





'''simple docstring'''


import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=FunnelConfig.from_json_file(lowercase__       )
     print(F"""Building PyTorch model from configuration: {config}"""       )
     a_       		=FunnelBaseModel(lowercase__       ) if base_model else FunnelModel(lowercase__       )

     # Load weights from tf checkpoint
     load_tf_weights_in_funnel(lowercase__       ,	lowercase__       ,	lowercase__       )

     # Save pytorch-model
     print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
     torch.save(model.state_dict()       ,	lowercase__       )


if __name__ == "__main__":
  lowercase							       =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
  )
  lowercase							       =			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
  )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase							       =			{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

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


