


'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowerCAmelCase				:Optional[int]        =      {
    '''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''',
    '''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''',
}
lowerCAmelCase				:List[Any]        =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





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





	"""simple docstring"""







	for attribute in key.split('.'		):
		__magic_name__	:   Tuple					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)

	if weight_type is not None:
		__magic_name__	:   Tuple					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		).shape
	else:
		__magic_name__	:   Optional[Any]					     =					hf_pointer.shape

	assert hf_shape == value.shape, (
	    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__	:   List[str]					     =					value
	elif weight_type == "weight_g":
		__magic_name__	:   Optional[int]					     =					value
	elif weight_type == "weight_v":
		__magic_name__	:   int					     =					value
	elif weight_type == "bias":
		__magic_name__	:   str					     =					value
	else:
		__magic_name__	:   List[str]					     =					value

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





def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					[]
	__magic_name__	:   str					     =					fairseq_model.state_dict()

	__magic_name__	:   Optional[Any]					     =					hf_model.feature_extractor

	# if encoder has different dim to decoder -> use proj_weight
	__magic_name__	:   Dict					     =					None

	for name, value in fairseq_dict.items():
		__magic_name__	:   Optional[int]					     =					False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		hf_model.config.feat_extract_norm == 'group'		,		)
			__magic_name__	:   List[str]					     =					True
		elif name.split('.'		)[0] == "proj":
			__magic_name__	:   List[str]					     =					fairseq_model.proj
			__magic_name__	:   Any					     =					True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
					__magic_name__	:   List[Any]					     =					True
					if "*" in mapped_key:
						__magic_name__	:   Optional[int]					     =					name.split(lowerCAmelCase		)[0].split('.'		)[-2]
						__magic_name__	:   str					     =					mapped_key.replace('*'		,		lowerCAmelCase		)
					if "weight_g" in name:
						__magic_name__	:   Any					     =					'weight_g'
					elif "weight_v" in name:
						__magic_name__	:   List[str]					     =					'weight_v'
					elif "bias" in name:
						__magic_name__	:   Any					     =					'bias'
					elif "weight" in name:
						__magic_name__	:   Union[str, Any]					     =					'weight'
					else:
						__magic_name__	:   Tuple					     =					None
					set_recursively(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
				continue
		if not is_used:
			unused_weights.append(lowerCAmelCase		)

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

	return proj_weight





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





	"""simple docstring"""







	__magic_name__	:   int					     =					full_name.split('conv_layers.'		)[-1]
	__magic_name__	:   Optional[Any]					     =					name.split('.'		)
	__magic_name__	:   Any					     =					int(items[0]		)
	__magic_name__	:   List[str]					     =					int(items[1]		)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    f'{full_name} has size {value.shape}, but'
			    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
			)
			__magic_name__	:   Dict					     =					value
			logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    f'{full_name} has size {value.shape}, but'
			    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
			)
			__magic_name__	:   int					     =					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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
			    " found."
			)
			__magic_name__	:   int					     =					value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    f'{full_name} has size {value.shape}, but'
			    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
			)
			__magic_name__	:   int					     =					value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
	else:
		unused_weights.append(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__     ,    __magic_name__	:   List[Any]					     =					emb.weight.shape
	__magic_name__	:   int					     =					nn.Linear(lowerCAmelCase		,		lowerCAmelCase		,		bias=lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					emb.weight.data
	return lin_layer





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	with open(lowerCAmelCase		,		'r'		,		encoding='utf-8'		) as f:
		__magic_name__	:   Tuple					     =					f.readlines()
		__magic_name__	:   Dict					     =					[line.split(' '		)[0] for line in lines]

	__magic_name__	:   str					     =					len(lowerCAmelCase		)

	__magic_name__	:   List[str]					     =					{
	    '<s>': 0,
	    '<pad>': 1,
	    '</s>': 2,
	    '<unk>': 3,
	}

	vocab_dict.update(dict(zip(lowerCAmelCase		,		range(4		,		num_words + 4		)		)		)		)
	return vocab_dict





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     str		,		):





	"""simple docstring"""







	__magic_name__	:   int					     =					WavaVecaConfig.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					SpeechaTextaConfig.from_pretrained(
	    lowerCAmelCase		,		vocab_size=lowerCAmelCase		,		decoder_layers=lowerCAmelCase		,		do_stable_layer_norm=lowerCAmelCase		)

	__magic_name__	:   Any					     =					WavaVecaFeatureExtractor(
	    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)

	__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Optional[int]					     =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]		,		arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
	__magic_name__	:   Optional[Any]					     =					model[0].eval()

	# set weights for wav2vec2 encoder
	__magic_name__	:   Optional[int]					     =					WavaVecaModel(lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					recursively_load_weights_wavaveca(model.encoder		,		lowerCAmelCase		)

	__magic_name__	:   Union[str, Any]					     =					SpeechaTextaForCausalLM(lowerCAmelCase		)
	__magic_name__     ,    __magic_name__	:   Dict					     =					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,		strict=lowerCAmelCase		)

	# set output linear layer
	unexpected_keys.remove('embed_out'		)
	__magic_name__	:   Optional[Any]					     =					nn.Parameter(model.decoder.embed_out.detach()		)

	# layer norm is init to identity matrix so leaving it is fine
	logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'		)
	logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'		)

	__magic_name__	:   str					     =					SpeechEncoderDecoderModel(encoder=lowerCAmelCase		,		decoder=lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					False

	# add projection layer
	__magic_name__	:   int					     =					nn.Parameter(projection_layer.weight		)
	__magic_name__	:   List[Any]					     =					nn.Parameter(projection_layer.bias		)

	__magic_name__	:   str					     =					create_vocab_dict(lowerCAmelCase		)

	with open(os.path.join(lowerCAmelCase		,		'vocab.json'		)		,		'w'		) as fp:
		json.dump(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Union[str, Any]					     =					SpeechaTextaTokenizer(os.path.join(lowerCAmelCase		,		'vocab.json'		)		)
	tokenizer.save_pretrained(lowerCAmelCase		)

	__magic_name__	:   Union[str, Any]					     =					hf_wavavec.config.to_dict()
	__magic_name__	:   Tuple					     =					tokenizer.pad_token_id
	__magic_name__	:   Dict					     =					tokenizer.bos_token_id
	__magic_name__	:   Tuple					     =					tokenizer.eos_token_id
	__magic_name__	:   str					     =					'speech_to_text_2'
	__magic_name__	:   List[str]					     =					'wav2vec2'

	__magic_name__	:   Optional[int]					     =					SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase		)

	hf_wavavec.save_pretrained(lowerCAmelCase		)
	feature_extractor.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				: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(
							    '''--encoder_config_path''',
							    default='''facebook/wav2vec2-large-lv60''',
							    type=str,
							    help='''Path to hf encoder wav2vec2 checkpoint config''',
							)
							parser.add_argument(
							    '''--decoder_config_path''',
							    default='''facebook/s2t-small-mustc-en-fr-st''',
							    type=str,
							    help='''Path to hf decoder s2t checkpoint config''',
							)
							parser.add_argument('''--vocab_size''', default=1_0_2_2_4, type=int, help='''Vocab size of decoder''')
							parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

							lowerCAmelCase				:Dict        =      parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    vocab_size=args.vocab_size,
							    num_decoder_layers=args.num_decoder_layers,
							)



'''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''',
}