


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

A	:		Dict    =       {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    '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',
}
A	:		List[Any]    =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Tuple   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Tuple					)       ->  int:
		"""simple docstring"""




		for attribute in key.split("""."""					):
				lowercase__					      =		getattr(__magic_name__   ,   __magic_name__					)

		if weight_type is not None:
				lowercase__					      =		getattr(__magic_name__   ,   __magic_name__					).shape
		else:
				lowercase__					      =		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":
				lowercase__					      =		value
		elif weight_type == "weight_g":
				lowercase__					      =		value
		elif weight_type == "weight_v":
				lowercase__					      =		value
		elif weight_type == "bias":
				lowercase__					      =		value
		elif weight_type == "running_mean":
				lowercase__					      =		value
		elif weight_type == "running_var":
				lowercase__					      =		value
		elif weight_type == "num_batches_tracked":
				lowercase__					      =		value
		elif weight_type == "inv_freq":
				lowercase__					      =		value
		else:
				lowercase__					      =		value

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






def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							str   ,   __magic_name__					:							List[Any]					)       ->  Optional[int]:
		"""simple docstring"""




		lowercase__					      =		[]
		lowercase__					      =		fairseq_model.state_dict()

		lowercase__					      =		hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase__					      =		False
				if "conv_layers" in name:
						load_conv_layer(
						    __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   hf_model.config.feat_extract_norm == """group"""   ,   )
						lowercase__					      =		True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase__					      =		"""wav2vec2_conformer.""" + 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]:
										lowercase__					      =		True
										if "*" in mapped_key:
												lowercase__					      =		name.split(__magic_name__					)[0].split("""."""					)[-2]
												lowercase__					      =		mapped_key.replace("""*"""   ,   __magic_name__					)
										if "pos_bias_u" in name:
												lowercase__					      =		None
										elif "pos_bias_v" in name:
												lowercase__					      =		None
										elif "weight_g" in name:
												lowercase__					      =		"""weight_g"""
										elif "weight_v" in name:
												lowercase__					      =		"""weight_v"""
										elif "bias" in name:
												lowercase__					      =		"""bias"""
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowercase__					      =		"""weight"""
										elif "running_mean" in name:
												lowercase__					      =		"""running_mean"""
										elif "inv_freq" in name:
												lowercase__					      =		"""inv_freq"""
										elif "running_var" in name:
												lowercase__					      =		"""running_var"""
										elif "num_batches_tracked" in name:
												lowercase__					      =		"""num_batches_tracked"""
										else:
												lowercase__					      =		None
										set_recursively(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					)
								continue
				if not is_used:
						unused_weights.append(__magic_name__					)

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






def 			UpperCamelCase (     __magic_name__					:							Any   ,   __magic_name__					:							Tuple   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Optional[int]					)       ->  Optional[Any]:
		"""simple docstring"""




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






@torch.no_grad()
def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							Tuple   ,   __magic_name__					:							List[str]=None   ,   __magic_name__					:							Tuple=None   ,   __magic_name__					:							List[str]=True					)       ->  List[str]:
		"""simple docstring"""




		if config_path is not None:
				lowercase__					      =		WavaVecaConformerConfig.from_pretrained(__magic_name__   ,   hidden_act="""swish"""					)
		else:
				lowercase__					      =		WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				lowercase__					      =		"""rotary"""

		if is_finetuned:
				if dict_path:
						lowercase__					      =		Dictionary.load(__magic_name__					)

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

						# fairseq has the <pad> and <s> switched
						lowercase__					      =		0
						lowercase__					      =		1
						with open(__magic_name__   ,   """w"""   ,   encoding="""utf-8"""					) as vocab_handle:
								json.dump(__magic_name__   ,   __magic_name__					)
						lowercase__					      =		WavaVecaCTCTokenizer(
						    __magic_name__   ,   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=__magic_name__   ,   )
						lowercase__					      =		True if config.feat_extract_norm == """layer""" else False
						lowercase__					      =		WavaVecaFeatureExtractor(
						    feature_size=1   ,   sampling_rate=1_6000   ,   padding_value=0   ,   do_normalize=__magic_name__   ,   return_attention_mask=__magic_name__   ,   )
						lowercase__					      =		WavaVecaProcessor(feature_extractor=__magic_name__   ,   tokenizer=__magic_name__					)
						processor.save_pretrained(__magic_name__					)

				lowercase__					      =		WavaVecaConformerForCTC(__magic_name__					)
		else:
				lowercase__					      =		WavaVecaConformerForPreTraining(__magic_name__					)

		if is_finetuned:
				lowercase__      ,     lowercase__      ,     lowercase__					      =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]   ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					)}					)
		else:
				lowercase__					      =		argparse.Namespace(task="""audio_pretraining"""					)
				lowercase__					      =		fairseq.tasks.setup_task(__magic_name__					)

				lowercase__      ,     lowercase__      ,     lowercase__					      =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,   task=__magic_name__					)

		lowercase__					      =		model[0].eval()

		recursively_load_weights(__magic_name__   ,   __magic_name__   ,   not is_finetuned					)

		hf_wavavec.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	A	:		int    =       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'
	)
	A	:		Dict    =       parser.parse_args()
	convert_wavaveca_conformer_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)





class 							A   :
					'''simple docstring'''






					def __init__(self   :						int				, _UpperCAmelCase   :						list		)						->       None:




							"""simple docstring"""


							lowercase__					      =		set_counts
							lowercase__					      =		max(_UpperCAmelCase		)
							lowercase__					      =		len(_UpperCAmelCase		)
							lowercase__					      =		[1] * num_sets
							lowercase__					      =		list(range(_UpperCAmelCase		)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						int		)						->       bool:




							"""simple docstring"""


							lowercase__					      =		self.get_parent(_UpperCAmelCase		)
							lowercase__					      =		self.get_parent(_UpperCAmelCase		)

							if src_parent == dst_parent:
									return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
									self.set_counts[dst_parent] += self.set_counts[src_parent]
									lowercase__					      =		0
									lowercase__					      =		dst_parent
									if self.ranks[dst_parent] == self.ranks[src_parent]:
											self.ranks[dst_parent] += 1
									lowercase__					      =		self.set_counts[dst_parent]
							else:
									self.set_counts[src_parent] += self.set_counts[dst_parent]
									lowercase__					      =		0
									lowercase__					      =		src_parent
									lowercase__					      =		self.set_counts[src_parent]

							lowercase__					      =		max(self.max_set				, _UpperCAmelCase		)
							return True


					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int		)						->       int:




							"""simple docstring"""


							if self.parents[disj_set] == disj_set:
									return disj_set
							lowercase__					      =		self.get_parent(self.parents[disj_set]		)
							return self.parents[disj_set]

