





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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
							# Initialise PyTorch model
							_UpperCamelCase              =						FunnelConfig.from_json_file(lowerCAmelCase					)
							print(f'''Building PyTorch model from configuration: {config}'''					)
							_UpperCamelCase              =						FunnelBaseModel(lowerCAmelCase					) if base_model else FunnelModel(lowerCAmelCase					)

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

							# Save pytorch-model
							print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
							torch.save(model.state_dict()    ,       lowerCAmelCase					)


if __name__ == "__main__":
							lowercase    : Any    	=  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    : Any    	=  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
							)













import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowercase    : str    	=  logging.get_logger(__name__)

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       *A,       **A      ):





            """simple docstring"""

            warnings.warn(
                '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                ''' use CLIPImageProcessor instead.''',       A,       )
            super().__init__(*A,       **A      )






