

"""simple docstring"""




import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					int   				=   json.load(open(__UpperCamelCase							,      """r""" ) )
 UpperCAmelCase__   :					Union[str, Any]   				=   GPTNeoConfig(
     hidden_size=config_json["""n_embd"""]							,      num_layers=config_json["""n_layer"""]							,      num_heads=config_json["""n_head"""]							,      attention_types=config_json["""attention_types"""]							,      max_position_embeddings=config_json["""n_positions"""]							,      resid_dropout=config_json["""res_dropout"""]							,      embed_dropout=config_json["""embed_dropout"""]							,      attention_dropout=config_json["""attn_dropout"""]							,      )
 print(F"Building PyTorch model from configuration: {config}" )
 UpperCAmelCase__   :					Union[str, Any]   				=   GPTNeoForCausalLM(__UpperCamelCase )

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

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


if __name__ == "__main__":
      __UpperCAmelCase	=			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 mesh-tf model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      __UpperCAmelCase	=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








"""simple docstring"""




from __future__ import annotations
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__				, UpperCAmelCase__   :					Optional[Any]   				=   m - 1, 0
 while i >= 0 and j < n:
  if key == mat[i][j]:
   print(F"Key {key} found at row- {i + 1} column- {j + 1}" )
   return
  if key < mat[i][j]:
   i -= 1
  else:
   j += 1
 print(F"Key {key} not found" )
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
 UpperCAmelCase__   :					int   				=   int(input("""Enter the element to be searched:""" ) )
 print(__UpperCamelCase )
 search_in_a_sorted_matrix(__UpperCamelCase							,      len(__UpperCamelCase )							,      len(mat[0] )							,      __UpperCamelCase )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





