





'''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()
snake_case_ :	Tuple             =   logging.get_logger(__name__)

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



def 			lowercase__(		_UpperCamelCase	:			Optional[int]					)-> Tuple:






					"""simple docstring"""

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



def 			lowercase__(		_UpperCamelCase	:			Any			, _UpperCamelCase	:			int			, _UpperCamelCase	:			Optional[Any]			, _UpperCamelCase	:			Union[str, Any]			, _UpperCamelCase	:			Tuple					)-> List[str]:






					"""simple docstring"""

					for attribute in key.split("."					):
										_UpperCamelCase									= getattr(__lowercase			, __lowercase					)

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

					if weight_type is not None and weight_type != "param":
										_UpperCamelCase									= getattr(__lowercase			, __lowercase					).shape
					elif weight_type is not None and weight_type == "param":
										_UpperCamelCase									= hf_pointer
										for attribute in hf_param_name.split("."					):
															_UpperCamelCase									= getattr(__lowercase			, __lowercase					)
										_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(__lowercase			, __lowercase					)
										_UpperCamelCase									= value
					else:
										_UpperCamelCase									= value

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



def 			lowercase__(		_UpperCamelCase	:			Optional[int]			, _UpperCamelCase	:			List[Any]			, _UpperCamelCase	:			Union[str, Any]			, _UpperCamelCase	:			Optional[int]			, _UpperCamelCase	:			Optional[Any]					)-> Optional[int]:






					"""simple docstring"""

					_UpperCamelCase									= None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(__lowercase					):
															_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]


snake_case_ :	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 			lowercase__(		_UpperCamelCase	:			int			, _UpperCamelCase	:			Optional[int]			, _UpperCamelCase	:			List[Any]=None			, _UpperCamelCase	:			Tuple=None					)-> List[Any]:






					"""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(__lowercase					)[0].split("."					)[-2]
																				_UpperCamelCase									= mapped_key.replace("*"			, __lowercase					)
															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(__lowercase			, __lowercase			, __lowercase			, __lowercase			, __lowercase					)
															else:
																				set_recursively(__lowercase			, __lowercase			, __lowercase			, __lowercase			, __lowercase					)
															return is_used
					return is_used



def 			lowercase__(		_UpperCamelCase	:			Dict			, _UpperCamelCase	:			int			, _UpperCamelCase	:			int					)-> 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(
															    __lowercase			, __lowercase			, __lowercase			, __lowercase			, hf_model.config.feat_extract_norm == "group"			, )
															_UpperCamelCase									= True
										else:
															_UpperCamelCase									= load_wavaveca_layer(__lowercase			, __lowercase			, __lowercase					)
										if not is_used:
															unused_weights.append(__lowercase					)

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



def 			lowercase__(		_UpperCamelCase	:			Dict			, _UpperCamelCase	:			Any			, _UpperCamelCase	:			str			, _UpperCamelCase	:			Union[str, Any]			, _UpperCamelCase	:			List[Any]					)-> Tuple:






					"""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(__lowercase					)



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






					"""simple docstring"""

					if config_path is not None:
										_UpperCamelCase									= WavaVecaConfig.from_pretrained(__lowercase					)
					else:
										_UpperCamelCase									= WavaVecaConfig()

					if is_seq_class:
										_UpperCamelCase									= read_txt_into_dict(__lowercase					)
										_UpperCamelCase									= idalabel
										_UpperCamelCase									= WavaVecaForSequenceClassification(__lowercase					)
										_UpperCamelCase									= WavaVecaFeatureExtractor(
										    feature_size=1			, sampling_rate=16000			, padding_value=0			, do_normalize=__lowercase			, return_attention_mask=__lowercase			, )
										feature_extractor.save_pretrained(__lowercase					)

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

															# 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(__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					)
															_UpperCamelCase									= target_dict.indices

															# fairseq has the <pad> and <s> switched
															_UpperCamelCase									= 0
															_UpperCamelCase									= 1
															with open(__lowercase			, "w"			, encoding="utf-8"					) as vocab_handle:
																				json.dump(__lowercase			, __lowercase					)
															_UpperCamelCase									= 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			, )
															_UpperCamelCase									= True if config.feat_extract_norm == '''layer''' else False
															_UpperCamelCase									= WavaVecaFeatureExtractor(
															    feature_size=1			, sampling_rate=16000			, padding_value=0			, do_normalize=__lowercase			, return_attention_mask=__lowercase			, )
															_UpperCamelCase									= WavaVecaProcessor(feature_extractor=__lowercase			, tokenizer=__lowercase					)
															processor.save_pretrained(__lowercase					)

										_UpperCamelCase									= WavaVecaForCTC(__lowercase					)
					else:
										_UpperCamelCase									= WavaVecaForPreTraining(__lowercase					)

					if is_finetuned or is_seq_class:
										_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(__lowercase					)

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

					_UpperCamelCase									= model[0].eval()

					recursively_load_weights(__lowercase			, __lowercase			, not is_finetuned					)

					hf_wavavec.save_pretrained(__lowercase					)


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

			snake_case_ :	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							:str					,  __lowercase							:str   )		-> float:

				def get_matched_characters(__lowercase							:str					,  __lowercase							:str   ) -> str:
								_A	:				Union[str, Any]   =      []
								_A	:				Dict   =      min(len(_stra   )					,  len(_stra   )   ) // 2
								for i, l in enumerate(_stra   ):
												_A	:				Tuple   =      int(max(0					,  i - limit   )   )
												_A	:				Union[str, Any]   =      int(min(i + limit + 1					,  len(_stra   )   )   )
												if l in _stra[left:right]:
																matched.append(__lowercase   )
																_A	:				List[Any]   =      f"""{_stra[0:_stra.index(__lowercase   )]} {_stra[_stra.index(__lowercase   ) + 1:]}"""

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

				# transposition
				_A	:				str   =      (
				    len([(ca, ca) for ca, ca in zip(__lowercase					,  __lowercase   ) if ca != ca]   ) // 2
				)

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				Any   =      0
				for ca, ca in zip(stra[:4]					,  stra[:4]   ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler('hello', 'world'))



