
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()
UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					{
    '''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''',
}
UpperCAmelCase       =					[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	for attribute in key.split('.'				):
		lowercase	    =       getattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

	if weight_type is not None:
		lowercase	    =       getattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				).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_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	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(
			    __SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				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(__SCREAMING_SNAKE_CASE				)[0].split('.'				)[-2]
						lowercase	    =       mapped_key.replace('*'					,				__SCREAMING_SNAKE_CASE				)
					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(__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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	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(__SCREAMING_SNAKE_CASE				)






@torch.no_grad()
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=True				):
	if config_path is not None:
		lowercase	    =       WavaVecaConformerConfig.from_pretrained(__SCREAMING_SNAKE_CASE					,				hidden_act='swish'				)
	else:
		lowercase	    =       WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		lowercase	    =       'rotary'

	if is_finetuned:
		if dict_path:
			lowercase	    =       Dictionary.load(__SCREAMING_SNAKE_CASE				)

			# 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(__SCREAMING_SNAKE_CASE					,				'vocab.json'				)
			if not os.path.isdir(__SCREAMING_SNAKE_CASE				):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE				)				)
				return
			os.makedirs(__SCREAMING_SNAKE_CASE					,				exist_ok=__SCREAMING_SNAKE_CASE				)
			lowercase	    =       target_dict.indices

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

		lowercase	    =       WavaVecaConformerForCTC(__SCREAMING_SNAKE_CASE				)
	else:
		lowercase	    =       WavaVecaConformerForPreTraining(__SCREAMING_SNAKE_CASE				)

	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(__SCREAMING_SNAKE_CASE				)

		lowercase    ,   lowercase    ,   lowercase	    =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,				task=__SCREAMING_SNAKE_CASE				)

	lowercase	    =       model[0].eval()

	recursively_load_weights(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				not is_finetuned				)

	hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
	UpperCAmelCase       =					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'''
	)
	UpperCAmelCase       =					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
	)







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			bool:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for ch in input_str:
								__lowerCamelCase									=       ord(UpperCamelCase__ )
								__lowerCamelCase									=       pow(2					,	UpperCamelCase__ )

								# If we already turned on bit for current character's unicode
								if bitmap >> ch_unicode & 1 == 1:
												return False
								bitmap |= ch_bit_index_on
				return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()


