




import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm
		import torch_xla.debug.metrics as met

class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Union[str, Any]		,      *__magic_name__						:Tuple		,      __magic_name__						:Any=None		,      __magic_name__						:int=None		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								super().__init__(*__magic_name__		,      **__magic_name__				)
								a										=							eval_examples
								a										=							post_process_function
							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:List[str]=None		,      __magic_name__						:Optional[int]=None		,      __magic_name__						:Optional[int]=None		,      __magic_name__						:str = "eval"				):







								'''simple docstring'''







								a										=							self.eval_dataset if eval_dataset is None else eval_dataset
								a										=							self.get_eval_dataloader(__magic_name__				)
								a										=							self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								a										=							self.compute_metrics
								a										=							None
								a										=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								a										=							time.time()
								try:
									a										=							eval_loop(
									    __magic_name__		,      description="""Evaluation"""		,      prediction_loss_only=True if compute_metrics is None else None		,      ignore_keys=__magic_name__		,      metric_key_prefix=__magic_name__		,      )
								finally:
									a										=							compute_metrics
								a										=							self.args.eval_batch_size * self.args.world_size
								if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
								output.metrics.update(
								    speed_metrics(
								        __magic_name__		,      __magic_name__		,      num_samples=output.num_samples		,      num_steps=math.ceil(output.num_samples / total_batch_size				)		,      )				)
								if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
									# Only the main node write the results by default
									a										=							self.post_process_function(__magic_name__		,      __magic_name__		,      output.predictions				)
									a										=							self.compute_metrics(__magic_name__				)

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys()				):
										if not key.startswith(F'{metric_key_prefix}_'				):
											a										=							metrics.pop(__magic_name__				)
									metrics.update(output.metrics				)
								else:
									a										=							output.metrics

								if self.args.should_log:
									# Only the main node log the results by default
									self.log(__magic_name__				)

								if self.args.tpu_metrics_debug or self.args.debug:
									# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
									xm.master_print(met.metrics_report()				)

								a										=							self.callback_handler.on_evaluate(self.args		,      self.state		,      self.control		,      __magic_name__				)
								return metrics





							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:Optional[Any]		,      __magic_name__						:Optional[Any]		,      __magic_name__						:List[Any]=None		,      __magic_name__						:str = "test"				):







								'''simple docstring'''







								a										=							self.get_test_dataloader(__magic_name__				)

								# Temporarily disable metric computation, we will do it in the loop here.
								a										=							self.compute_metrics
								a										=							None
								a										=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								a										=							time.time()
								try:
									a										=							eval_loop(
									    __magic_name__		,      description="""Prediction"""		,      prediction_loss_only=True if compute_metrics is None else None		,      ignore_keys=__magic_name__		,      metric_key_prefix=__magic_name__		,      )
								finally:
									a										=							compute_metrics
								a										=							self.args.eval_batch_size * self.args.world_size
								if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
								output.metrics.update(
								    speed_metrics(
								        __magic_name__		,      __magic_name__		,      num_samples=output.num_samples		,      num_steps=math.ceil(output.num_samples / total_batch_size				)		,      )				)

								if self.post_process_function is None or self.compute_metrics is None:
									return output

								a										=							self.post_process_function(__magic_name__		,      __magic_name__		,      output.predictions		,      """predict"""				)
								a										=							self.compute_metrics(__magic_name__				)

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()				):
									if not key.startswith(F'{metric_key_prefix}_'				):
										a										=							metrics.pop(__magic_name__				)
								metrics.update(output.metrics				)
								return PredictionOutput(predictions=predictions.predictions		,      label_ids=predictions.label_ids		,      metrics=__magic_name__				)







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""projector.weight"""]
	a										=							downstream_dict["""projector.bias"""]
	a										=							downstream_dict["""model.post_net.linear.weight"""]
	a										=							downstream_dict["""model.post_net.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""model.linear.weight"""]
	a										=							downstream_dict["""model.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[Any]:
	a										=							WavaVecaForXVector.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""connector.weight"""]
	a										=							downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
		a										=							downstream_dict[
		    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		a										=							downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	a										=							downstream_dict["""objective.W"""]
	return model





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							torch.load(__lowerCamelCase							,			map_location="""cpu"""    )

	a										=							checkpoint["""Downstream"""]

	a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	a										=							WavaVecaFeatureExtractor.from_pretrained(
	    __lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			do_normalize=__lowerCamelCase    )

	a										=							hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""    ):
		a										=							convert_classification(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForAudioFrameClassification"""    ):
		a										=							convert_diarization(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForXVector"""    ):
		a										=							convert_xvector(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	else:
		raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'    )

	if hf_config.use_weighted_layer_sum:
		a										=							checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(__lowerCamelCase    )
	hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       List[str]    					=      argparse.ArgumentParser()
		parser.add_argument(
		    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
		)
		parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
		parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

