
'''simple docstring'''





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()
UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    "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      A (       A_							:					Dict    ,							A_							:					Dict    ,							A_							:					List[Any]    ,							A_							:					List[str]    ,							A_							:					int       ):





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

						if weight_type is not None:
												snake_case       :     List[Any] =    getattr(__snake_case    ,							__snake_case       ).shape
						else:
												snake_case       :     str =    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       :     int =    value
						elif weight_type == "weight_g":
												snake_case       :     Optional[Any] =    value
						elif weight_type == "weight_v":
												snake_case       :     Dict =    value
						elif weight_type == "bias":
												snake_case       :     List[str] =    value
						else:
												snake_case       :     int =    value

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

def      A (       A_							:					Dict    ,							A_							:					Optional[Any]    ,							A_							:					Optional[int]       ):
						snake_case       :     int =    []
						snake_case       :     List[Any] =    fairseq_model.state_dict()

						snake_case       :     Union[str, Any] =    hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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

def      A (       A_							:					Union[str, Any]    ,							A_							:					str    ,							A_							:					Any    ,							A_							:					Optional[Any]    ,							A_							:					Any       ):
						snake_case       :     Tuple =    full_name.split('''conv_layers.'''       )[-1]
						snake_case       :     List[Any] =    name.split('''.'''       )
						snake_case       :     Any =    int(items[0]       )
						snake_case       :     List[Any] =    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       :     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."""
																		)
																		snake_case       :     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:
																		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       :     List[str] =    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       :     Optional[int] =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(__snake_case       )

@torch.no_grad()
def      A (       A_							:					str    ,							A_							:					int    ,							A_							:					int=None    ,							A_							:					List[str]=None    ,							A_							:					Dict=True       ):





						if config_path is not None:
												snake_case       :     List[Any] =    HubertConfig.from_pretrained(__snake_case       )
						else:
												snake_case       :     Union[str, Any] =    HubertConfig()

						if is_finetuned:
												if dict_path:
																		snake_case       :     Union[str, Any] =    Dictionary.load(__snake_case       )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		snake_case       :     Any =    target_dict.pad_index
																		snake_case       :     Optional[int] =    target_dict.bos_index
																		snake_case       :     List[Any] =    target_dict.eos_index
																		snake_case       :     int =    len(target_dict.symbols       )
																		snake_case       :     Tuple =    os.path.join(__snake_case    ,							'''vocab.json'''       )
																		if not os.path.isdir(__snake_case       ):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case       )       )
																								return
																		os.makedirs(__snake_case    ,							exist_ok=__snake_case       )
																		with open(__snake_case    ,							'''w'''    ,							encoding='''utf-8'''       ) as vocab_handle:
																								json.dump(target_dict.indices    ,							__snake_case       )
																		snake_case       :     Any =    WavaVecaCTCTokenizer(
																		    __snake_case    ,							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=__snake_case    ,							)
																		snake_case       :     Optional[int] =    True if config.feat_extract_norm == '''layer''' else False
																		snake_case       :     Any =    WavaVecaFeatureExtractor(
																		    feature_size=1    ,							sampling_rate=16000    ,							padding_value=0    ,							do_normalize=__snake_case    ,							return_attention_mask=__snake_case    ,							)
																		snake_case       :     Optional[int] =    WavaVecaProcessor(feature_extractor=__snake_case    ,							tokenizer=__snake_case       )
																		processor.save_pretrained(__snake_case       )

												snake_case       :     Union[str, Any] =    HubertForCTC(__snake_case       )
						else:
												snake_case       :     Any =    HubertModel(__snake_case       )

						if is_finetuned:
												snake_case,					snake_case,					snake_case       :     Optional[Any] =    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       :     Optional[Any] =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

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

						recursively_load_weights(__snake_case    ,							__snake_case    ,							__snake_case       )

						hf_wavavec.save_pretrained(__snake_case       )


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



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   ["ConvBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

