




'''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()
_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_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",
}
_SCREAMING_SNAKE_CASE			=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			for attribute in key.split('.'     ):
						A_									= getattr(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )

			if weight_type is not None:
						A_									= getattr(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     ).shape
			else:
						A_									= 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_									= value
			elif weight_type == "weight_g":
						A_									= value
			elif weight_type == "weight_v":
						A_									= value
			elif weight_type == "bias":
						A_									= value
			elif weight_type == "running_mean":
						A_									= value
			elif weight_type == "running_var":
						A_									= value
			elif weight_type == "num_batches_tracked":
						A_									= value
			elif weight_type == "inv_freq":
						A_									= value
			else:
						A_									= value

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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			A_									= []
			A_									= fairseq_model.state_dict()

			A_									= hf_model.wavaveca_conformer.feature_extractor

			for name, value in fairseq_dict.items():
						A_									= False
						if "conv_layers" in name:
									load_conv_layer(
									    SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,hf_model.config.feat_extract_norm == 'group'       ,)
									A_									= True
						else:
									for key, mapped_key in MAPPING.items():
												A_									= '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_									= True
															if "*" in mapped_key:
																		A_									= name.split(SCREAMING_SNAKE_CASE_     )[0].split('.'     )[-2]
																		A_									= mapped_key.replace('*'       ,SCREAMING_SNAKE_CASE_     )
															if "pos_bias_u" in name:
																		A_									= None
															elif "pos_bias_v" in name:
																		A_									= None
															elif "weight_g" in name:
																		A_									= 'weight_g'
															elif "weight_v" in name:
																		A_									= 'weight_v'
															elif "bias" in name:
																		A_									= 'bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		A_									= 'weight'
															elif "running_mean" in name:
																		A_									= 'running_mean'
															elif "inv_freq" in name:
																		A_									= 'inv_freq'
															elif "running_var" in name:
																		A_									= 'running_var'
															elif "num_batches_tracked" in name:
																		A_									= 'num_batches_tracked'
															else:
																		A_									= None
															set_recursively(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
												continue
						if not is_used:
									unused_weights.append(SCREAMING_SNAKE_CASE_     )

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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		int:
			A_									= full_name.split('conv_layers.'     )[-1]
			A_									= name.split('.'     )
			A_									= int(items[0]     )
			A_									= 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_									= 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_									= 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_									= 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_									= value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
			else:
						unused_weights.append(SCREAMING_SNAKE_CASE_     )





@torch.no_grad()
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=None       ,SCREAMING_SNAKE_CASE_=None       ,SCREAMING_SNAKE_CASE_=True     )		->		Optional[Any]:
			if config_path is not None:
						A_									= WavaVecaConformerConfig.from_pretrained(SCREAMING_SNAKE_CASE_       ,hidden_act='swish'     )
			else:
						A_									= WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						A_									= 'rotary'

			if is_finetuned:
						if dict_path:
									A_									= Dictionary.load(SCREAMING_SNAKE_CASE_     )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									A_									= target_dict.pad_index
									A_									= target_dict.bos_index
									A_									= target_dict.eos_index
									A_									= len(target_dict.symbols     )
									A_									= os.path.join(SCREAMING_SNAKE_CASE_       ,'vocab.json'     )
									if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE_     )     )
												return
									os.makedirs(SCREAMING_SNAKE_CASE_       ,exist_ok=SCREAMING_SNAKE_CASE_     )
									A_									= target_dict.indices

									# fairseq has the <pad> and <s> switched
									A_									= 0
									A_									= 1
									with open(SCREAMING_SNAKE_CASE_       ,'w'       ,encoding='utf-8'     ) as vocab_handle:
												json.dump(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
									A_									= WavaVecaCTCTokenizer(
									    SCREAMING_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=SCREAMING_SNAKE_CASE_       ,)
									A_									= True if config.feat_extract_norm == 'layer' else False
									A_									= WavaVecaFeatureExtractor(
									    feature_size=1       ,sampling_rate=1_6000       ,padding_value=0       ,do_normalize=SCREAMING_SNAKE_CASE_       ,return_attention_mask=SCREAMING_SNAKE_CASE_       ,)
									A_									= WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_       ,tokenizer=SCREAMING_SNAKE_CASE_     )
									processor.save_pretrained(SCREAMING_SNAKE_CASE_     )

						A_									= WavaVecaConformerForCTC(SCREAMING_SNAKE_CASE_     )
			else:
						A_									= WavaVecaConformerForPreTraining(SCREAMING_SNAKE_CASE_     )

			if is_finetuned:
						A_		, A_		, A_									= fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       ,arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
			else:
						A_									= argparse.Namespace(task='audio_pretraining'     )
						A_									= fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE_     )

						A_		, A_		, A_									= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,task=SCREAMING_SNAKE_CASE_     )

			A_									= model[0].eval()

			recursively_load_weights(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,not is_finetuned     )

			hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							_SCREAMING_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"
							)
							_SCREAMING_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
							)










'''simple docstring'''
import os
import sys

_SCREAMING_SNAKE_CASE			=						os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_SCREAMING_SNAKE_CASE			=						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		int:
			return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		List[str]:
			return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModel.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Dict:
			return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		str:
			return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )




