'''simple docstring'''

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      UpperCamelCase_						(					snake_case_       :       int      )					->     Optional[Any]:






 '''simple docstring'''

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

 return orig_key







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       List[str]      )					->     Union[str, Any]:






 '''simple docstring'''

 for key in orig_state_dict.copy().keys():
  __lowerCAmelCase											=						orig_state_dict.pop(SCREAMING_SNAKE_CASE__      )

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

 __lowerCAmelCase											=						orig_state_dict["""cls.predictions.decoder.bias"""]
 __lowerCAmelCase											=						torch.arange(SCREAMING_SNAKE_CASE__      ).expand((1, -1)      ) + 2

 return orig_state_dict







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]						,		snake_case_       :       Optional[int]						,		snake_case_       :       List[str]      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(SCREAMING_SNAKE_CASE__						,		map_location="""cpu"""      )["""model_state_dict"""]
 __lowerCAmelCase											=						YosoConfig.from_json_file(SCREAMING_SNAKE_CASE__      )
 __lowerCAmelCase											=						YosoForMaskedLM(SCREAMING_SNAKE_CASE__      )

 __lowerCAmelCase											=						convert_checkpoint_helper(config.max_position_embeddings						,		SCREAMING_SNAKE_CASE__      )

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

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


if __name__ == "__main__":
 _A			:      Any					      =				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.'''
 )
 _A			:      str					      =				parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A		       =						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['VivitImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'VivitModel',
       'VivitPreTrainedModel',
       'VivitForVideoClassification',
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





