

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__				=		logging.get_logger(__name__)

a__				=		{
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def    __UpperCAmelCase (	__a      :			Any		,__a      :			Optional[Any]		,__a      :			Any		,__a      :			Any		,__a      :			Union[str, Any]	)			->     Optional[int]:

					"""simple docstring"""

					for attribute in key.split('''.'''	):
										_a	:    List[str]         =		getattr(__a		,__a	)

					if weight_type is not None:
										_a	:    int         =		getattr(__a		,__a	).shape
					else:
										_a	:    Dict         =		hf_pointer.shape

					assert hf_shape == value.shape, (
					    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	:    Optional[int]         =		value
					elif weight_type == "weight_g":
										_a	:    List[Any]         =		value
					elif weight_type == "weight_v":
										_a	:    Optional[Any]         =		value
					elif weight_type == "bias":
										_a	:    str         =		value
					else:
										_a	:    int         =		value

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



def    __UpperCAmelCase (	__a      :			Dict		,__a      :			Any		,__a      :			Tuple	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    int         =		[]
					_a	:    int         =		fairseq_model.state_dict()

					_a	:    Dict         =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										_a	:    Optional[int]         =		False
										if "conv_layers" in name:
															load_conv_layer(
															    __a		,__a		,__a		,__a		,hf_model.config.feat_extract_norm == '''group'''		,)
															_a	:    str         =		True
										else:
															for key, mapped_key in MAPPING.items():
																				_a	:    Optional[int]         =		'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																				if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
																									_a	:    Union[str, Any]         =		True
																									if "*" in mapped_key:
																														_a	:    List[Any]         =		name.split(__a	)[0].split('''.'''	)[-2]
																														_a	:    List[str]         =		mapped_key.replace('''*'''		,__a	)
																									if "weight_g" in name:
																														_a	:    int         =		'''weight_g'''
																									elif "weight_v" in name:
																														_a	:    Tuple         =		'''weight_v'''
																									elif "weight" in name:
																														_a	:    Optional[int]         =		'''weight'''
																									elif "bias" in name:
																														_a	:    str         =		'''bias'''
																									else:
																														_a	:    Optional[Any]         =		None
																									set_recursively(__a		,__a		,__a		,__a		,__a	)
																				continue
										if not is_used:
															unused_weights.append(__a	)

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



def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			Optional[int]		,__a      :			str		,__a      :			int		,__a      :			str	)			->     Tuple:

					"""simple docstring"""

					_a	:    int         =		full_name.split('''conv_layers.'''	)[-1]
					_a	:    Any         =		name.split('''.'''	)
					_a	:    List[str]         =		int(items[0]	)
					_a	:    Optional[int]         =		int(items[1]	)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    F"""{full_name} has size {value.shape}, but"""
															    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
															)
															_a	:    Optional[Any]         =		value
															logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    F"""{full_name} has size {value.shape}, but"""
															    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
															)
															_a	:    Tuple         =		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:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
															    " found."
															)
															_a	:    Optional[int]         =		value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    F"""{full_name} has size {value.shape}, but"""
															    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
															)
															_a	:    Tuple         =		value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
					else:
										unused_weights.append(__a	)



@torch.no_grad()
def    __UpperCAmelCase (	__a      :			int		,__a      :			int		,__a      :			str=None		,__a      :			List[Any]=None		,__a      :			List[str]=True	)			->     Tuple:

					"""simple docstring"""

					if config_path is not None:
										_a	:    Optional[Any]         =		HubertConfig.from_pretrained(__a	)
					else:
										_a	:    List[str]         =		HubertConfig()

					if is_finetuned:
										if dict_path:
															_a	:    Dict         =		Dictionary.load(__a	)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_a	:    Union[str, Any]         =		target_dict.pad_index
															_a	:    Any         =		target_dict.bos_index
															_a	:    Optional[int]         =		target_dict.eos_index
															_a	:    Optional[Any]         =		len(target_dict.symbols	)
															_a	:    int         =		os.path.join(__a		,'''vocab.json'''	)
															if not os.path.isdir(__a	):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__a	)	)
																				return
															os.makedirs(__a		,exist_ok=__a	)
															with open(__a		,'''w'''		,encoding='''utf-8'''	) as vocab_handle:
																				json.dump(target_dict.indices		,__a	)
															_a	:    Tuple         =		WavaVecaCTCTokenizer(
															    __a		,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=__a		,)
															_a	:    Tuple         =		True if config.feat_extract_norm == '''layer''' else False
															_a	:    Tuple         =		WavaVecaFeatureExtractor(
															    feature_size=1		,sampling_rate=16_000		,padding_value=0		,do_normalize=__a		,return_attention_mask=__a		,)
															_a	:    Tuple         =		WavaVecaProcessor(feature_extractor=__a		,tokenizer=__a	)
															processor.save_pretrained(__a	)

										_a	:    Tuple         =		HubertForCTC(__a	)
					else:
										_a	:    Optional[int]         =		HubertModel(__a	)

					if is_finetuned:
										_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      ,					_a      ,					_a	:    List[Any]         =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

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

					recursively_load_weights(__a		,__a		,__a	)

					hf_wavavec.save_pretrained(__a	)


if __name__ == "__main__":
			a__				=		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'''
			)
			a__				=		parser.parse_args()
			convert_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)






def    __UpperCAmelCase (	__a      :			float	)			->     float:

					"""simple docstring"""

					return 10 - x * x



def    __UpperCAmelCase (	__a      :			float		,__a      :			float	)			->     float:

					"""simple docstring"""

					if equation(__a	) * equation(__a	) >= 0:
										raise ValueError('''Wrong space!'''	)

					_a	:    Dict         =		a
					while (b - a) >= 0.01:
										# Find middle point
										_a	:    Any         =		(a + b) / 2
										# Check if middle point is root
										if equation(__a	) == 0.0:
															break
										# Decide the side to repeat the steps
										if equation(__a	) * equation(__a	) < 0:
															_a	:    str         =		c
										else:
															_a	:    Union[str, Any]         =		c
					return c


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(bisection(-2, 5))
			print(bisection(0, 6))



