


'''simple docstring'''

import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


lowerCAmelCase				:Optional[int]        =      {
    '''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
    '''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
    '''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
    '''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
    '''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
    '''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
    '''mask_downscaling.0''': '''mask_embed.conv1''',
    '''mask_downscaling.1''': '''mask_embed.layer_norm1''',
    '''mask_downscaling.3''': '''mask_embed.conv2''',
    '''mask_downscaling.4''': '''mask_embed.layer_norm2''',
    '''mask_downscaling.6''': '''mask_embed.conv3''',
    '''point_embeddings''': '''point_embed''',
    '''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
    '''image_encoder''': '''vision_encoder''',
    '''neck.0''': '''neck.conv1''',
    '''neck.1''': '''neck.layer_norm1''',
    '''neck.2''': '''neck.conv2''',
    '''neck.3''': '''neck.layer_norm2''',
    '''patch_embed.proj''': '''patch_embed.projection''',
    '''.norm''': '''.layer_norm''',
    '''blocks''': '''layers''',
}





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   int					     =					{}
	state_dict.pop('pixel_mean'		,		lowerCamelCase__		)
	state_dict.pop('pixel_std'		,		lowerCamelCase__		)

	__magic_name__	:   Optional[Any]					     =					R'.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'

	for key, value in state_dict.items():
		for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
			if key_to_modify in key:
				__magic_name__	:   Optional[Any]					     =					key.replace(lowerCamelCase__		,		lowerCamelCase__		)

		if re.match(lowerCamelCase__		,		lowerCamelCase__		):
			__magic_name__	:   Tuple					     =					int(re.match(lowerCamelCase__		,		lowerCamelCase__		).group(2		)		)
			if layer_nb == 0:
				__magic_name__	:   Union[str, Any]					     =					key.replace('layers.0'		,		'proj_in'		)
			elif layer_nb == 1:
				__magic_name__	:   Optional[Any]					     =					key.replace('layers.1'		,		'layers.0'		)
			elif layer_nb == 2:
				__magic_name__	:   Tuple					     =					key.replace('layers.2'		,		'proj_out'		)

		__magic_name__	:   Union[str, Any]					     =					value

	__magic_name__	:   Optional[Any]					     =					model_state_dict[
	    'prompt_encoder.shared_embedding.positional_embedding'
	]

	return model_state_dict





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[int]="ybelkada/segment-anything"		):





	"""simple docstring"""







	__magic_name__	:   str					     =					hf_hub_download(lowerCamelCase__		,		f'checkpoints/{model_name}.pth'		)

	if "sam_vit_b" in model_name:
		__magic_name__	:   str					     =					SamConfig()
	elif "sam_vit_l" in model_name:
		__magic_name__	:   List[Any]					     =					SamVisionConfig(
		    hidden_size=1024		,		num_hidden_layers=24		,		num_attention_heads=16		,		global_attn_indexes=[5, 11, 17, 23]		,		)

		__magic_name__	:   Optional[int]					     =					SamConfig(
		    vision_config=lowerCamelCase__		,		)
	elif "sam_vit_h" in model_name:
		__magic_name__	:   str					     =					SamVisionConfig(
		    hidden_size=1280		,		num_hidden_layers=32		,		num_attention_heads=16		,		global_attn_indexes=[7, 15, 23, 31]		,		)

		__magic_name__	:   Tuple					     =					SamConfig(
		    vision_config=lowerCamelCase__		,		)

	__magic_name__	:   Union[str, Any]					     =					torch.load(lowerCamelCase__		,		map_location='cpu'		)
	__magic_name__	:   List[str]					     =					replace_keys(lowerCamelCase__		)

	__magic_name__	:   Any					     =					SamImageProcessor()

	__magic_name__	:   List[str]					     =					SamProcessor(image_processor=lowerCamelCase__		)
	__magic_name__	:   str					     =					SamModel(lowerCamelCase__		)

	hf_model.load_state_dict(lowerCamelCase__		)
	__magic_name__	:   List[str]					     =					hf_model.to('cuda'		)

	__magic_name__	:   Tuple					     =					'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
	__magic_name__	:   Optional[Any]					     =					Image.open(requests.get(lowerCamelCase__		,		stream=lowerCamelCase__		).raw		).convert('RGB'		)

	__magic_name__	:   List[str]					     =					[[[400, 650]]]
	__magic_name__	:   str					     =					[[1]]

	__magic_name__	:   int					     =					processor(images=np.array(lowerCamelCase__		)		,		return_tensors='pt'		).to('cuda'		)

	with torch.no_grad():
		__magic_name__	:   Any					     =					hf_model(**lowerCamelCase__		)
	__magic_name__	:   str					     =					output.iou_scores.squeeze()

	if model_name == "sam_vit_h_4b8939":
		assert scores[-1].item() == 0.579_8902_5115_9668

		__magic_name__	:   Optional[int]					     =					processor(
		    images=np.array(lowerCamelCase__		)		,		input_points=lowerCamelCase__		,		input_labels=lowerCamelCase__		,		return_tensors='pt'		).to('cuda'		)

		with torch.no_grad():
			__magic_name__	:   Union[str, Any]					     =					hf_model(**lowerCamelCase__		)
		__magic_name__	:   int					     =					output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9712_6030_9219_3604

		__magic_name__	:   Any					     =					((75, 275, 1725, 850),)

		__magic_name__	:   Optional[Any]					     =					processor(images=np.array(lowerCamelCase__		)		,		input_boxes=lowerCamelCase__		,		return_tensors='pt'		).to('cuda'		)

		with torch.no_grad():
			__magic_name__	:   List[str]					     =					hf_model(**lowerCamelCase__		)
		__magic_name__	:   Tuple					     =					output.iou_scores.squeeze()

		assert scores[-1].item() == 0.8686_0156_0592_6514

		# Test with 2 points and 1 image.
		__magic_name__	:   Dict					     =					[[[400, 650], [800, 650]]]
		__magic_name__	:   Any					     =					[[1, 1]]

		__magic_name__	:   Tuple					     =					processor(
		    images=np.array(lowerCamelCase__		)		,		input_points=lowerCamelCase__		,		input_labels=lowerCamelCase__		,		return_tensors='pt'		).to('cuda'		)

		with torch.no_grad():
			__magic_name__	:   int					     =					hf_model(**lowerCamelCase__		)
		__magic_name__	:   int					     =					output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
							lowerCAmelCase				:List[Any]        =      argparse.ArgumentParser()
							lowerCAmelCase				:Union[str, Any]        =      ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
							parser.add_argument(
							    '''--model_name''',
							    default='''sam_vit_h_4b8939''',
							    choices=choices,
							    type=str,
							    help='''Path to hf config.json of model to convert''',
							)
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							    help='''Whether to push the model and processor to the hub after converting''',
							)
							parser.add_argument(
							    '''--model_hub_id''',
							    default='''ybelkada/segment-anything''',
							    choices=choices,
							    type=str,
							    help='''Path to hf config.json of model to convert''',
							)

							lowerCAmelCase				:Union[str, Any]        =      parser.parse_args()

							convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase				:str        =      logging.get_logger(__name__)

lowerCAmelCase				:str        =      {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
lowerCAmelCase				:List[str]        =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					{}
	with open(lowerCAmelCase		,		'r'		) as file:
		for line_number, line in enumerate(lowerCAmelCase		):
			__magic_name__	:   Optional[Any]					     =					line.strip()
			if line:
				__magic_name__	:   Optional[int]					     =					line.split()
				__magic_name__	:   Any					     =					line_number
				__magic_name__	:   Union[str, Any]					     =					words[0]
				__magic_name__	:   Dict					     =					value
	return result





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	for attribute in key.split('.'		):
		__magic_name__	:   Optional[Any]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Tuple					     =					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase		):
			__magic_name__	:   Optional[Any]					     =					PARAM_MAPPING[full_name.split('.'		)[-1]]
			__magic_name__	:   List[Any]					     =					'param'

	if weight_type is not None and weight_type != "param":
		__magic_name__	:   List[str]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		).shape
	elif weight_type is not None and weight_type == "param":
		__magic_name__	:   Tuple					     =					hf_pointer
		for attribute in hf_param_name.split('.'		):
			__magic_name__	:   str					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   Union[str, Any]					     =					shape_pointer.shape

		# let's reduce dimension
		__magic_name__	:   int					     =					value[0]
	else:
		__magic_name__	:   Optional[Any]					     =					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":
		__magic_name__	:   Optional[Any]					     =					value
	elif weight_type == "weight_g":
		__magic_name__	:   List[str]					     =					value
	elif weight_type == "weight_v":
		__magic_name__	:   Optional[int]					     =					value
	elif weight_type == "bias":
		__magic_name__	:   Optional[Any]					     =					value
	elif weight_type == "param":
		for attribute in hf_param_name.split('.'		):
			__magic_name__	:   Optional[int]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   List[str]					     =					value
	else:
		__magic_name__	:   int					     =					value

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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase		):
			__magic_name__	:   List[Any]					     =					PARAM_MAPPING[full_name.split('.'		)[-1]]
			__magic_name__	:   Dict					     =					'param'

	if weight_type is not None and weight_type != "param":
		__magic_name__	:   Union[str, Any]					     =					'.'.join([key, weight_type]		)
	elif weight_type is not None and weight_type == "param":
		__magic_name__	:   Optional[Any]					     =					'.'.join([key, hf_param_name]		)
	else:
		__magic_name__	:   int					     =					key

	__magic_name__	:   int					     =					value if 'lm_head' in full_key else value[0]


lowerCAmelCase				:int        =      {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Tuple=None		,		lowerCAmelCase    :     Tuple=None		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					False
	for key, mapped_key in MAPPING.items():
		__magic_name__	:   int					     =					'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
		if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
			__magic_name__	:   Union[str, Any]					     =					True
			if "*" in mapped_key:
				__magic_name__	:   List[Any]					     =					name.split(lowerCAmelCase		)[0].split('.'		)[-2]
				__magic_name__	:   List[str]					     =					mapped_key.replace('*'		,		lowerCAmelCase		)
			if "weight_g" in name:
				__magic_name__	:   str					     =					'weight_g'
			elif "weight_v" in name:
				__magic_name__	:   Optional[int]					     =					'weight_v'
			elif "bias" in name:
				__magic_name__	:   int					     =					'bias'
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				__magic_name__	:   List[str]					     =					'weight'
			else:
				__magic_name__	:   Any					     =					None
			if hf_dict is not None:
				rename_dict(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
			else:
				set_recursively(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
			return is_used
	return is_used





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					[]
	__magic_name__	:   Any					     =					fairseq_model.state_dict()

	__magic_name__	:   Optional[int]					     =					hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		__magic_name__	:   Optional[Any]					     =					False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		hf_model.config.feat_extract_norm == 'group'		,		)
			__magic_name__	:   Optional[int]					     =					True
		else:
			__magic_name__	:   str					     =					load_wavaveca_layer(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
		if not is_used:
			unused_weights.append(lowerCAmelCase		)

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





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					full_name.split('conv_layers.'		)[-1]
	__magic_name__	:   int					     =					name.split('.'		)
	__magic_name__	:   Any					     =					int(items[0]		)
	__magic_name__	:   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.'		)
			__magic_name__	:   Union[str, 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.'		)
			__magic_name__	:   str					     =					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.'		)
			__magic_name__	:   Optional[int]					     =					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.'		)
			__magic_name__	:   Dict					     =					value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
	else:
		unused_weights.append(lowerCAmelCase		)





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Dict=None		,		lowerCAmelCase    :     Any=None		,		lowerCAmelCase    :     Optional[Any]=True		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if config_path is not None:
		__magic_name__	:   int					     =					WavaVecaConfig.from_pretrained(lowerCAmelCase		)
	else:
		__magic_name__	:   List[str]					     =					WavaVecaConfig()

	if is_seq_class:
		__magic_name__	:   Any					     =					read_txt_into_dict(lowerCAmelCase		)
		__magic_name__	:   Optional[Any]					     =					idalabel
		__magic_name__	:   Union[str, Any]					     =					WavaVecaForSequenceClassification(lowerCAmelCase		)
		__magic_name__	:   Optional[int]					     =					WavaVecaFeatureExtractor(
		    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)
		feature_extractor.save_pretrained(lowerCAmelCase		)

	elif is_finetuned:
		if dict_path:
			__magic_name__	:   str					     =					Dictionary.load(lowerCAmelCase		)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__magic_name__	:   Dict					     =					target_dict.pad_index
			__magic_name__	:   Union[str, Any]					     =					target_dict.bos_index
			__magic_name__	:   Union[str, Any]					     =					target_dict.eos_index
			__magic_name__	:   Union[str, Any]					     =					len(target_dict.symbols		)
			__magic_name__	:   Dict					     =					os.path.join(lowerCAmelCase		,		'vocab.json'		)
			if not os.path.isdir(lowerCAmelCase		):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase		)		)
				return
			os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)
			__magic_name__	:   List[Any]					     =					target_dict.indices

			# fairseq has the <pad> and <s> switched
			__magic_name__	:   Any					     =					0
			__magic_name__	:   Optional[int]					     =					1
			with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as vocab_handle:
				json.dump(lowerCAmelCase		,		lowerCAmelCase		)
			__magic_name__	:   List[str]					     =					WavaVecaCTCTokenizer(
			    lowerCAmelCase		,		unk_token=target_dict.unk_word		,		pad_token=target_dict.pad_word		,		bos_token=target_dict.bos_word		,		eos_token=target_dict.eos_word		,		word_delimiter_token='|'		,		do_lower_case=lowerCAmelCase		,		)
			__magic_name__	:   Tuple					     =					True if config.feat_extract_norm == 'layer' else False
			__magic_name__	:   Optional[Any]					     =					WavaVecaFeatureExtractor(
			    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)
			__magic_name__	:   List[Any]					     =					WavaVecaProcessor(feature_extractor=lowerCAmelCase		,		tokenizer=lowerCAmelCase		)
			processor.save_pretrained(lowerCAmelCase		)

		__magic_name__	:   Dict					     =					WavaVecaForCTC(lowerCAmelCase		)
	else:
		__magic_name__	:   Tuple					     =					WavaVecaForPreTraining(lowerCAmelCase		)

	if is_finetuned or is_seq_class:
		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   str					     =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]		,		arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
	else:
		__magic_name__	:   Optional[Any]					     =					argparse.Namespace(task='audio_pretraining'		)
		__magic_name__	:   Dict					     =					fairseq.tasks.setup_task(lowerCAmelCase		)

		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Optional[Any]					     =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,		task=lowerCAmelCase		)

	__magic_name__	:   Any					     =					model[0].eval()

	recursively_load_weights(lowerCAmelCase		,		lowerCAmelCase		,		not is_finetuned		)

	hf_wavavec.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				:str        =      argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
							)
							parser.add_argument(
							    '''--is_seq_class''',
							    action='''store_true''',
							    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
							)
							lowerCAmelCase				:Dict        =      parser.parse_args()

							lowerCAmelCase				:Tuple        =      not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)