


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()
snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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''',
}
snake_case__			 =						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Optional[Any]	,  a	:   List[Any]	,  a	:   Optional[int]	,  a	:   Optional[int]				)    ->	str:





			"""simple docstring"""
			for attribute in key.split("."				):
						a__ :Union[str, Any]						  =    getattr(a	,  a				)

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

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


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   List[Any]	,  a	:   Any				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Tuple						  =    []
			a__ :Tuple						  =    fairseq_model.state_dict()

			a__ :Optional[int]						  =    hf_model.wavaveca_conformer.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__ :Union[str, Any]						  =    True
						else:
									for key, mapped_key in MAPPING.items():
												a__ :int						  =    "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]:
															a__ :Any						  =    True
															if "*" in mapped_key:
																		a__ :Union[str, Any]						  =    name.split(a				)[0].split("."				)[-2]
																		a__ :str						  =    mapped_key.replace("*"	,  a				)
															if "pos_bias_u" in name:
																		a__ :Dict						  =    None
															elif "pos_bias_v" in name:
																		a__ :str						  =    None
															elif "weight_g" in name:
																		a__ :List[str]						  =    "weight_g"
															elif "weight_v" in name:
																		a__ :Tuple						  =    "weight_v"
															elif "bias" in name:
																		a__ :Any						  =    "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a__ :List[Any]						  =    "weight"
															elif "running_mean" in name:
																		a__ :List[Any]						  =    "running_mean"
															elif "inv_freq" in name:
																		a__ :Union[str, Any]						  =    "inv_freq"
															elif "running_var" in name:
																		a__ :str						  =    "running_var"
															elif "num_batches_tracked" in name:
																		a__ :Optional[Any]						  =    "num_batches_tracked"
															else:
																		a__ :List[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 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   Union[str, Any]	,  a	:   str	,  a	:   Union[str, Any]	,  a	:   Optional[Any]				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Any						  =    full_name.split("conv_layers."				)[-1]
			a__ :Union[str, Any]						  =    name.split("."				)
			a__ :str						  =    int(items[0]				)
			a__ :int						  =    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.'''				)
									a__ :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.'''				)
									a__ :Optional[int]						  =    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.'''				)
									a__ :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.'''				)
									a__ :Any						  =    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 				lowerCamelCase__	(      a	:   str	,  a	:   Dict	,  a	:   int=None	,  a	:   Optional[int]=None	,  a	:   int=True				)    ->	Optional[Any]:





			"""simple docstring"""
			if config_path is not None:
						a__ :Optional[int]						  =    WavaVecaConformerConfig.from_pretrained(a	,  hidden_act="swish"				)
			else:
						a__ :int						  =    WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						a__ :Any						  =    "rotary"

			if is_finetuned:
						if dict_path:
									a__ :Tuple						  =    Dictionary.load(a				)

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

									# fairseq has the <pad> and <s> switched
									a__ :List[Any]						  =    0
									a__ :Dict						  =    1
									with open(a	,  "w"	,  encoding="utf-8"				) as vocab_handle:
												json.dump(a	,  a				)
									a__ :List[str]						  =    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__ :Dict						  =    True if config.feat_extract_norm == "layer" else False
									a__ :Union[str, Any]						  =    WavaVecaFeatureExtractor(
									    feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=a	,  return_attention_mask=a	,  )
									a__ :List[Any]						  =    WavaVecaProcessor(feature_extractor=a	,  tokenizer=a				)
									processor.save_pretrained(a				)

						a__ :List[Any]						  =    WavaVecaConformerForCTC(a				)
			else:
						a__ :Any						  =    WavaVecaConformerForPreTraining(a				)

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

						a__  ,  a__  ,  a__ :Optional[Any]						  =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,  task=a				)

			a__ :List[Any]						  =    model[0].eval()

			recursively_load_weights(a	,  a	,  not is_finetuned				)

			hf_wavavec.save_pretrained(a				)


if __name__ == "__main__":
		snake_case__			 =						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'''
		)
		snake_case__			 =						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
		)






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *


