
'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase    :							Union[str, Any]      =					{
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	Dict:
	for attribute in key.split('.'	):
		_snake_case   						=		getattr(__A			,				__A	)

	if weight_type is not None:
		_snake_case   						=		getattr(__A			,				__A	).shape
	else:
		_snake_case   						=		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":
		_snake_case   						=		value
	elif weight_type == "weight_g":
		_snake_case   						=		value
	elif weight_type == "weight_v":
		_snake_case   						=		value
	elif weight_type == "bias":
		_snake_case   						=		value
	else:
		_snake_case   						=		value

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Any:
	_snake_case   						=		[]
	_snake_case   						=		fairseq_model.state_dict()

	_snake_case   						=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_snake_case   						=		False
		if "conv_layers" in name:
			load_conv_layer(
			    __A			,				__A			,				__A			,				__A			,				hf_model.config.feat_extract_norm == 'group'			,				)
			_snake_case   						=		True
		else:
			for key, mapped_key in MAPPING.items():
				_snake_case   						=		'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

				if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
					_snake_case   						=		True
					if "*" in mapped_key:
						_snake_case   						=		name.split(__A	)[0].split('.'	)[-2]
						_snake_case   						=		mapped_key.replace('*'			,				__A	)
					if "weight_g" in name:
						_snake_case   						=		'weight_g'
					elif "weight_v" in name:
						_snake_case   						=		'weight_v'
					elif "weight" in name:
						_snake_case   						=		'weight'
					elif "bias" in name:
						_snake_case   						=		'bias'
					else:
						_snake_case   						=		None
					set_recursively(__A			,				__A			,				__A			,				__A			,				__A	)
				continue
		if not is_used:
			unused_weights.append(__A	)

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	int:
	_snake_case   						=		full_name.split('conv_layers.'	)[-1]
	_snake_case   						=		name.split('.'	)
	_snake_case   						=		int(items[0]	)
	_snake_case   						=		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.'
			)
			_snake_case   						=		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.'
			)
			_snake_case   						=		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."
			)
			_snake_case   						=		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.'
			)
			_snake_case   						=		value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
	else:
		unused_weights.append(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		SEWConfig()
	if is_finetuned:
		_snake_case   						=		model.wav_encoder.wav_model.cfg
	else:
		_snake_case   						=		model.cfg

	_snake_case   						=		fs_config.conv_bias
	_snake_case   						=		eval(fs_config.conv_feature_layers	)
	_snake_case   						=		[x[0] for x in conv_layers]
	_snake_case   						=		[x[1] for x in conv_layers]
	_snake_case   						=		[x[2] for x in conv_layers]
	_snake_case   						=		'gelu'
	_snake_case   						=		'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_snake_case   						=		0.0
	_snake_case   						=		fs_config.activation_fn.name
	_snake_case   						=		fs_config.encoder_embed_dim
	_snake_case   						=		0.0_2
	_snake_case   						=		fs_config.encoder_ffn_embed_dim
	_snake_case   						=		1e-5
	_snake_case   						=		fs_config.encoder_layerdrop
	_snake_case   						=		fs_config.encoder_attention_heads
	_snake_case   						=		fs_config.conv_pos_groups
	_snake_case   						=		fs_config.conv_pos
	_snake_case   						=		len(__A	)
	_snake_case   						=		fs_config.encoder_layers
	_snake_case   						=		fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		_snake_case   						=		model.cfg
		_snake_case   						=		fs_config.final_dropout
		_snake_case   						=		fs_config.layerdrop
	_snake_case   						=		fs_config.activation_dropout
	_snake_case   						=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	_snake_case   						=		fs_config.attention_dropout
	_snake_case   						=		fs_config.dropout_input
	_snake_case   						=		fs_config.dropout
	_snake_case   						=		fs_config.mask_channel_length
	_snake_case   						=		fs_config.mask_channel_prob
	_snake_case   						=		fs_config.mask_length
	_snake_case   						=		fs_config.mask_prob

	_snake_case   						=		'Wav2Vec2FeatureExtractor'
	_snake_case   						=		'Wav2Vec2CTCTokenizer'

	return config







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=None			,				__A=None			,				__A=True	)       ->	List[str]:

	if is_finetuned:
		_snake_case						, _snake_case						, _snake_case   						=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]			,				arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
	else:
		_snake_case						, _snake_case						, _snake_case   						=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

	if config_path is not None:
		_snake_case   						=		SEWConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		convert_config(model[0]			,				__A	)
	_snake_case   						=		model[0].eval()

	_snake_case   						=		True if config.feat_extract_norm == 'layer' else False
	_snake_case   						=		WavaVecaFeatureExtractor(
	    feature_size=1			,				sampling_rate=16_000			,				padding_value=0			,				do_normalize=__A			,				return_attention_mask=__A			,				)

	if is_finetuned:
		if dict_path:
			_snake_case   						=		Dictionary.load(__A	)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_snake_case   						=		target_dict.pad_index
			_snake_case   						=		target_dict.bos_index
			_snake_case   						=		target_dict.pad_index
			_snake_case   						=		target_dict.bos_index
			_snake_case   						=		target_dict.eos_index
			_snake_case   						=		len(target_dict.symbols	)
			_snake_case   						=		os.path.join(__A			,				'vocab.json'	)
			if not os.path.isdir(__A	):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__A	)	)
				return
			os.makedirs(__A			,				exist_ok=__A	)
			with open(__A			,				'w'			,				encoding='utf-8'	) as vocab_handle:
				json.dump(target_dict.indices			,				__A	)
			_snake_case   						=		WavaVecaCTCTokenizer(
			    __A			,				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=__A			,				)
			_snake_case   						=		WavaVecaProcessor(feature_extractor=__A			,				tokenizer=__A	)
			processor.save_pretrained(__A	)

		_snake_case   						=		SEWForCTC(__A	)
	else:
		_snake_case   						=		SEWModel(__A	)
		feature_extractor.save_pretrained(__A	)

	recursively_load_weights(__A			,				__A			,				__A	)

	hf_model.save_pretrained(__A	)


if __name__ == "__main__":
					lowercase    :							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(
					    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					lowercase    :							Union[str, Any]      =					parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)








import socket






def lowerCamelCase__    (							)      ->				Any:
   __snake_case							       =   socket.socket(socket.AF_INET       ,      socket.SOCK_STREAM       )
   __snake_case							       =   socket.gethostname()
   __snake_case							       =   1_2312

   sock.connect((host, port)       )
   sock.send(B'''Hello server!'''       )

   with open('''Received_file'''       ,      '''wb'''       ) as out_file:
      print('''File opened'''       )
      print('''Receiving data...'''       )
      while True:
         __snake_case							       =   sock.recv(1024       )
         if not data:
            break
         out_file.write(snake_case_       )

   print('''Successfully received the file'''       )
   sock.close()
   print('''Connection closed'''       )


if __name__ == "__main__":
    main()

