





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_lowerCAmelCase							:					List[Any]		    =		'''true'''

def    __snake_case  (    _lowerCAmelCase       :       int , _lowerCAmelCase       :       str=82 , _lowerCAmelCase       :       Tuple=16		)       ->  Optional[int]:
   set_seed(42		)
   A_    : List[Any]								=	RegressionModel()
   A_    : List[Any]								=	deepcopy(_lowerCAmelCase		)
   A_    : Union[str, Any]								=	RegressionDataset(length=_lowerCAmelCase		)
   A_    : str								=	DataLoader(_lowerCAmelCase , batch_size=_lowerCAmelCase		)
   model.to(accelerator.device		)
   A_    : Any								=	accelerator.prepare(_lowerCAmelCase , _lowerCAmelCase		)
   return model, ddp_model, dataloader

def    __snake_case  (    _lowerCAmelCase       :       Accelerator , _lowerCAmelCase       :       List[str]=False		)       ->  Union[str, Any]:
   A_    : List[Any]								=	AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"		)
   A_    : Optional[int]								=	load_dataset("glue" , "mrpc" , split="validation"		)

   def tokenize_function(_lowerCAmelCase       :       Optional[int]		):
      A_    : int								=	tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase		)
      return outputs

   with accelerator.main_process_first():
      A_    : str								=	dataset.map(
          _lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["idx", "sentence1", "sentence2"] , )

   A_    : Any								=	tokenized_datasets.rename_column("label" , "labels"		)

   def collate_fn(_lowerCAmelCase       :       Dict		):
      if use_longest:
         return tokenizer.pad(_lowerCAmelCase , padding="longest" , return_tensors="pt"		)
      return tokenizer.pad(_lowerCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt"		)

   return DataLoader(_lowerCAmelCase , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=16		)

def    __snake_case  (    _lowerCAmelCase       :       List[Any] , _lowerCAmelCase       :       Dict		)       ->  Any:
   A_    : Dict								=	Accelerator(dispatch_batches=_lowerCAmelCase , split_batches=_lowerCAmelCase		)
   A_    : Tuple								=	get_dataloader(_lowerCAmelCase , not dispatch_batches		)
   A_    : Optional[Any]								=	AutoModelForSequenceClassification.from_pretrained(
       "hf-internal-testing/mrpc-bert-base-cased" , return_dict=_lowerCAmelCase		)
   A_    : Tuple								=	accelerator.prepare(_lowerCAmelCase , _lowerCAmelCase		)
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def    __snake_case  (    _lowerCAmelCase       :       str , _lowerCAmelCase       :       int , _lowerCAmelCase       :       int		)       ->  List[Any]:
   A_    : Union[str, Any]								=	[]
   for batch in dataloader:
      A_    : List[str]								=	batch.values()
      with torch.no_grad():
         A_    : Dict								=	model(_lowerCAmelCase		)
         A_    : Optional[int]								=	accelerator.gather_for_metrics((logit, target)		)
         logits_and_targets.append((logit, target)		)
   A_    : Union[str, Any]								=	[], []
   for logit, targ in logits_and_targets:
      logits.append(_lowerCAmelCase		)
      targs.append(_lowerCAmelCase		)
   A_    : Dict								=	torch.cat(_lowerCAmelCase		), torch.cat(_lowerCAmelCase		)
   return logits, targs

def    __snake_case  (    _lowerCAmelCase       :       Accelerator , _lowerCAmelCase       :       List[str]=82 , _lowerCAmelCase       :       Tuple=False , _lowerCAmelCase       :       str=False , _lowerCAmelCase       :       Union[str, Any]=16		)       ->  Tuple:
   A_    : int								=	get_basic_setup(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase		)
   A_    : List[str]								=	generate_predictions(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase		)
   assert (
       len(_lowerCAmelCase		) == num_samples
   ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowerCAmelCase		)}"

def    __snake_case  (    _lowerCAmelCase       :       bool = False , _lowerCAmelCase       :       bool = False		)       ->  Any:
   A_    : List[str]								=	evaluate.load("glue" , "mrpc"		)
   A_    : Union[str, Any]								=	get_mrpc_setup(_lowerCAmelCase , _lowerCAmelCase		)
   # First do baseline
   A_    : Any								=	setup['no']
   model.to(_lowerCAmelCase		)
   model.eval()
   for batch in dataloader:
      batch.to(_lowerCAmelCase		)
      with torch.inference_mode():
         A_    : Union[str, Any]								=	model(**_lowerCAmelCase		)
      A_    : Union[str, Any]								=	outputs.logits.argmax(dim=-1		)
      metric.add_batch(predictions=_lowerCAmelCase , references=batch["labels"]		)
   A_    : List[Any]								=	metric.compute()

   # Then do distributed
   A_    : List[str]								=	setup['ddp']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         A_    : Optional[Any]								=	model(**_lowerCAmelCase		)
      A_    : Optional[Any]								=	outputs.logits.argmax(dim=-1		)
      A_    : Union[str, Any]								=	batch['labels']
      A_    : Optional[int]								=	accelerator.gather_for_metrics((preds, references)		)
      metric.add_batch(predictions=_lowerCAmelCase , references=_lowerCAmelCase		)
   A_    : List[str]								=	metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key] , distributed[key]		), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"

def    __snake_case  (    )       ->  Dict:
   A_    : int								=	Accelerator(split_batches=_lowerCAmelCase , dispatch_batches=_lowerCAmelCase		)
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print("**Testing gather_for_metrics**"		)
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"		)
            test_mrpc(_lowerCAmelCase , _lowerCAmelCase		)
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("**Test torch metrics**"		)
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         A_    : Dict								=	Accelerator(split_batches=_lowerCAmelCase , dispatch_batches=_lowerCAmelCase		)
         if accelerator.is_local_main_process:
            print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"		)
         test_torch_metrics(_lowerCAmelCase , 99		)
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("**Test last batch is not dropped when perfectly divisible**"		)
   A_    : int								=	Accelerator()
   test_torch_metrics(_lowerCAmelCase , 512		)
   accelerator.state._reset_state()

def    __snake_case  (    _lowerCAmelCase       :       Tuple		)       ->  str:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       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.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



