
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase  :					str						    =							500000

lowercase  :					Tuple						    =							os.path.split(__file__)
lowercase  :					Tuple						    =							os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    Any:
       lowercase							:     Any       	=      dataset.map(**__lowerCamelCase				)







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     List[Any]       	=      dataset.filter(**__lowerCamelCase				)







def 						_snake_case(  ) ->    Optional[Any]:
       lowercase							:     Optional[Any]       	=      {"""num examples""": SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              lowercase							:     int       	=      datasets.Features({"""text""": datasets.Value("""string"""				), """numbers""": datasets.Value("""float32"""				)}				)
              lowercase							:     Any       	=      generate_example_dataset(
                  os.path.join(__lowerCamelCase					,	"""dataset.arrow"""				)					,	__lowerCamelCase					,	num_examples=__lowerCamelCase				)

              lowercase							:     Union[str, Any]       	=      transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""					,	use_fast=__lowerCamelCase				)

              def tokenize(SCREAMING_SNAKE_CASE__				):
                     return tokenizer(examples["""text"""]				)

              lowercase							:     List[Any]       	=      map(__lowerCamelCase				)

              lowercase							:     Any       	=      map(__lowerCamelCase					,	batched=__lowerCamelCase				)

              lowercase							:     Optional[Any]       	=      map(__lowerCamelCase					,	function=lambda SCREAMING_SNAKE_CASE__				: None					,	batched=__lowerCamelCase				)

              with dataset.formatted_as(type="""numpy"""				):
                     lowercase							:     Tuple       	=      map(__lowerCamelCase					,	function=lambda SCREAMING_SNAKE_CASE__				: None					,	batched=__lowerCamelCase				)

              with dataset.formatted_as(type="""pandas"""				):
                     lowercase							:     List[Any]       	=      map(__lowerCamelCase					,	function=lambda SCREAMING_SNAKE_CASE__				: None					,	batched=__lowerCamelCase				)

              with dataset.formatted_as(type="""torch"""					,	columns="""numbers"""				):
                     lowercase							:     Optional[int]       	=      map(__lowerCamelCase					,	function=lambda SCREAMING_SNAKE_CASE__				: None					,	batched=__lowerCamelCase				)

              with dataset.formatted_as(type="""tensorflow"""					,	columns="""numbers"""				):
                     lowercase							:     Tuple       	=      map(__lowerCamelCase					,	function=lambda SCREAMING_SNAKE_CASE__				: None					,	batched=__lowerCamelCase				)

              lowercase							:     Dict       	=      map(__lowerCamelCase					,	function=__lowerCamelCase					,	batched=__lowerCamelCase				)

              lowercase							:     int       	=      filter(__lowerCamelCase				)

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

       with open(__lowerCamelCase					,	"""wb"""				) as f:
              f.write(json.dumps(__lowerCamelCase				).encode("""utf-8"""				)				)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()






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)

