


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     if "model" in orig_key:
          __UpperCamelCase =			orig_key.replace('model.'					,  '' )
     if "norm1" in orig_key:
          __UpperCamelCase =			orig_key.replace('norm1'					,  'attention.output.LayerNorm' )
     if "norm2" in orig_key:
          __UpperCamelCase =			orig_key.replace('norm2'					,  'output.LayerNorm' )
     if "norm" in orig_key:
          __UpperCamelCase =			orig_key.replace('norm'					,  'LayerNorm' )
     if "transformer" in orig_key:
          __UpperCamelCase =			orig_key.split('.' )[0].split('_' )[-1]
          __UpperCamelCase =			orig_key.replace(f'''transformer_{layer_num}'''					,  f'''encoder.layer.{layer_num}''' )
     if "mha.attn" in orig_key:
          __UpperCamelCase =			orig_key.replace('mha.attn'					,  'attention.self' )
     if "mha" in orig_key:
          __UpperCamelCase =			orig_key.replace('mha'					,  'attention' )
     if "W_q" in orig_key:
          __UpperCamelCase =			orig_key.replace('W_q'					,  'self.query' )
     if "W_k" in orig_key:
          __UpperCamelCase =			orig_key.replace('W_k'					,  'self.key' )
     if "W_v" in orig_key:
          __UpperCamelCase =			orig_key.replace('W_v'					,  'self.value' )
     if "ff1" in orig_key:
          __UpperCamelCase =			orig_key.replace('ff1'					,  'intermediate.dense' )
     if "ff2" in orig_key:
          __UpperCamelCase =			orig_key.replace('ff2'					,  'output.dense' )
     if "ff" in orig_key:
          __UpperCamelCase =			orig_key.replace('ff'					,  'output.dense' )
     if "mlm_class" in orig_key:
          __UpperCamelCase =			orig_key.replace('mlm.mlm_class'					,  'cls.predictions.decoder' )
     if "mlm" in orig_key:
          __UpperCamelCase =			orig_key.replace('mlm'					,  'cls.predictions.transform' )
     if "cls" not in orig_key:
          __UpperCamelCase =			'yoso.' + orig_key

     return orig_key






def 		_A		(		_lowercase					,  _lowercase )					-> List[Any]:


     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          __UpperCamelCase =			orig_state_dict.pop(_lowercase )

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               __UpperCamelCase =			val

     __UpperCamelCase =			orig_state_dict['cls.predictions.decoder.bias']
     __UpperCamelCase =			torch.arange(_lowercase ).expand((1, -1) ) + 2

     return orig_state_dict






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


     __UpperCamelCase =			torch.load(_lowercase					,  map_location='cpu' )['model_state_dict']
     __UpperCamelCase =			YosoConfig.from_json_file(_lowercase )
     __UpperCamelCase =			YosoForMaskedLM(_lowercase )

     __UpperCamelCase =			convert_checkpoint_helper(config.max_position_embeddings					,  _lowercase )

     print(model.load_state_dict(_lowercase ) )
     model.eval()
     model.save_pretrained(_lowercase )

     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The json file for YOSO model config.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __snake_case   							=						parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__snake_case   							=						get_tests_dir('''fixtures''')



class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			mock.Mock()
            __UpperCamelCase =			500
            __UpperCamelCase =			{}
            __UpperCamelCase =			HTTPError
            __UpperCamelCase =			{}

            # Download this model to make sure it's in the cache.
            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request',return_value=A_					) as mock_head:
                 __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'					)



@is_staging_test
class 	__lowerCamelCase			(unittest.TestCase						):







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			TOKEN
            HfFolder.save_token(A_					)







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            try:
                 delete_repo(token=cls._token,repo_id='test-feature-extractor'					)
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token,repo_id='valid_org/test-feature-extractor-org'					)
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token,repo_id='test-dynamic-feature-extractor'					)
            except HTTPError:
                 pass







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

            # Reset repo
            delete_repo(token=self._token,repo_id='test-feature-extractor'					)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     A_,repo_id='test-feature-extractor',push_to_hub=A_,use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('valid_org/test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

            # Reset repo
            delete_repo(token=self._token,repo_id='valid_org/test-feature-extractor'					)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     A_,repo_id='valid_org/test-feature-extractor-org',push_to_hub=A_,use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            CustomFeatureExtractor.register_for_auto_class()
            __UpperCamelCase =			CustomFeatureExtractor.from_pretrained(A_					)

            feature_extractor.push_to_hub('test-dynamic-feature-extractor',use_auth_token=self._token					)

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map,{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'},)

            __UpperCamelCase =			AutoFeatureExtractor.from_pretrained(
                F'''{USER}/test-dynamic-feature-extractor''',trust_remote_code=A_					)
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__,'CustomFeatureExtractor'					)


