


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowercase_           =	['UtteranceLevel']
@torch.no_grad()
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple							=						torch.load(SCREAMING_SNAKE_CASE__			,       map_location='cpu'						)
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(f'The supported s3prl models are {SUPPORTED_MODELS}'						)

 __lowerCamelCase		:							List[str]							=						checkpoint['Downstream']

 __lowerCamelCase		:							Optional[Any]							=						HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							str							=						HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__			,       config=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[Any]							=						WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE__			,       return_attention_mask=SCREAMING_SNAKE_CASE__			,       do_normalize=SCREAMING_SNAKE_CASE__						)

 if hf_congfig.use_weighted_layer_sum:
  __lowerCamelCase		:							Optional[int]							=						checkpoint['Featurizer']['weights']

 __lowerCamelCase		:							int							=						downstream_dict['projector.weight']
 __lowerCamelCase		:							Any							=						downstream_dict['projector.bias']
 __lowerCamelCase		:							Any							=						downstream_dict['model.post_net.linear.weight']
 __lowerCamelCase		:							str							=						downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE__						)


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





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import CamembertModel


@require_torch
@require_sentencepiece
@require_tokenizers
class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

