



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       :	List[str]            =logging.get_logger(__name__)

__snake_case       :	List[Any]            ={
    '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       :	Union[str, Any]            =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	int):





				'''simple docstring'''

				for attribute in key.split('''.'''):
								lowerCAmelCase__  :       Optional[int]				=  getattr(lowerCamelCase_ ,lowerCamelCase_)

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

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Tuple):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  []
				lowerCAmelCase__  :       Optional[int]				=  fairseq_model.state_dict()

				lowerCAmelCase__  :       Tuple				=  hf_model.wavaveca_conformer.feature_extractor

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





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[int]				=  full_name.split('''conv_layers.''')[-1]
				lowerCAmelCase__  :       Optional[int]				=  name.split('''.''')
				lowerCAmelCase__  :       Optional[int]				=  int(items[0])
				lowerCAmelCase__  :       Optional[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__  :       Any				=  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__  :       Optional[Any]				=  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__  :       Dict				=  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__  :       Tuple				=  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_	:	List[Any] ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Tuple=None ,lowerCamelCase_	:	Tuple=None ,lowerCamelCase_	:	List[str]=True):





				'''simple docstring'''

				if config_path is not None:
								lowerCAmelCase__  :       Tuple				=  WavaVecaConformerConfig.from_pretrained(lowerCamelCase_ ,hidden_act='''swish''')
				else:
								lowerCAmelCase__  :       Optional[int]				=  WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								lowerCAmelCase__  :       Tuple				=  '''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__  :       Dict				=  target_dict.pad_index
												lowerCAmelCase__  :       int				=  target_dict.bos_index
												lowerCAmelCase__  :       Tuple				=  target_dict.eos_index
												lowerCAmelCase__  :       Optional[int]				=  len(target_dict.symbols)
												lowerCAmelCase__  :       Union[str, Any]				=  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__  :       Dict				=  target_dict.indices

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

								lowerCAmelCase__  :       Union[str, Any]				=  WavaVecaConformerForCTC(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       str				=  WavaVecaConformerForPreTraining(lowerCamelCase_)

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

								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,task=lowerCamelCase_)

				lowerCAmelCase__  :       List[str]				=  model[0].eval()

				recursively_load_weights(lowerCamelCase_ ,lowerCamelCase_ ,not is_finetuned)

				hf_wavavec.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case       :	List[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'
							)
							__snake_case       :	int            =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 scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__snake_case       :	str            ='\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

__snake_case       :	Tuple            ='\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

__snake_case       :	int            ='\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

				return float((preds == labels).mean())






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  simple_accuracy(lowerCamelCase_ ,lowerCamelCase_)
				lowerCAmelCase__  :       Any				=  float(fa_score(y_true=lowerCamelCase_ ,y_pred=lowerCamelCase_))
				return {
				    "accuracy": acc,
				    "f1": fa,
				}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  float(pearsonr(lowerCamelCase_ ,lowerCamelCase_)[0])
				lowerCAmelCase__  :       int				=  float(spearmanr(lowerCamelCase_ ,lowerCamelCase_)[0])
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,       _KWARGS_DESCRIPTION)
class  lowerCamelCase__    (							datasets.Metric):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						if self.config_name not in [
						    "sst2",
						    "mnli",
						    "mnli_mismatched",
						    "mnli_matched",
						    "cola",
						    "stsb",
						    "mrpc",
						    "qqp",
						    "qnli",
						    "rte",
						    "wnli",
						    "hans",
						]:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
										    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
						return datasets.MetricInfo(
						    description=_DESCRIPTION	,citation=_CITATION	,inputs_description=_KWARGS_DESCRIPTION	,features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
						            '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
						        }				)	,codebase_urls=[]	,reference_urls=[]	,format='''numpy'''	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						if self.config_name == "cola":
										return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase	,__lowerCamelCase				)}
						elif self.config_name == "stsb":
										return pearson_and_spearman(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["mrpc", "qqp"]:
										return acc_and_fa(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
										return {"accuracy": simple_accuracy(__lowerCamelCase	,__lowerCamelCase				)}
						else:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
										    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)

