






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			:	Tuple								=logging.get_logger(__name__)

_UpperCamelCase			:	Any								={
    '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			:	Union[str, Any]								=[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def  a__ (__lowercase							:Optional[int]   )		-> Tuple:
				_A	:				int   =      {}
				with open(__lowercase					,  '''r'''   ) as file:
								for line_number, line in enumerate(__lowercase   ):
												_A	:				Union[str, Any]   =      line.strip()
												if line:
																_A	:				Optional[int]   =      line.split()
																_A	:				Union[str, Any]   =      line_number
																_A	:				Optional[Any]   =      words[0]
																_A	:				Tuple   =      value
				return result



def  a__ (__lowercase							:Any					,  __lowercase							:int					,  __lowercase							:Optional[Any]					,  __lowercase							:Union[str, Any]					,  __lowercase							:Tuple   )		-> List[str]:
				for attribute in key.split('''.'''   ):
								_A	:				Dict   =      getattr(__lowercase					,  __lowercase   )

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

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

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

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



def  a__ (__lowercase							:Optional[int]					,  __lowercase							:List[Any]					,  __lowercase							:Union[str, Any]					,  __lowercase							:Optional[int]					,  __lowercase							:Optional[Any]   )		-> Optional[int]:
				_A	:				Optional[int]   =      None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(__lowercase   ):
												_A	:				str   =      PARAM_MAPPING[full_name.split('''.'''   )[-1]]
												_A	:				Optional[Any]   =      '''param'''

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

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


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



def  a__ (__lowercase							:Dict					,  __lowercase							:int					,  __lowercase							:int   )		-> str:
				_A	:				Optional[int]   =      []
				_A	:				Tuple   =      fairseq_model.state_dict()

				_A	:				Dict   =      hf_model.wavaveca.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	:				Tuple   =      True
								else:
												_A	:				Optional[Any]   =      load_wavaveca_layer(__lowercase					,  __lowercase					,  __lowercase   )
								if not is_used:
												unused_weights.append(__lowercase   )

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



def  a__ (__lowercase							:Dict					,  __lowercase							:Any					,  __lowercase							:str					,  __lowercase							:Union[str, Any]					,  __lowercase							:List[Any]   )		-> Tuple:
				_A	:				str   =      full_name.split('''conv_layers.'''   )[-1]
				_A	:				Optional[Any]   =      name.split('''.'''   )
				_A	:				Tuple   =      int(items[0]   )
				_A	:				Union[str, Any]   =      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."""   )
												_A	:				List[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."""   )
												_A	:				Dict   =      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."""   )
												_A	:				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."""   )
												_A	:				List[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  a__ (__lowercase							:Dict					,  __lowercase							:List[str]					,  __lowercase							:Optional[Any]=None					,  __lowercase							:List[Any]=None					,  __lowercase							:Optional[Any]=True					,  __lowercase							:List[Any]=False   )		-> Union[str, Any]:
				if config_path is not None:
								_A	:				List[Any]   =      WavaVecaConfig.from_pretrained(__lowercase   )
				else:
								_A	:				Optional[int]   =      WavaVecaConfig()

				if is_seq_class:
								_A	:				List[Any]   =      read_txt_into_dict(__lowercase   )
								_A	:				List[str]   =      idalabel
								_A	:				List[str]   =      WavaVecaForSequenceClassification(__lowercase   )
								_A	:				List[str]   =      WavaVecaFeatureExtractor(
								    feature_size=1					,  sampling_rate=1_6000					,  padding_value=0					,  do_normalize=__lowercase					,  return_attention_mask=__lowercase					,  )
								feature_extractor.save_pretrained(__lowercase   )

				elif is_finetuned:
								if dict_path:
												_A	:				Any   =      Dictionary.load(__lowercase   )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_A	:				str   =      target_dict.pad_index
												_A	:				Optional[Any]   =      target_dict.bos_index
												_A	:				str   =      target_dict.eos_index
												_A	:				Optional[Any]   =      len(target_dict.symbols   )
												_A	:				Tuple   =      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   )
												_A	:				int   =      target_dict.indices

												# fairseq has the <pad> and <s> switched
												_A	:				List[str]   =      0
												_A	:				List[str]   =      1
												with open(__lowercase					,  '''w'''					,  encoding='''utf-8'''   ) as vocab_handle:
																json.dump(__lowercase					,  __lowercase   )
												_A	:				List[str]   =      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	:				Optional[Any]   =      True if config.feat_extract_norm == '''layer''' else False
												_A	:				Union[str, Any]   =      WavaVecaFeatureExtractor(
												    feature_size=1					,  sampling_rate=1_6000					,  padding_value=0					,  do_normalize=__lowercase					,  return_attention_mask=__lowercase					,  )
												_A	:				Optional[int]   =      WavaVecaProcessor(feature_extractor=__lowercase					,  tokenizer=__lowercase   )
												processor.save_pretrained(__lowercase   )

								_A	:				Union[str, Any]   =      WavaVecaForCTC(__lowercase   )
				else:
								_A	:				List[str]   =      WavaVecaForPreTraining(__lowercase   )

				if is_finetuned or is_seq_class:
								_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	:				Dict   =      argparse.Namespace(task='''audio_pretraining'''   )
								_A	:				int   =      fairseq.tasks.setup_task(__lowercase   )

								_A  ,				_A  ,				_A	:				List[str]   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,  task=__lowercase   )

				_A	:				int   =      model[0].eval()

				recursively_load_weights(__lowercase					,  __lowercase					,  not is_finetuned   )

				hf_wavavec.save_pretrained(__lowercase   )


if __name__ == "__main__":
	_UpperCamelCase			:	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'
	)
	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			:	Dict								=parser.parse_args()

	_UpperCamelCase			:	Optional[Any]								=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__ (__lowercase							:int					,  __lowercase							:int   )		-> int:
				return abs(__lowercase   ) if a == 0 else greatest_common_divisor(b % a					,  __lowercase   )



def  a__ (__lowercase							:int					,  __lowercase							:int   )		-> int:
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								_A  ,				_A	:				Union[str, Any]   =      y, x % y
				return abs(__lowercase   )



def  a__ ()		-> Optional[Any]:
				try:
								_A	:				Optional[Any]   =      input('''Enter two integers separated by comma (,): '''   ).split(''','''   )
								_A	:				List[Any]   =      int(nums[0]   )
								_A	:				Optional[int]   =      int(nums[1]   )
								print(
								    f"""greatest_common_divisor({num_a}, {num_a}) = """
								    f"""{greatest_common_divisor(__lowercase					,  __lowercase   )}"""   )
								print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowercase					,  __lowercase   )}"""   )
				except (IndexError, UnboundLocalError, ValueError):
								print('''Wrong input'''   )


if __name__ == "__main__":
	main()



