



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()
UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = {
    "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",
}
UpperCAmelCase__     = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 							A			(  _UpperCAmelCase       :     List[str]					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			with open(_UpperCAmelCase							,     'r'					) as file:
						for line_number, line in enumerate(_UpperCAmelCase					):
									_UpperCAmelCase	 =     line.strip()
									if line:
												_UpperCAmelCase	 =     line.split()
												_UpperCAmelCase	 =     line_number
												_UpperCAmelCase	 =     words[0]
												_UpperCAmelCase	 =     value
			return result





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Any					)       ->				str:







			'''simple docstring'''
			for attribute in key.split('.'					):
						_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     _UpperCAmelCase					)

			_UpperCAmelCase	 =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_UpperCAmelCase					):
									_UpperCAmelCase	 =     PARAM_MAPPING[full_name.split('.'					)[-1]]
									_UpperCAmelCase	 =     'param'

			if weight_type is not None and weight_type != "param":
						_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     _UpperCAmelCase					).shape
			elif weight_type is not None and weight_type == "param":
						_UpperCAmelCase	 =     hf_pointer
						for attribute in hf_param_name.split('.'					):
									_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     _UpperCAmelCase					)
						_UpperCAmelCase	 =     shape_pointer.shape

						# let's reduce dimension
						_UpperCAmelCase	 =     value[0]
			else:
						_UpperCAmelCase	 =     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":
						_UpperCAmelCase	 =     value
			elif weight_type == "weight_g":
						_UpperCAmelCase	 =     value
			elif weight_type == "weight_v":
						_UpperCAmelCase	 =     value
			elif weight_type == "bias":
						_UpperCAmelCase	 =     value
			elif weight_type == "param":
						for attribute in hf_param_name.split('.'					):
									_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     _UpperCAmelCase					)
						_UpperCAmelCase	 =     value
			else:
						_UpperCAmelCase	 =     value

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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_UpperCAmelCase					):
									_UpperCAmelCase	 =     PARAM_MAPPING[full_name.split('.'					)[-1]]
									_UpperCAmelCase	 =     'param'

			if weight_type is not None and weight_type != "param":
						_UpperCAmelCase	 =     '.'.join([key, weight_type]					)
			elif weight_type is not None and weight_type == "param":
						_UpperCAmelCase	 =     '.'.join([key, hf_param_name]					)
			else:
						_UpperCAmelCase	 =     key

			_UpperCAmelCase	 =     value if 'lm_head' in full_key else value[0]


UpperCAmelCase__     = {
    "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 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Optional[int]=None							,     _UpperCAmelCase       :     Union[str, Any]=None					)       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     False
			for key, mapped_key in MAPPING.items():
						_UpperCAmelCase	 =     '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]:
									_UpperCAmelCase	 =     True
									if "*" in mapped_key:
												_UpperCAmelCase	 =     name.split(_UpperCAmelCase					)[0].split('.'					)[-2]
												_UpperCAmelCase	 =     mapped_key.replace('*'							,     _UpperCAmelCase					)
									if "weight_g" in name:
												_UpperCAmelCase	 =     'weight_g'
									elif "weight_v" in name:
												_UpperCAmelCase	 =     'weight_v'
									elif "bias" in name:
												_UpperCAmelCase	 =     'bias'
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_UpperCAmelCase	 =     'weight'
									else:
												_UpperCAmelCase	 =     None
									if hf_dict is not None:
												rename_dict(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)
									else:
												set_recursively(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)
									return is_used
			return is_used





def 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Any					)       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     []
			_UpperCAmelCase	 =     fairseq_model.state_dict()

			_UpperCAmelCase	 =     hf_model.wavaveca.feature_extractor

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

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





def 							A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[Any]					)       ->				str:







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





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     List[Any]=None							,     _UpperCAmelCase       :     Any=None							,     _UpperCAmelCase       :     List[Any]=True							,     _UpperCAmelCase       :     Union[str, Any]=False					)       ->				List[str]:







			'''simple docstring'''
			if config_path is not None:
						_UpperCAmelCase	 =     WavaVecaConfig.from_pretrained(_UpperCAmelCase					)
			else:
						_UpperCAmelCase	 =     WavaVecaConfig()

			if is_seq_class:
						_UpperCAmelCase	 =     read_txt_into_dict(_UpperCAmelCase					)
						_UpperCAmelCase	 =     idalabel
						_UpperCAmelCase	 =     WavaVecaForSequenceClassification(_UpperCAmelCase					)
						_UpperCAmelCase	 =     WavaVecaFeatureExtractor(
						    feature_size=1							,     sampling_rate=16_000							,     padding_value=0							,     do_normalize=_UpperCAmelCase							,     return_attention_mask=_UpperCAmelCase							,     )
						feature_extractor.save_pretrained(_UpperCAmelCase					)

			elif is_finetuned:
						if dict_path:
									_UpperCAmelCase	 =     Dictionary.load(_UpperCAmelCase					)

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

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

						_UpperCAmelCase	 =     WavaVecaForCTC(_UpperCAmelCase					)
			else:
						_UpperCAmelCase	 =     WavaVecaForPreTraining(_UpperCAmelCase					)

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

						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,     task=_UpperCAmelCase					)

			_UpperCAmelCase	 =     model[0].eval()

			recursively_load_weights(_UpperCAmelCase							,     _UpperCAmelCase							,     not is_finetuned					)

			hf_wavavec.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = 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",
							)
							UpperCAmelCase__     = parser.parse_args()

							UpperCAmelCase__     = 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,
							)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				int:







			'''simple docstring'''
			while second != 0:
						_UpperCAmelCase	 =     first & second
						first ^= second
						_UpperCAmelCase	 =     c << 1
			return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase__     = int(input("Enter the first number: ").strip())
							UpperCAmelCase__     = int(input("Enter the second number: ").strip())
							print(f"""{add(first, second) = }""")
