


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				:       Tuple      =  logging.get_logger(__name__)

A				:       Optional[int]      =  {
    '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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						for attribute in key.split("."			):
												__snake_case				=		getattr(_lowerCAmelCase   ,						_lowerCAmelCase			)

						if weight_type is not None:
												__snake_case				=		getattr(_lowerCAmelCase   ,						_lowerCAmelCase			).shape
						else:
												__snake_case				=		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":
												__snake_case				=		value
						elif weight_type == "weight_g":
												__snake_case				=		value
						elif weight_type == "weight_v":
												__snake_case				=		value
						elif weight_type == "bias":
												__snake_case				=		value
						else:
												__snake_case				=		value

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						__snake_case				=		[]
						__snake_case				=		fairseq_model.state_dict()

						__snake_case				=		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												__snake_case				=		False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						hf_model.config.feat_extract_norm == "group"   ,						)
																		__snake_case				=		True
												else:
																		for key, mapped_key in MAPPING.items():
																								__snake_case				=		"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):
																														__snake_case				=		True
																														if "*" in mapped_key:
																																				__snake_case				=		name.split(_lowerCAmelCase			)[0].split("."			)[-2]
																																				__snake_case				=		mapped_key.replace("*"   ,						_lowerCAmelCase			)
																														if "weight_g" in name:
																																				__snake_case				=		"weight_g"
																														elif "weight_v" in name:
																																				__snake_case				=		"weight_v"
																														elif "weight" in name:
																																				__snake_case				=		"weight"
																														elif "bias" in name:
																																				__snake_case				=		"bias"
																														else:
																																				__snake_case				=		None
																														set_recursively(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
																								continue
												if not is_used:
																		unused_weights.append(_lowerCAmelCase			)

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[Any]:


						'''simple docstring'''






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





@torch.no_grad()
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=None   ,						_lowerCAmelCase=None   ,						_lowerCAmelCase=True			)    ->						List[str]:


						'''simple docstring'''






						if config_path is not None:
												__snake_case				=		HubertConfig.from_pretrained(_lowerCAmelCase			)
						else:
												__snake_case				=		HubertConfig()

						if is_finetuned:
												if dict_path:
																		__snake_case				=		Dictionary.load(_lowerCAmelCase			)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		__snake_case				=		target_dict.pad_index
																		__snake_case				=		target_dict.bos_index
																		__snake_case				=		target_dict.eos_index
																		__snake_case				=		len(target_dict.symbols			)
																		__snake_case				=		os.path.join(_lowerCAmelCase   ,						"vocab.json"			)
																		if not os.path.isdir(_lowerCAmelCase			):
																								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCAmelCase			)			)
																								return
																		os.makedirs(_lowerCAmelCase   ,						exist_ok=_lowerCAmelCase			)
																		with open(_lowerCAmelCase   ,						"w"   ,						encoding="utf-8"			) as vocab_handle:
																								json.dump(target_dict.indices   ,						_lowerCAmelCase			)
																		__snake_case				=		WavaVecaCTCTokenizer(
																		    _lowerCAmelCase   ,						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=_lowerCAmelCase   ,						)
																		__snake_case				=		True if config.feat_extract_norm == "layer" else False
																		__snake_case				=		WavaVecaFeatureExtractor(
																		    feature_size=1   ,						sampling_rate=1_6000   ,						padding_value=0   ,						do_normalize=_lowerCAmelCase   ,						return_attention_mask=_lowerCAmelCase   ,						)
																		__snake_case				=		WavaVecaProcessor(feature_extractor=_lowerCAmelCase   ,						tokenizer=_lowerCAmelCase			)
																		processor.save_pretrained(_lowerCAmelCase			)

												__snake_case				=		HubertForCTC(_lowerCAmelCase			)
						else:
												__snake_case				=		HubertModel(_lowerCAmelCase			)

						if is_finetuned:
												__snake_case		,       __snake_case		,       __snake_case				=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]   ,						arg_overrides={"data": "/".join(dict_path.split("/"			)[:-1]			)}			)
						else:
												__snake_case		,       __snake_case		,       __snake_case				=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

						__snake_case				=		model[0].eval()

						recursively_load_weights(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)

						hf_wavavec.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
	A				:       Optional[Any]      =  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				:       List[str]      =  parser.parse_args()
	convert_hubert_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A				:       Union[str, Any]      =  logging.get_logger(__name__)

A				:       Dict[Optional[str], Type[Formatter]]      =  {}
A				:       Dict[Optional[str], str]      =  {}
A				:       Dict[Optional[str], Exception]      =  {}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None   ,						)    ->						str:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''			)
						__snake_case				=		formatter_cls
						for alias in set(aliases + [format_type]			):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''			)
												__snake_case				=		format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						for alias in set(aliases + [format_type]			):
												__snake_case				=		unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
	A				:       Any      =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
	_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
	A				:       Tuple      =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
	_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
	A				:       Optional[int]      =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
	_register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[str]:


						'''simple docstring'''






						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						**_lowerCAmelCase			)    ->						Formatter:


						'''simple docstring'''






						__snake_case				=		get_format_type_from_alias(_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''''			)





