




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     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Union[str, Any]				)  ->							Optional[int]:
    # Initialise PyTorch model
    UpperCAmelCase_										=					FunnelConfig.from_json_file(__UpperCamelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    UpperCAmelCase_										=					FunnelBaseModel(__UpperCamelCase				) if base_model else FunnelModel(__UpperCamelCase				)

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

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


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











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    while cur > 1:
        # Find the maximum number in arr
        UpperCAmelCase_										=					arr.index(max(arr[0:cur]				)				)
        # Reverse from 0 to mi
        UpperCAmelCase_										=					arr[mi::-1] + arr[mi + 1 : len(__UpperCamelCase				)]
        # Reverse whole list
        UpperCAmelCase_										=					arr[cur - 1 :: -1] + arr[cur : len(__UpperCamelCase				)]
        cur -= 1
    return arr


if __name__ == "__main__":
 _lowerCamelCase      			=						input('Enter numbers separated by a comma:\n').strip()
 _lowerCamelCase      			=						[int(item) for item in user_input.split(',')]
 print(pancake_sort(unsorted))





