





"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a						: Optional[Any]     	=			logging.get_logger(__name__)

a						: 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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    List[str]    ,       _lowercase						:    List[str]    ,       _lowercase						:    Dict					) ->Optional[Any]:






	'''simple docstring'''






	for attribute in key.split("."					):
		a      :		Dict	=				getattr(_lowercase    ,       _lowercase					)

	if weight_type is not None:
		a      :		Optional[Any]	=				getattr(_lowercase    ,       _lowercase					).shape
	else:
		a      :		Tuple	=				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":
		a      :		List[str]	=				value
	elif weight_type == "weight_g":
		a      :		int	=				value
	elif weight_type == "weight_v":
		a      :		int	=				value
	elif weight_type == "bias":
		a      :		Tuple	=				value
	else:
		a      :		Union[str, Any]	=				value

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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any    ,       _lowercase						:    List[str]    ,       _lowercase						:    int					) ->Any:






	'''simple docstring'''






	a      :		Dict	=				[]
	a      :		int	=				fairseq_model.state_dict()

	a      :		Tuple	=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		a      :		int	=				False
		if "conv_layers" in name:
			load_conv_layer(
			    _lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase    ,       hf_model.config.feat_extract_norm == "group"    ,       )
			a      :		Optional[Any]	=				True
		else:
			for key, mapped_key in MAPPING.items():
				a      :		Optional[Any]	=				"hubert." + 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] and not is_finetuned):
					a      :		List[str]	=				True
					if "*" in mapped_key:
						a      :		Union[str, Any]	=				name.split(_lowercase					)[0].split("."					)[-2]
						a      :		str	=				mapped_key.replace("*"    ,       _lowercase					)
					if "weight_g" in name:
						a      :		Optional[int]	=				"weight_g"
					elif "weight_v" in name:
						a      :		Optional[Any]	=				"weight_v"
					elif "weight" in name:
						a      :		Tuple	=				"weight"
					elif "bias" in name:
						a      :		Tuple	=				"bias"
					else:
						a      :		Union[str, Any]	=				None
					set_recursively(_lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase					)
				continue
		if not is_used:
			unused_weights.append(_lowercase					)

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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]    ,       _lowercase						:    Optional[Any]    ,       _lowercase						:    Optional[Any]    ,       _lowercase						:    str    ,       _lowercase						:    Optional[Any]					) ->List[Any]:






	'''simple docstring'''






	a      :		List[Any]	=				full_name.split("conv_layers."					)[-1]
	a      :		Any	=				name.split("."					)
	a      :		List[str]	=				int(items[0]					)
	a      :		Any	=				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."""
			)
			a      :		Tuple	=				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."""
			)
			a      :		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:
			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."
			)
			a      :		str	=				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."""
			)
			a      :		str	=				value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(_lowercase					)






@torch.no_grad()
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any    ,       _lowercase						:    Tuple    ,       _lowercase						:    List[str]=None    ,       _lowercase						:    Dict=None    ,       _lowercase						:    int=True					) ->List[Any]:






	'''simple docstring'''






	if config_path is not None:
		a      :		Tuple	=				HubertConfig.from_pretrained(_lowercase					)
	else:
		a      :		Any	=				HubertConfig()

	if is_finetuned:
		if dict_path:
			a      :		str	=				Dictionary.load(_lowercase					)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			a      :		int	=				target_dict.pad_index
			a      :		Optional[int]	=				target_dict.bos_index
			a      :		Dict	=				target_dict.eos_index
			a      :		Optional[int]	=				len(target_dict.symbols					)
			a      :		List[str]	=				os.path.join(_lowercase    ,       "vocab.json"					)
			if not os.path.isdir(_lowercase					):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowercase					)					)
				return
			os.makedirs(_lowercase    ,       exist_ok=_lowercase					)
			with open(_lowercase    ,       "w"    ,       encoding="utf-8"					) as vocab_handle:
				json.dump(target_dict.indices    ,       _lowercase					)
			a      :		Optional[int]	=				WavaVecaCTCTokenizer(
			    _lowercase    ,       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=_lowercase    ,       )
			a      :		int	=				True if config.feat_extract_norm == "layer" else False
			a      :		Any	=				WavaVecaFeatureExtractor(
			    feature_size=1    ,       sampling_rate=1_6000    ,       padding_value=0    ,       do_normalize=_lowercase    ,       return_attention_mask=_lowercase    ,       )
			a      :		str	=				WavaVecaProcessor(feature_extractor=_lowercase    ,       tokenizer=_lowercase					)
			processor.save_pretrained(_lowercase					)

		a      :		int	=				HubertForCTC(_lowercase					)
	else:
		a      :		str	=				HubertModel(_lowercase					)

	if is_finetuned:
		a,       a,       a      :		List[str]	=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]    ,       arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
	else:
		a,       a,       a      :		int	=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

	a      :		Optional[int]	=				model[0].eval()

	recursively_load_weights(_lowercase    ,       _lowercase    ,       _lowercase					)

	hf_wavavec.save_pretrained(_lowercase					)


if __name__ == "__main__":
		a						: Tuple     	=			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						: Optional[Any]     	=			parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)









"""simple docstring"""




from unittest.mock import Mock, patch

from file_transfer.send_file import send_file






@patch("socket.socket"					)
@patch("builtins.open"					)
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	a      :		Optional[Any]	=				Mock()
	a      :		Dict	=				conn, Mock()
	a      :		Union[str, Any]	=				iter([1, None]					)
	a      :		Optional[int]	=				lambda _lowercase					: next(_lowercase					)

	# ===== invoke =====
	send_file(filename="mytext.txt"    ,       testing=_lowercase					)

	# ===== ensurance =====
	sock.assert_called_once()
	sock.return_value.bind.assert_called_once()
	sock.return_value.listen.assert_called_once()
	sock.return_value.accept.assert_called_once()
	conn.recv.assert_called_once()

	file.return_value.__enter__.assert_called_once()
	file.return_value.__enter__.return_value.read.assert_called()

	conn.send.assert_called_once()
	conn.close.assert_called_once()
	sock.return_value.shutdown.assert_called_once()
	sock.return_value.close.assert_called_once()


