"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    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.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''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''',
}
__A  =				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	Union[str, Any]      ) ->      Union[str, Any]:



							'''simple docstring'''

							for attribute in key.split("."      ):
														__lowerCamelCase					:		str		   =     getattr(_lowerCamelCase						,   _lowerCamelCase      )

							if weight_type is not None:
														__lowerCamelCase					:		str		   =     getattr(_lowerCamelCase						,   _lowerCamelCase      ).shape
							else:
														__lowerCamelCase					:		Tuple		   =     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":
														__lowerCamelCase					:		Optional[Any]		   =     value
							elif weight_type == "weight_g":
														__lowerCamelCase					:		Optional[Any]		   =     value
							elif weight_type == "weight_v":
														__lowerCamelCase					:		Any		   =     value
							elif weight_type == "bias":
														__lowerCamelCase					:		str		   =     value
							elif weight_type == "running_mean":
														__lowerCamelCase					:		Tuple		   =     value
							elif weight_type == "running_var":
														__lowerCamelCase					:		Optional[int]		   =     value
							elif weight_type == "num_batches_tracked":
														__lowerCamelCase					:		Optional[Any]		   =     value
							elif weight_type == "inv_freq":
														__lowerCamelCase					:		int		   =     value
							else:
														__lowerCamelCase					:		Optional[Any]		   =     value

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


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	str      ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     []
							__lowerCamelCase					:		Union[str, Any]		   =     fairseq_model.state_dict()

							__lowerCamelCase					:		Tuple		   =     hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														__lowerCamelCase					:		Dict		   =     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   hf_model.config.feat_extract_norm == "group"						,   )
																					__lowerCamelCase					:		Optional[Any]		   =     True
														else:
																					for key, mapped_key in MAPPING.items():
																												__lowerCamelCase					:		Dict		   =     "wav2vec2_conformer." + 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]:
																																			__lowerCamelCase					:		Union[str, Any]		   =     True
																																			if "*" in mapped_key:
																																										__lowerCamelCase					:		Union[str, Any]		   =     name.split(_lowerCamelCase      )[0].split("."      )[-2]
																																										__lowerCamelCase					:		int		   =     mapped_key.replace("*"						,   _lowerCamelCase      )
																																			if "pos_bias_u" in name:
																																										__lowerCamelCase					:		List[str]		   =     None
																																			elif "pos_bias_v" in name:
																																										__lowerCamelCase					:		List[str]		   =     None
																																			elif "weight_g" in name:
																																										__lowerCamelCase					:		Any		   =     "weight_g"
																																			elif "weight_v" in name:
																																										__lowerCamelCase					:		str		   =     "weight_v"
																																			elif "bias" in name:
																																										__lowerCamelCase					:		Optional[Any]		   =     "bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__lowerCamelCase					:		int		   =     "weight"
																																			elif "running_mean" in name:
																																										__lowerCamelCase					:		str		   =     "running_mean"
																																			elif "inv_freq" in name:
																																										__lowerCamelCase					:		int		   =     "inv_freq"
																																			elif "running_var" in name:
																																										__lowerCamelCase					:		int		   =     "running_var"
																																			elif "num_batches_tracked" in name:
																																										__lowerCamelCase					:		str		   =     "num_batches_tracked"
																																			else:
																																										__lowerCamelCase					:		Dict		   =     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    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Union[str, Any]      ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     full_name.split("conv_layers."      )[-1]
							__lowerCamelCase					:		Any		   =     name.split("."      )
							__lowerCamelCase					:		Optional[int]		   =     int(items[0]      )
							__lowerCamelCase					:		Any		   =     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."""      )
																					__lowerCamelCase					:		Dict		   =     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."""      )
																					__lowerCamelCase					:		List[str]		   =     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."""      )
																					__lowerCamelCase					:		List[Any]		   =     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."""      )
																					__lowerCamelCase					:		Optional[int]		   =     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    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	List[Any]=None						,   _lowerCamelCase:	str=None						,   _lowerCamelCase:	Optional[Any]=True      ) ->      int:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		List[str]		   =     WavaVecaConformerConfig.from_pretrained(_lowerCamelCase						,   hidden_act="swish"      )
							else:
														__lowerCamelCase					:		Optional[int]		   =     WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														__lowerCamelCase					:		List[str]		   =     "rotary"

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

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					__lowerCamelCase					:		List[str]		   =     target_dict.pad_index
																					__lowerCamelCase					:		Dict		   =     target_dict.bos_index
																					__lowerCamelCase					:		Any		   =     target_dict.eos_index
																					__lowerCamelCase					:		List[str]		   =     len(target_dict.symbols      )
																					__lowerCamelCase					:		str		   =     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      )
																					__lowerCamelCase					:		List[str]		   =     target_dict.indices

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

														__lowerCamelCase					:		List[str]		   =     WavaVecaConformerForCTC(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		str		   =     WavaVecaConformerForPreTraining(_lowerCamelCase      )

							if is_finetuned:
														__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]						,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
							else:
														__lowerCamelCase					:		int		   =     argparse.Namespace(task="audio_pretraining"      )
														__lowerCamelCase					:		str		   =     fairseq.tasks.setup_task(_lowerCamelCase      )

														__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		int		   =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,   task=_lowerCamelCase      )

							__lowerCamelCase					:		Optional[Any]		   =     model[0].eval()

							recursively_load_weights(_lowerCamelCase						,   _lowerCamelCase						,   not is_finetuned      )

							hf_wavavec.save_pretrained(_lowerCamelCase      )


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_wavaveca_conformer_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_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)