


"""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()
SCREAMING_SNAKE_CASE :    Tuple									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    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""",
    """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""",
}
SCREAMING_SNAKE_CASE :    Dict									=				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]






def 		lowercase							(     _snake_case						:      Dict     )     ->int:
	"""simple docstring"""





	__snake_case	:						int						=				{}
	with open(_snake_case     ,   '''r'''     ) as file:
		for line_number, line in enumerate(_snake_case     ):
			__snake_case	:						Union[str, Any]						=				line.strip()
			if line:
				__snake_case	:						List[Any]						=				line.split()
				__snake_case	:						Optional[int]						=				line_number
				__snake_case	:						Tuple						=				words[0]
				__snake_case	:						Dict						=				value
	return result






def 		lowercase							(     _snake_case						:      List[str]     ,   _snake_case						:      str     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      List[Any]     )     ->List[Any]:
	"""simple docstring"""





	for attribute in key.split('''.'''     ):
		__snake_case	:						int						=				getattr(_snake_case     ,   _snake_case     )

	__snake_case	:						str						=				None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(_snake_case     ):
			__snake_case	:						Tuple						=				PARAM_MAPPING[full_name.split('''.'''     )[-1]]
			__snake_case	:						Union[str, Any]						=				'''param'''

	if weight_type is not None and weight_type != "param":
		__snake_case	:						Union[str, Any]						=				getattr(_snake_case     ,   _snake_case     ).shape
	elif weight_type is not None and weight_type == "param":
		__snake_case	:						Optional[int]						=				hf_pointer
		for attribute in hf_param_name.split('''.'''     ):
			__snake_case	:						List[str]						=				getattr(_snake_case     ,   _snake_case     )
		__snake_case	:						Optional[Any]						=				shape_pointer.shape

		# let's reduce dimension
		__snake_case	:						Tuple						=				value[0]
	else:
		__snake_case	:						Tuple						=				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":
		__snake_case	:						Any						=				value
	elif weight_type == "weight_g":
		__snake_case	:						List[str]						=				value
	elif weight_type == "weight_v":
		__snake_case	:						int						=				value
	elif weight_type == "bias":
		__snake_case	:						List[Any]						=				value
	elif weight_type == "param":
		for attribute in hf_param_name.split('''.'''     ):
			__snake_case	:						Optional[int]						=				getattr(_snake_case     ,   _snake_case     )
		__snake_case	:						List[Any]						=				value
	else:
		__snake_case	:						Optional[Any]						=				value

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






def 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      Union[str, Any]     ,   _snake_case						:      str     ,   _snake_case						:      Any     ,   _snake_case						:      Tuple     )     ->Optional[Any]:
	"""simple docstring"""





	__snake_case	:						Optional[int]						=				None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(_snake_case     ):
			__snake_case	:						List[str]						=				PARAM_MAPPING[full_name.split('''.'''     )[-1]]
			__snake_case	:						str						=				'''param'''

	if weight_type is not None and weight_type != "param":
		__snake_case	:						Union[str, Any]						=				'''.'''.join([key, weight_type]     )
	elif weight_type is not None and weight_type == "param":
		__snake_case	:						Dict						=				'''.'''.join([key, hf_param_name]     )
	else:
		__snake_case	:						Dict						=				key

	__snake_case	:						Optional[int]						=				value if '''lm_head''' in full_key else value[0]


SCREAMING_SNAKE_CASE :    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 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      List[str]     ,   _snake_case						:      int=None     ,   _snake_case						:      Optional[Any]=None     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						Dict						=				False
	for key, mapped_key in MAPPING.items():
		__snake_case	:						Tuple						=				'''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]:
			__snake_case	:						Optional[int]						=				True
			if "*" in mapped_key:
				__snake_case	:						str						=				name.split(_snake_case     )[0].split('''.'''     )[-2]
				__snake_case	:						Optional[Any]						=				mapped_key.replace('''*'''     ,   _snake_case     )
			if "weight_g" in name:
				__snake_case	:						List[str]						=				'''weight_g'''
			elif "weight_v" in name:
				__snake_case	:						List[Any]						=				'''weight_v'''
			elif "bias" in name:
				__snake_case	:						Tuple						=				'''bias'''
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				__snake_case	:						List[Any]						=				'''weight'''
			else:
				__snake_case	:						Dict						=				None
			if hf_dict is not None:
				rename_dict(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )
			else:
				set_recursively(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )
			return is_used
	return is_used






def 		lowercase							(     _snake_case						:      Dict     ,   _snake_case						:      Union[str, Any]     ,   _snake_case						:      List[str]     )     ->str:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				[]
	__snake_case	:						Union[str, Any]						=				fairseq_model.state_dict()

	__snake_case	:						Optional[int]						=				hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		__snake_case	:						int						=				False
		if "conv_layers" in name:
			load_conv_layer(
			    _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   hf_model.config.feat_extract_norm == '''group'''     ,   )
			__snake_case	:						Tuple						=				True
		else:
			__snake_case	:						int						=				load_wavaveca_layer(_snake_case     ,   _snake_case     ,   _snake_case     )
		if not is_used:
			unused_weights.append(_snake_case     )

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






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Any     ,   _snake_case						:      List[Any]     ,   _snake_case						:      List[Any]     )     ->str:
	"""simple docstring"""





	__snake_case	:						Optional[int]						=				full_name.split('''conv_layers.'''     )[-1]
	__snake_case	:						Dict						=				name.split('''.'''     )
	__snake_case	:						Optional[Any]						=				int(items[0]     )
	__snake_case	:						Dict						=				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."""     )
			__snake_case	:						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."""     )
			__snake_case	:						Any						=				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."""     )
			__snake_case	:						List[Any]						=				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."""     )
			__snake_case	:						Optional[int]						=				value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
	else:
		unused_weights.append(_snake_case     )






@torch.no_grad()
def 		lowercase							(     _snake_case						:      Union[str, Any]     ,   _snake_case						:      List[str]     ,   _snake_case						:      Optional[Any]=None     ,   _snake_case						:      Union[str, Any]=None     ,   _snake_case						:      int=True     ,   _snake_case						:      Optional[Any]=False     )     ->str:
	"""simple docstring"""





	if config_path is not None:
		__snake_case	:						Tuple						=				WavaVecaConfig.from_pretrained(_snake_case     )
	else:
		__snake_case	:						Optional[int]						=				WavaVecaConfig()

	if is_seq_class:
		__snake_case	:						Optional[int]						=				read_txt_into_dict(_snake_case     )
		__snake_case	:						Any						=				idalabel
		__snake_case	:						List[str]						=				WavaVecaForSequenceClassification(_snake_case     )
		__snake_case	:						Dict						=				WavaVecaFeatureExtractor(
		    feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=_snake_case     ,   return_attention_mask=_snake_case     ,   )
		feature_extractor.save_pretrained(_snake_case     )

	elif is_finetuned:
		if dict_path:
			__snake_case	:						Optional[int]						=				Dictionary.load(_snake_case     )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__snake_case	:						Union[str, Any]						=				target_dict.pad_index
			__snake_case	:						int						=				target_dict.bos_index
			__snake_case	:						Tuple						=				target_dict.eos_index
			__snake_case	:						List[str]						=				len(target_dict.symbols     )
			__snake_case	:						Dict						=				os.path.join(_snake_case     ,   '''vocab.json'''     )
			if not os.path.isdir(_snake_case     ):
				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case     )     )
				return
			os.makedirs(_snake_case     ,   exist_ok=_snake_case     )
			__snake_case	:						Optional[Any]						=				target_dict.indices

			# fairseq has the <pad> and <s> switched
			__snake_case	:						Dict						=				0
			__snake_case	:						str						=				1
			with open(_snake_case     ,   '''w'''     ,   encoding='''utf-8'''     ) as vocab_handle:
				json.dump(_snake_case     ,   _snake_case     )
			__snake_case	:						str						=				WavaVecaCTCTokenizer(
			    _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=_snake_case     ,   )
			__snake_case	:						Optional[Any]						=				True if config.feat_extract_norm == '''layer''' else False
			__snake_case	:						Dict						=				WavaVecaFeatureExtractor(
			    feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=_snake_case     ,   return_attention_mask=_snake_case     ,   )
			__snake_case	:						Optional[int]						=				WavaVecaProcessor(feature_extractor=_snake_case     ,   tokenizer=_snake_case     )
			processor.save_pretrained(_snake_case     )

		__snake_case	:						Any						=				WavaVecaForCTC(_snake_case     )
	else:
		__snake_case	:						Dict						=				WavaVecaForPreTraining(_snake_case     )

	if is_finetuned or is_seq_class:
		__snake_case	,							__snake_case	,							__snake_case	:						Optional[Any]						=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]     ,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
	else:
		__snake_case	:						Union[str, Any]						=				argparse.Namespace(task='''audio_pretraining'''     )
		__snake_case	:						int						=				fairseq.tasks.setup_task(_snake_case     )

		__snake_case	,							__snake_case	,							__snake_case	:						Optional[Any]						=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,   task=_snake_case     )

	__snake_case	:						Any						=				model[0].eval()

	recursively_load_weights(_snake_case     ,   _snake_case     ,   not is_finetuned     )

	hf_wavavec.save_pretrained(_snake_case     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    List[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""",
			)
			SCREAMING_SNAKE_CASE :    Tuple									=				parser.parse_args()

			SCREAMING_SNAKE_CASE :    str									=				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"""
def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      int     )     ->str:
	"""simple docstring"""





	if a < 0 or b < 0:
		raise ValueError('''the value of both inputs must be positive'''     )
	__snake_case	:						Tuple						=				str(bin(_snake_case     )     )[2:]  # remove the leading "0b"
	__snake_case	:						List[Any]						=				str(bin(_snake_case     )     )[2:]
	__snake_case	:						Any						=				max(len(_snake_case     )     ,   len(_snake_case     )     )
	return "0b" + "".join(
	    str(int('''1''' in (char_a, char_b)     )     )
	    for char_a, char_b in zip(a_binary.zfill(_snake_case     )     ,   b_binary.zfill(_snake_case     )     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





