


'''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,
							)



'''simple docstring'''

lowerCAmelCase				:Union[str, Any]        =      '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase				:Union[str, Any]        =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase				:Tuple        =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}