


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

a : Optional[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''',
}
a : Optional[Any]		=				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







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

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

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

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

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







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

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

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


a : Tuple		=				{
    '''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       ,			_UpperCamelCase       ,			_UpperCamelCase=None       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







			__lowercase 					=   False
			for key, mapped_key in MAPPING.items():
						__lowercase 					=   '''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]:
									__lowercase 					=   True
									if "*" in mapped_key:
												__lowercase 					=   name.split(_UpperCamelCase  )[0].split('''.'''  )[-2]
												__lowercase 					=   mapped_key.replace('''*'''       ,			_UpperCamelCase  )
									if "weight_g" in name:
												__lowercase 					=   '''weight_g'''
									elif "weight_v" in name:
												__lowercase 					=   '''weight_v'''
									elif "bias" in name:
												__lowercase 					=   '''bias'''
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__lowercase 					=   '''weight'''
									else:
												__lowercase 					=   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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   fairseq_model.state_dict()

			__lowercase 					=   hf_model.wavaveca.feature_extractor

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

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   full_name.split('''conv_layers.'''  )[-1]
			__lowercase 					=   name.split('''.'''  )
			__lowercase 					=   int(items[0]  )
			__lowercase 					=   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.'  )
									__lowercase 					=   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.'  )
									__lowercase 					=   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.'  )
									__lowercase 					=   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.'  )
									__lowercase 					=   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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase=None       ,			_UpperCamelCase=None       ,			_UpperCamelCase=True       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







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

			if is_seq_class:
						__lowercase 					=   read_txt_into_dict(_UpperCamelCase  )
						__lowercase 					=   idalabel
						__lowercase 					=   WavaVecaForSequenceClassification(_UpperCamelCase  )
						__lowercase 					=   WavaVecaFeatureExtractor(
						    feature_size=1       ,			sampling_rate=1_60_00       ,			padding_value=0       ,			do_normalize=_UpperCamelCase       ,			return_attention_mask=_UpperCamelCase       ,			)
						feature_extractor.save_pretrained(_UpperCamelCase  )

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

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									__lowercase 					=   target_dict.pad_index
									__lowercase 					=   target_dict.bos_index
									__lowercase 					=   target_dict.eos_index
									__lowercase 					=   len(target_dict.symbols  )
									__lowercase 					=   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  )
									__lowercase 					=   target_dict.indices

									# fairseq has the <pad> and <s> switched
									__lowercase 					=   0
									__lowercase 					=   1
									with open(_UpperCamelCase       ,			'''w'''       ,			encoding='''utf-8'''  ) as vocab_handle:
												json.dump(_UpperCamelCase       ,			_UpperCamelCase  )
									__lowercase 					=   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       ,			)
									__lowercase 					=   True if config.feat_extract_norm == '''layer''' else False
									__lowercase 					=   WavaVecaFeatureExtractor(
									    feature_size=1       ,			sampling_rate=1_60_00       ,			padding_value=0       ,			do_normalize=_UpperCamelCase       ,			return_attention_mask=_UpperCamelCase       ,			)
									__lowercase 					=   WavaVecaProcessor(feature_extractor=_UpperCamelCase       ,			tokenizer=_UpperCamelCase  )
									processor.save_pretrained(_UpperCamelCase  )

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

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

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

			__lowercase 					=   model[0].eval()

			recursively_load_weights(_UpperCamelCase       ,			_UpperCamelCase       ,			not is_finetuned  )

			hf_wavavec.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : 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''',
	)
	a : int		=				parser.parse_args()

	a : 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,
	)






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a : Optional[int]		=				typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a : Union[str, Any]		=				typing.Union[np.floataa, int, float]  # noqa: UP007


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return np.sqrt(np.sum((np.asarray(_UpperCamelCase  ) - np.asarray(_UpperCamelCase  )) ** 2  )  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return sum((va - va) ** 2 for va, va in zip(_UpperCamelCase       ,			_UpperCamelCase  )  ) ** (1 / 2)


if __name__ == "__main__":


	def 		lowercase_  (     ):


				'''simple docstring'''







				from timeit import timeit

				print('''Without Numpy'''  )
				print(
				    timeit(
				        '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''       ,			number=1_00_00       ,			globals=globals()       ,			)  )
				print('''With Numpy'''  )
				print(
				    timeit(
				        '''euclidean_distance([1, 2, 3], [4, 5, 6])'''       ,			number=1_00_00       ,			globals=globals()       ,			)  )





	benchmark()


