f"{name} was ignored"      )
			continue

		UpperCAmelCase__:	Any									= False
		if "conv_layers" in name:
			load_conv_layer(
			    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,hf_model.config.feat_extract_norm == "group"	,)
			UpperCAmelCase__:	Optional[Any]									= True
		else:
			for key, mapped_key in MAPPING.items():
				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

				if "*" in key:
					UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= key.split(".*."      )
					if prefix in name and suffix in name:
						UpperCAmelCase__:	str									= suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					UpperCAmelCase__:	Optional[int]									= True
					if "*" in mapped_key:
						UpperCAmelCase__:	Any									= name.split(SCREAMING_SNAKE_CASE      )[0].split("."      )[-2]
						UpperCAmelCase__:	Dict									= mapped_key.replace("*"	,SCREAMING_SNAKE_CASE      )
					if "weight_g" in name:
						UpperCAmelCase__:	List[str]									= "weight_g"
					elif "weight_v" in name:
						UpperCAmelCase__:	Union[str, Any]									= "weight_v"
					elif "bias" in name:
						UpperCAmelCase__:	str									= "bias"
					elif "weight" in name:
						UpperCAmelCase__:	List[Any]									= "weight"
					elif "running_mean" in name:
						UpperCAmelCase__:	List[Any]									= "running_mean"
					elif "running_var" in name:
						UpperCAmelCase__:	Optional[Any]									= "running_var"
					elif "num_batches_tracked" in name:
						UpperCAmelCase__:	Optional[int]									= "num_batches_tracked"
					else:
						UpperCAmelCase__:	Dict									= None
					set_recursively(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
				continue
		if not is_used:
			unused_weights.append(SCREAMING_SNAKE_CASE      )

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= full_name.split("conv_layers."      )[-1]
	UpperCAmelCase__:	Union[str, Any]									= name.split("."      )
	UpperCAmelCase__:	Dict									= int(items[0]      )
	UpperCAmelCase__:	List[Any]									= int(items[1]      )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."      )
			UpperCAmelCase__:	Optional[Any]									= value
			logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."      )
			UpperCAmelCase__:	Optional[int]									= value
			logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."      )
			UpperCAmelCase__:	str									= value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."      )
			UpperCAmelCase__:	Union[str, Any]									= value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
	else:
		unused_weights.append(SCREAMING_SNAKE_CASE      )



@torch.no_grad()
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,):
	if config_path is not None:
		UpperCAmelCase__:	Optional[int]									= SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	Tuple									= SpeechTaConfig()

	if task == "s2t":
		UpperCAmelCase__:	List[Any]									= config.max_text_positions
		UpperCAmelCase__:	List[str]									= SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE      )
	elif task == "t2s":
		UpperCAmelCase__:	Dict									= 1_8_7_6
		UpperCAmelCase__:	Dict									= 6_0_0
		UpperCAmelCase__:	str									= config.max_speech_positions
		UpperCAmelCase__:	int									= SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE      )
	elif task == "s2s":
		UpperCAmelCase__:	Any									= 1_8_7_6
		UpperCAmelCase__:	List[str]									= config.max_speech_positions
		UpperCAmelCase__:	Union[str, Any]									= SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE      )
	else:
		raise ValueError(f"Unknown task name: {task}"      )

	if vocab_path:
		UpperCAmelCase__:	int									= SpeechTaTokenizer(SCREAMING_SNAKE_CASE	,model_max_length=config.max_text_positions      )

		# Mask token behaves like a normal word, i.e. include the space before it
		UpperCAmelCase__:	List[str]									= AddedToken("<mask>"	,lstrip=SCREAMING_SNAKE_CASE	,rstrip=SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	List[Any]									= mask_token
		tokenizer.add_special_tokens({"mask_token": mask_token}      )
		tokenizer.add_tokens(["<ctc_blank>"]      )

	UpperCAmelCase__:	int									= SpeechTaFeatureExtractor()
	UpperCAmelCase__:	Optional[Any]									= SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE	,feature_extractor=SCREAMING_SNAKE_CASE      )
	processor.save_pretrained(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	int									= torch.load(SCREAMING_SNAKE_CASE      )
	recursively_load_weights(fairseq_checkpoint["model"]	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	model.save_pretrained(SCREAMING_SNAKE_CASE      )

	if repo_id:
		print("Pushing to the hub..."      )
		processor.push_to_hub(SCREAMING_SNAKE_CASE      )
		model.push_to_hub(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =argparse.ArgumentParser()
					parser.add_argument(
					    """--task""",
					    default="""s2t""",
					    type=str,
					    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
					)
					parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
					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."""
					)

					_lowerCAmelCase  :      Optional[int]			 =parser.parse_args()
					convert_speechta_checkpoint(
					    args.task,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.vocab_path,
					    args.push_to_hub,
					)