



'''simple docstring'''





def lowerCAmelCase  ():



     """simple docstring"""



     return [list(range(1_000 - i  ,	-1_000 - i  ,	-1)) for i in range(1_000)]


lowercase_		       =  generate_large_matrix()
lowercase_		       =  (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert all(row == sorted(__lowercase  ,	reverse=__lowercase) for row in grid)
     assert all(list(__lowercase) == sorted(__lowercase  ,	reverse=__lowercase) for col in zip(*__lowercase))




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  0
     _a      =  len(__lowercase) - 1

     # Edge cases such as no values or all numbers are negative.
     if not array or array[0] < 0:
          return 0

     while right + 1 > left:
          _a      =  (left + right) // 2
          _a      =  array[mid]

          # Num must be negative and the index must be greater than or equal to 0.
          if num < 0 and array[mid - 1] >= 0:
               return mid

          if num >= 0:
               _a      =  mid + 1
          else:
               _a      =  mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
     return len(__lowercase)




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  0
     _a      =  len(grid[0])

     for i in range(len(__lowercase)):
          _a      =  find_negative_index(grid[i][:bound])
          total += bound
     return (len(__lowercase) * len(grid[0])) - total




def lowerCAmelCase  (__A):



     """simple docstring"""



     return len([number for row in grid for number in row if number < 0])




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  0
     for row in grid:
          for i, number in enumerate(__lowercase):
               if number < 0:
                    total += len(__lowercase) - i
                    break
     return total




def lowerCAmelCase  ():



     """simple docstring"""



     from timeit import timeit

     print('''Running benchmarks''')
     _a      =  (
         '''from __main__ import count_negatives_binary_search, '''
         '''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
     )
     for func in (
         "count_negatives_binary_search",  # took 0.7727 seconds
         "count_negatives_brute_force_with_break",  # took 4.6505 seconds
         "count_negatives_brute_force",  # took 12.8160 seconds
     ):
          _a      =  timeit(F'''{func}(grid=grid)'''  ,	setup=__lowercase  ,	number=500)
          print(F'''{func}() took {time:0.4f} seconds''')


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()










'''simple docstring'''




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   TapasModel(config=__lowercase       )
     else:
          raise ValueError(f'Task {task} not supported.'       )

     print(f'Building PyTorch model from configuration: {config}'       )
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(__lowercase			,     __lowercase			,     __lowercase       )

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     model.save_pretrained(__lowercase       )

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'       )
     _UpperCAmelCase     							=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,     model_max_length=512       )
     tokenizer.save_pretrained(__lowercase       )

     print("Used relative position embeddings:"			,     model.config.reset_position_index_per_cell       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
  )
  parser.add_argument(
      '''--reset_position_index_per_cell''',
      default=False,
      action='''store_true''',
      help='''Whether to use relative position embeddings or not. Defaults to True.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--tapas_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained TAPAS 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.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )




