


'''simple docstring'''




import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				List[str]				)    ->      List[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= XCLIPTextConfig()

	# derive patch size from model name
	lowerCAmelCase					:    str      							= model_name.find('patch'				)
	lowerCAmelCase					:    Tuple      							= int(model_name[start_idx + len('patch'				) : start_idx + len('patch'				) + 2]				)
	lowerCAmelCase					:    List[Any]      							= XCLIPVisionConfig(patch_size=_A					,   num_frames=_A				)

	if "large" in model_name:
		lowerCAmelCase					:    List[Any]      							= 7_68
		lowerCAmelCase					:    List[str]      							= 30_72
		lowerCAmelCase					:    Dict      							= 12

		lowerCAmelCase					:    Tuple      							= 10_24
		lowerCAmelCase					:    Any      							= 40_96
		lowerCAmelCase					:    str      							= 16
		lowerCAmelCase					:    Union[str, Any]      							= 24
		lowerCAmelCase					:    Union[str, Any]      							= 7_68
		lowerCAmelCase					:    Optional[Any]      							= 30_72

	if model_name == "xclip-large-patch14-16-frames":
		lowerCAmelCase					:    Optional[Any]      							= 3_36

	lowerCAmelCase					:    Tuple      							= XCLIPConfig.from_text_vision_configs(_A					,   _A				)

	if "large" in model_name:
		lowerCAmelCase					:    int      							= 7_68

	return config

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      Optional[int]:




	"""simple docstring"""





	if name == "token_embedding.weight":
		lowerCAmelCase					:    Optional[Any]      							= name.replace('token_embedding.weight'					,   'text_model.embeddings.token_embedding.weight'				)
	if name == "positional_embedding":
		lowerCAmelCase					:    Dict      							= name.replace('positional_embedding'					,   'text_model.embeddings.position_embedding.weight'				)
	if "ln_1" in name:
		lowerCAmelCase					:    Optional[Any]      							= name.replace('ln_1'					,   'layer_norm1'				)
	if "ln_2" in name:
		lowerCAmelCase					:    List[Any]      							= name.replace('ln_2'					,   'layer_norm2'				)
	if "c_fc" in name:
		lowerCAmelCase					:    Dict      							= name.replace('c_fc'					,   'fc1'				)
	if "c_proj" in name:
		lowerCAmelCase					:    Dict      							= name.replace('c_proj'					,   'fc2'				)
	if name.startswith('transformer.resblocks'				):
		lowerCAmelCase					:    Tuple      							= name.replace('transformer.resblocks'					,   'text_model.encoder.layers'				)
	if "attn.out_proj" in name and "message" not in name:
		lowerCAmelCase					:    Optional[Any]      							= name.replace('attn.out_proj'					,   'self_attn.out_proj'				)
	if "ln_final" in name:
		lowerCAmelCase					:    List[Any]      							= name.replace('ln_final'					,   'text_model.final_layer_norm'				)
	# visual encoder
	if name == "visual.class_embedding":
		lowerCAmelCase					:    int      							= name.replace('visual.class_embedding'					,   'vision_model.embeddings.class_embedding'				)
	if name == "visual.positional_embedding":
		lowerCAmelCase					:    Union[str, Any]      							= name.replace('visual.positional_embedding'					,   'vision_model.embeddings.position_embedding.weight'				)
	if name.startswith('visual.transformer.resblocks'				):
		lowerCAmelCase					:    int      							= name.replace('visual.transformer.resblocks'					,   'vision_model.encoder.layers'				)
	if "visual.conv1" in name:
		lowerCAmelCase					:    str      							= name.replace('visual.conv1'					,   'vision_model.embeddings.patch_embedding'				)
	if "visual.ln_pre" in name:
		lowerCAmelCase					:    List[Any]      							= name.replace('visual.ln_pre'					,   'vision_model.pre_layernorm'				)
	if "visual.ln_post" in name:
		lowerCAmelCase					:    List[str]      							= name.replace('visual.ln_post'					,   'vision_model.post_layernorm'				)
	if "visual.proj" in name:
		lowerCAmelCase					:    int      							= name.replace('visual.proj'					,   'visual_projection.weight'				)
	if "text_projection" in name:
		lowerCAmelCase					:    List[Any]      							= name.replace('text_projection'					,   'text_projection.weight'				)
	# things on top
	if "prompts_visual_proj" in name:
		lowerCAmelCase					:    str      							= name.replace('prompts_visual_proj'					,   'prompts_visual_projection'				)
	if "prompts_visual_ln" in name:
		lowerCAmelCase					:    str      							= name.replace('prompts_visual_ln'					,   'prompts_visual_layernorm'				)
	# mit
	if name == "mit.positional_embedding":
		lowerCAmelCase					:    str      							= name.replace('positional'					,   'position'				)
	if name.startswith('mit.resblocks'				):
		lowerCAmelCase					:    str      							= name.replace('mit.resblocks'					,   'mit.encoder.layers'				)
	# prompts generator
	if name.startswith('prompts_generator.norm'				):
		lowerCAmelCase					:    List[str]      							= name.replace('prompts_generator.norm'					,   'prompts_generator.layernorm'				)

	return name

def 			__UpperCamelCase    (    _A    :				Optional[int]					,   _A    :				Any				)    ->      str:




	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		lowerCAmelCase					:    List[str]      							= orig_state_dict.pop(_A				)

		if "attn.in_proj" in key:
			lowerCAmelCase					:    Optional[int]      							= key.split('.'				)
			if key.startswith('visual'				):
				lowerCAmelCase					:    str      							= key_split[3]
				lowerCAmelCase					:    int      							= config.vision_config.hidden_size
				if "message_attn" in key:
					if "weight" in key:
						lowerCAmelCase					:    Optional[Any]      							= val[
						    :dim, :
						]
						lowerCAmelCase					:    Dict      							= val[
						    dim : dim * 2, :
						]
						lowerCAmelCase					:    int      							= val[
						    -dim:, :
						]
					else:
						lowerCAmelCase					:    int      							= val[
						    :dim
						]
						lowerCAmelCase					:    Union[str, Any]      							= val[
						    dim : dim * 2
						]
						lowerCAmelCase					:    Optional[int]      							= val[
						    -dim:
						]
				else:
					if "weight" in key:
						lowerCAmelCase					:    Any      							= val[
						    :dim, :
						]
						lowerCAmelCase					:    List[str]      							= val[
						    dim : dim * 2, :
						]
						lowerCAmelCase					:    str      							= val[
						    -dim:, :
						]
					else:
						lowerCAmelCase					:    int      							= val[:dim]
						lowerCAmelCase					:    List[Any]      							= val[
						    dim : dim * 2
						]
						lowerCAmelCase					:    str      							= val[-dim:]
			elif key.startswith('mit'				):
				lowerCAmelCase					:    List[Any]      							= key_split[2]
				lowerCAmelCase					:    Union[str, Any]      							= config.vision_config.mit_hidden_size
				if "weight" in key:
					lowerCAmelCase					:    List[str]      							= val[:dim, :]
					lowerCAmelCase					:    str      							= val[dim : dim * 2, :]
					lowerCAmelCase					:    Tuple      							= val[-dim:, :]
				else:
					lowerCAmelCase					:    Optional[int]      							= val[:dim]
					lowerCAmelCase					:    str      							= val[dim : dim * 2]
					lowerCAmelCase					:    int      							= val[-dim:]
			else:
				lowerCAmelCase					:    str      							= key_split[2]
				lowerCAmelCase					:    str      							= config.text_config.hidden_size
				if "weight" in key:
					lowerCAmelCase					:    Union[str, Any]      							= val[:dim, :]
					lowerCAmelCase					:    Union[str, Any]      							= val[
					    dim : dim * 2, :
					]
					lowerCAmelCase					:    Tuple      							= val[-dim:, :]
				else:
					lowerCAmelCase					:    Union[str, Any]      							= val[:dim]
					lowerCAmelCase					:    Dict      							= val[
					    dim : dim * 2
					]
					lowerCAmelCase					:    Optional[int]      							= val[-dim:]
		else:
			lowerCAmelCase					:    int      							= rename_key(_A				)
			if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
				lowerCAmelCase					:    Dict      							= val.T
			lowerCAmelCase					:    Optional[Any]      							= val

	return orig_state_dict

def 			__UpperCamelCase    (    _A    :				Any				)    ->      Optional[Any]:




	"""simple docstring"""





	if num_frames == 8:
		lowerCAmelCase					:    Dict      							= 'eating_spaghetti_8_frames.npy'
	elif num_frames == 16:
		lowerCAmelCase					:    Union[str, Any]      							= 'eating_spaghetti.npy'
	elif num_frames == 32:
		lowerCAmelCase					:    Optional[int]      							= 'eating_spaghetti_32_frames.npy'
	lowerCAmelCase					:    List[Any]      							= hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'					,   filename=_A					,   repo_type='dataset'					,   )
	lowerCAmelCase					:    Any      							= np.load(_A				)
	return list(_A				)

def 			__UpperCamelCase    (    _A    :				Optional[Any]					,   _A    :				Union[str, Any]=None					,   _A    :				Dict=False				)    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= {
	    # fully supervised kinetics-400 checkpoints
	    'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
	    'xclip-base-patch32-16-frames': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
	    ),
	    'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
	    'xclip-base-patch16-16-frames': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
	    ),
	    'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
	    'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
	    # fully supervised kinetics-600 checkpoints
	    'xclip-base-patch16-kinetics-600': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
	    ),
	    'xclip-base-patch16-kinetics-600-16-frames': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
	    ),
	    'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
	    # few shot
	    'xclip-base-patch16-hmdb-2-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
	    ),
	    'xclip-base-patch16-hmdb-4-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
	    ),
	    'xclip-base-patch16-hmdb-8-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
	    ),
	    'xclip-base-patch16-hmdb-16-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
	    ),
	    'xclip-base-patch16-ucf-2-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
	    ),
	    'xclip-base-patch16-ucf-4-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
	    ),
	    'xclip-base-patch16-ucf-8-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
	    ),
	    'xclip-base-patch16-ucf-16-shot': (
	        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
	    ),
	    # zero shot
	    'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
	}

	lowerCAmelCase					:    Any      							= model_to_url[model_name]
	lowerCAmelCase					:    Optional[Any]      							= 8
	if "16-frames" in model_name:
		lowerCAmelCase					:    Union[str, Any]      							= 16
	elif "shot" in model_name:
		lowerCAmelCase					:    Optional[Any]      							= 32

	lowerCAmelCase					:    Dict      							= get_xclip_config(_A					,   _A				)
	lowerCAmelCase					:    Tuple      							= XCLIPModel(_A				)
	model.eval()

	if "drive" in checkpoint_url:
		lowerCAmelCase					:    Union[str, Any]      							= 'pytorch_model.bin'
		gdown.cached_download(_A					,   _A					,   quiet=_A				)
		lowerCAmelCase					:    Optional[Any]      							= torch.load(_A					,   map_location='cpu'				)['model']
	else:
		lowerCAmelCase					:    List[str]      							= torch.hub.load_state_dict_from_url(_A				)['model']

	lowerCAmelCase					:    Optional[int]      							= convert_state_dict(_A					,   _A				)

	lowerCAmelCase					:    int      							= XCLIPModel(_A				)
	lowerCAmelCase							,   lowerCAmelCase					:    Union[str, Any]      							= model.load_state_dict(_A					,   strict=_A				)
	assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
	model.eval()

	lowerCAmelCase					:    int      							= 3_36 if model_name == 'xclip-large-patch14-16-frames' else 2_24
	lowerCAmelCase					:    Dict      							= VideoMAEImageProcessor(size=_A				)
	lowerCAmelCase					:    Optional[int]      							= CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'				)
	lowerCAmelCase					:    Union[str, Any]      							= CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'				)
	lowerCAmelCase					:    Optional[int]      							= XCLIPProcessor(image_processor=_A					,   tokenizer=_A				)

	lowerCAmelCase					:    Dict      							= prepare_video(_A				)
	lowerCAmelCase					:    List[str]      							= processor(
	    text=['playing sports', 'eating spaghetti', 'go shopping']					,   videos=_A					,   return_tensors='pt'					,   padding=_A				)

	print('Shape of pixel values:'					,   inputs.pixel_values.shape				)

	with torch.no_grad():
		lowerCAmelCase					:    Any      							= model(**_A				)

	# Verify outputs
	lowerCAmelCase					:    str      							= outputs.logits_per_video
	lowerCAmelCase					:    List[str]      							= logits_per_video.softmax(dim=1				)
	print('Probs:'					,   _A				)
	# kinetics-400
	if model_name == "xclip-base-patch32":
		lowerCAmelCase					:    Any      							= torch.tensor([[0.00_19, 0.99_51, 0.00_30]]				)
	elif model_name == "xclip-base-patch32-16-frames":
		lowerCAmelCase					:    str      							= torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]				)
	elif model_name == "xclip-base-patch16":
		lowerCAmelCase					:    Union[str, Any]      							= torch.tensor([[0.00_83, 0.96_81, 0.02_36]]				)
	elif model_name == "xclip-base-patch16-16-frames":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]				)
	elif model_name == "xclip-large-patch14":
		lowerCAmelCase					:    List[str]      							= torch.tensor([[0.00_62, 0.98_64, 0.00_75]]				)
	elif model_name == "xclip-large-patch14-16-frames":
		lowerCAmelCase					:    str      							= torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]				)
	# kinetics-600
	elif model_name == "xclip-base-patch16-kinetics-600":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[0.05_55, 0.89_14, 0.05_31]]				)
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		lowerCAmelCase					:    str      							= torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]				)
	elif model_name == "xclip-large-patch14-kinetics-600":
		lowerCAmelCase					:    str      							= torch.tensor([[0.00_36, 0.99_20, 0.00_45]]				)
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		lowerCAmelCase					:    Tuple      							= torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]				)
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		lowerCAmelCase					:    List[str]      							= torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]				)
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		lowerCAmelCase					:    Optional[int]      							= torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]				)
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		lowerCAmelCase					:    List[Any]      							= torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]				)
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		lowerCAmelCase					:    Union[str, Any]      							= torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]				)
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]				)
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[0.00_27, 0.99_04, 0.00_70]]				)
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		lowerCAmelCase					:    Any      							= torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]				)
	# zero shot
	elif model_name == "xclip-base-patch16-zero-shot":
		lowerCAmelCase					:    List[str]      							= torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]				)
	else:
		raise ValueError(F"Model name {model_name} not supported"				)
	assert torch.allclose(_A					,   _A					,   atol=1e-3				)
	print('Looks ok!'				)

	if pytorch_dump_folder_path is not None:
		print(F"Saving model {model_name} to {pytorch_dump_folder_path}"				)
		model.save_pretrained(_A				)

	if push_to_hub:
		print('Pushing model, processor and slow tokenizer files to the hub...'				)
		model.push_to_hub(_A					,   organization='nielsr'				)
		processor.push_to_hub(_A					,   organization='nielsr'				)
		slow_tokenizer.push_to_hub(_A					,   organization='nielsr'				)


if __name__ == "__main__":
				_lowerCAmelCase  :		Optional[int]		     =	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='xclip-base-patch32',
				    type=str,
				    help='Name of the model.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				_lowerCAmelCase  :		Dict		     =	parser.parse_args()
				convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates				)				):
		lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"upsamples.{i}.1.weight_g"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.weight_v"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.bias"]

	for i in range(len(config.upsample_rates				) * len(config.resblock_kernel_sizes				)				):
		for j in range(len(config.resblock_dilation_sizes				)				):
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
			lowerCAmelCase					:    str      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

			lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

	lowerCAmelCase					:    List[Any]      							= checkpoint['output_conv.1.weight_g']
	lowerCAmelCase					:    List[str]      							= checkpoint['output_conv.1.weight_v']
	lowerCAmelCase					:    Optional[Any]      							= checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    int      							= torch.from_numpy(_A				).float()

	model.save_pretrained(_A				)

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


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	argparse.ArgumentParser()
				parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
				parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
				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  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
