


from __future__ import annotations

__a	 =      []
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   for i in range(len(lowerCAmelCase_ ) ):
      if board[row][i] == 1:
         return False
   for i in range(len(lowerCAmelCase_ ) ):
      if board[i][column] == 1:
         return False
   for i, j in zip(range(lowerCAmelCase_	,    -1	,    -1 )	,    range(lowerCAmelCase_	,    -1	,    -1 ) ):
      if board[i][j] == 1:
         return False
   for i, j in zip(range(lowerCAmelCase_	,    -1	,    -1 )	,    range(lowerCAmelCase_	,    len(lowerCAmelCase_ ) ) ):
      if board[i][j] == 1:
         return False
   return True
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   if row >= len(lowerCAmelCase_ ):
      solution.append(lowerCAmelCase_ )
      printboard(lowerCAmelCase_ )
      print()
      return True
   for i in range(len(lowerCAmelCase_ ) ):
      if is_safe(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ ):
         UpperCAmelCase      					=  1
         solve(lowerCAmelCase_	,    row + 1 )
         UpperCAmelCase      					=  0
   return False
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->None:
   for i in range(len(lowerCAmelCase_ ) ):
      for j in range(len(lowerCAmelCase_ ) ):
         if board[i][j] == 1:
            print("""Q"""	,    end=""" """ )
         else:
            print("""."""	,    end=""" """ )
      print()


# n=int(input("The no. of queens"))
__a	 =      8
__a	 =      [[0 for i in range(n)] for j in range(n)]
solve(board, 0)







print("""The total no. of solutions are :""", len(solution))







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   UpperCAmelCase      					=  ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

   UpperCAmelCase      					=  (
       ("""layer.""", """layer_"""),
       ("""word_embeddings.weight""", """word_embeddings"""),
       ("""position_embeddings.weight""", """position_embeddings"""),
       ("""token_type_embeddings.weight""", """token_type_embeddings"""),
       (""".""", """/"""),
       ("""LayerNorm/weight""", """LayerNorm/gamma"""),
       ("""LayerNorm/bias""", """LayerNorm/beta"""),
       ("""weight""", """kernel"""),
   )

   if not os.path.isdir(lowerCAmelCase_ ):
      os.makedirs(lowerCAmelCase_ )

   UpperCAmelCase      					=  model.state_dict()

   def to_tf_var_name(lowerCAmelCase_ ):
      for patt, repl in iter(lowerCAmelCase_ ):
         UpperCAmelCase      					=  name.replace(lowerCAmelCase_	,    lowerCAmelCase_ )
      return F"""bert/{name}"""

   def create_tf_var(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ ):
      UpperCAmelCase      					=  tf.dtypes.as_dtype(tensor.dtype )
      UpperCAmelCase      					=  tf.get_variable(dtype=lowerCAmelCase_	,    shape=tensor.shape	,    name=lowerCAmelCase_	,    initializer=tf.zeros_initializer() )
      session.run(tf.variables_initializer([tf_var] ) )
      session.run(lowerCAmelCase_ )
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         UpperCAmelCase      					=  to_tf_var_name(lowerCAmelCase_ )
         UpperCAmelCase      					=  state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose ):
            UpperCAmelCase      					=  torch_tensor.T
         UpperCAmelCase      					=  create_tf_var(tensor=lowerCAmelCase_	,    name=lowerCAmelCase_	,    session=lowerCAmelCase_ )
         tf.keras.backend.set_value(lowerCAmelCase_	,    lowerCAmelCase_ )
         UpperCAmelCase      					=  session.run(lowerCAmelCase_ )
         print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_	,    lowerCAmelCase_ )}""" )

      UpperCAmelCase      					=  tf.train.Saver(tf.trainable_variables() )
      saver.save(lowerCAmelCase_	,    os.path.join(lowerCAmelCase_	,    model_name.replace("""-"""	,    """_""" ) + """.ckpt""" ) )
def 	_UpperCamelCase					(    lowerCAmelCase_=None )    ->Union[str, Any]:
   UpperCAmelCase      					=  argparse.ArgumentParser()
   parser.add_argument("""--model_name"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""model name e.g. bert-base-uncased""" )
   parser.add_argument(
       """--cache_dir"""	,    type=lowerCAmelCase_	,    default=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory containing pytorch model""" )
   parser.add_argument("""--pytorch_model_path"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""/path/to/<pytorch-model-name>.bin""" )
   parser.add_argument("""--tf_cache_dir"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory in which to save tensorflow model""" )
   UpperCAmelCase      					=  parser.parse_args(lowerCAmelCase_ )

   UpperCAmelCase      					=  BertModel.from_pretrained(
       pretrained_model_name_or_path=args.model_name	,    state_dict=torch.load(args.pytorch_model_path )	,    cache_dir=args.cache_dir	,    )

   convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase_	,    ckpt_dir=args.tf_cache_dir	,    model_name=args.model_name )


if __name__ == "__main__":
 main()



