




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


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

							return orig_key
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							for key in orig_state_dict.copy().keys():
														_a     					=    orig_state_dict.pop(UpperCamelCase				)

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

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

							return orig_state_dict
def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


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

							_a     					=    convert_checkpoint_helper(config.max_position_embeddings			,						UpperCamelCase				)

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

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


if __name__ == "__main__":
				_snake_case				:				Dict       							=		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				:				List[Any]       							=		parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







'''simple docstring'''


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_snake_case				:				int       							=		get_tests_dir('fixtures')
_snake_case				:				Tuple       							=		get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_snake_case				:				Optional[int]       							=		get_tests_dir('fixtures/dummy-config.json')

class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  int )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    0







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							int:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''' )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Any:


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a     					=    WavaVecaConfig()

																					# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ ).to_dict()

																					config_dict.pop('''feature_extractor_type''' )
																					_a     					=    WavaVecaFeatureExtractor(**lowerCAmelCase_ )

																					# save in new folder
																					model_config.save_pretrained(lowerCAmelCase_ )
																					config.save_pretrained(lowerCAmelCase_ )

																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )

																					# make sure private variable is not incorrectly saved
																					_a     					=    json.loads(config.to_json_string() )
																					self.assertTrue('''_processor_class''' not in dict_as_saved )

														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							str:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      '''bert-base is not a local folder and is not a valid model identifier''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''bert-base''' )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Union[str, Any]:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_							,      revision='''aaaaaa''' )







							def        __lowerCAmelCase      (  self	:  Any )      ->							Dict:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''							,      ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''' )







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Any:


														"""simple docstring"""


														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
														# If remote code is disabled, we can't load this config.
														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )

														_a     					=    AutoFeatureExtractor.from_pretrained(
														    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
														self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )

														# Test feature extractor can be reloaded.
														with tempfile.TemporaryDirectory() as tmp_dir:
																					feature_extractor.save_pretrained(lowerCAmelCase_ )
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_							,      trust_remote_code=lowerCAmelCase_ )
														self.assertEqual(reloaded_feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )







							def        __lowerCAmelCase      (  self	:  int )      ->							Optional[Any]:


														"""simple docstring"""


														try:
																					AutoConfig.register('''custom'''							,      lowerCAmelCase_ )
																					AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )
																					# Trying to register something existing in the Transformers library will raise an error
																					with self.assertRaises(lowerCAmelCase_ ):
																												AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )

																					# Now that the config is registered, it can be used as any other config with the auto-API
																					_a     					=    CustomFeatureExtractor.from_pretrained(lowerCAmelCase_ )
																					with tempfile.TemporaryDirectory() as tmp_dir:
																												feature_extractor.save_pretrained(lowerCAmelCase_ )
																												_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
																												self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																												del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Any:


														"""simple docstring"""


														class       A   (		_a   ):
																					lowercase_          =    True

														try:
																					AutoConfig.register('''custom'''							,      lowerCAmelCase_ )
																					AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )
																					# If remote code is not set, the default is to use local
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote code is disabled, we load the local one.
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote is enabled, we load from the Hub
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(not hasattr(lowerCAmelCase_							,      '''is_local''' ) )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																												del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]

