



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					)     ->   Dict:






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, 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 T5 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.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 if not head:
  return True
 # split the list to two parts
 a		,  a						=		head.next, head
 while fast and fast.next:
  a						=		fast.next.next
  a						=		slow.next
 a						=		slow.next
 a						=		None  # Don't forget here! But forget still works!
 # reverse the second part
 a						=		None
 while second:
  a						=		second.next
  a						=		node
  a						=		second
  a						=		nxt
 # compare two parts
 # second part has the same or one less node
 while node:
  if node.val != head.val:
   return False
  a						=		node.next
  a						=		head.next
 return True


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 if not head or not head.next:
  return True

 # 1. Get the midpoint (slow)
 a						=		a						=		a						=		head
 while fast and fast.next:
  a		,  a						=		fast.next.next, slow.next

 # 2. Push the second half into the stack
 a						=		[slow.val]
 while slow.next:
  a						=		slow.next
  stack.append(slow.val					)

 # 3. Comparison
 while stack:
  if stack.pop() != cur.val:
   return False
  a						=		cur.next

 return True




def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 if not head or not head.next:
  return True
 a						=		{}
 a						=		0
 while head:
  if head.val in d:
   d[head.val].append(UpperCAmelCase__					)
  else:
   a						=		[pos]
  a						=		head.next
  pos += 1
 a						=		pos - 1
 a						=		0
 for v in d.values():
  if len(UpperCAmelCase__					) % 2 != 0:
   middle += 1
  else:
   a						=		0
   for i in range(0 ,					len(UpperCAmelCase__					)					):
    if v[i] + v[len(UpperCAmelCase__					) - 1 - step] != checksum:
     return False
    step += 1
  if middle > 1:
   return False
 return True

