





def 	_SCREAMING_SNAKE_CASE		(      )      ->   list[list[int]]:
 return [list(range(1_000 - i   ,   -1_000 - i   ,   -1   )   ) for i in range(1_000   )]


_UpperCAmelCase             =							generate_large_matrix()
_UpperCAmelCase             =							(
    [[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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   None:
 assert all(row == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for row in grid   )
 assert all(list(SCREAMING_SNAKE_CASE   ) == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for col in zip(*SCREAMING_SNAKE_CASE   )   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[int]   )      ->   int:
 __lowerCAmelCase					:	Optional[int]      =       0
 __lowerCAmelCase					:	Tuple      =       len(SCREAMING_SNAKE_CASE   ) - 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:
  __lowerCAmelCase					:	Any      =       (left + right) // 2
  __lowerCAmelCase					:	Optional[int]      =       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:
   __lowerCAmelCase					:	Dict      =       mid + 1
  else:
   __lowerCAmelCase					:	str      =       mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
 return len(SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	Any      =       0
 __lowerCAmelCase					:	Union[str, Any]      =       len(grid[0]   )

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 return len([number for row in grid for number in row if number < 0]   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	List[str]      =       0
 for row in grid:
  for i, number in enumerate(SCREAMING_SNAKE_CASE   ):
   if number < 0:
    total += len(SCREAMING_SNAKE_CASE   ) - i
    break
 return total






def 	_SCREAMING_SNAKE_CASE		(      )      ->   None:
 from timeit import timeit

 print("""Running benchmarks"""   )
 __lowerCAmelCase					:	Tuple      =       (
     """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
 ):
  __lowerCAmelCase					:	Optional[int]      =       timeit(F'''{func}(grid=grid)'''   ,   setup=SCREAMING_SNAKE_CASE   ,   number=500   )
  print(F'''{func}() took {time:0.4f} seconds'''   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class        snake_case_    (		__lowercase						):
  A_      							=   'gptj'
  A_      							=   {
      'max_position_embeddings': 'n_positions',
      'hidden_size': 'n_embd',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }






  def __init__(							self			:					Tuple       ,  _snake_case			:					Optional[int]=50400       ,  _snake_case			:					Union[str, Any]=2048       ,  _snake_case			:					Tuple=4096       ,  _snake_case			:					Optional[int]=28       ,  _snake_case			:					Tuple=16       ,  _snake_case			:					Optional[Any]=64       ,  _snake_case			:					Optional[int]=None       ,  _snake_case			:					str="gelu_new"       ,  _snake_case			:					str=0.0       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					List[str]=0.0       ,  _snake_case			:					Tuple=1E-5       ,  _snake_case			:					List[str]=0.02       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[Any]=50256       ,  _snake_case			:					List[str]=50256       ,  _snake_case			:					str=False       ,  **_snake_case			:					Tuple       ,  )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       vocab_size
   __lowerCAmelCase					:	Any      =       n_positions
   __lowerCAmelCase					:	Optional[Any]      =       n_embd
   __lowerCAmelCase					:	Optional[int]      =       n_layer
   __lowerCAmelCase					:	Optional[int]      =       n_head
   __lowerCAmelCase					:	List[Any]      =       n_inner
   __lowerCAmelCase					:	List[str]      =       rotary_dim
   __lowerCAmelCase					:	int      =       activation_function
   __lowerCAmelCase					:	str      =       resid_pdrop
   __lowerCAmelCase					:	Union[str, Any]      =       embd_pdrop
   __lowerCAmelCase					:	Dict      =       attn_pdrop
   __lowerCAmelCase					:	Optional[Any]      =       layer_norm_epsilon
   __lowerCAmelCase					:	Optional[Any]      =       initializer_range
   __lowerCAmelCase					:	Dict      =       use_cache

   __lowerCAmelCase					:	List[str]      =       bos_token_id
   __lowerCAmelCase					:	List[Any]      =       eos_token_id

   super().__init__(
       bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  tie_word_embeddings=_snake_case       ,  **_snake_case      )






class        snake_case_    (		__lowercase						):






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					PretrainedConfig       ,  _snake_case			:					str = "default"       ,  _snake_case			:					List[PatchingSpec] = None       ,  _snake_case			:					bool = False       ,  )->str:






   '''simple docstring'''

   super().__init__(_snake_case       ,  task=_snake_case       ,  patching_specs=_snake_case       ,  use_past=_snake_case      )
   if not getattr(self._config       ,  """pad_token_id"""       ,  _snake_case      ):
    # TODO: how to do that better?
    __lowerCAmelCase					:	Dict      =       0






  @property
  def 				UpperCAmelCase__						(							self			:					Tuple      )->Mapping[str, Mapping[int, str]]:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
   if self.use_past:
    self.fill_with_past_key_values_(_snake_case       ,  direction="""inputs"""      )
    __lowerCAmelCase					:	Dict      =       {0: """batch""", 1: """past_sequence + sequence"""}
   else:
    __lowerCAmelCase					:	Optional[Any]      =       {0: """batch""", 1: """sequence"""}

   return common_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->int:






   '''simple docstring'''

   return self._config.n_layer






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return self._config.n_head






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					PreTrainedTokenizer       ,  _snake_case			:					int = -1       ,  _snake_case			:					int = -1       ,  _snake_case			:					bool = False       ,  _snake_case			:					Optional[TensorType] = None       ,  )->Mapping[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       super(_snake_case       ,  self      ).generate_dummy_inputs(
       _snake_case       ,  batch_size=_snake_case       ,  seq_length=_snake_case       ,  is_pair=_snake_case       ,  framework=_snake_case      )

   # We need to order the input in the way they appears in the forward()
   __lowerCAmelCase					:	Union[str, Any]      =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

   # Need to add the past_keys
   if self.use_past:
    if not is_torch_available():
     raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
    else:
     import torch

     __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     __lowerCAmelCase					:	Optional[Any]      =       seqlen + 2
     __lowerCAmelCase					:	Dict      =       (
         batch,
         self.num_attention_heads,
         past_key_values_length,
         self._config.hidden_size // self.num_attention_heads,
     )
     __lowerCAmelCase					:	Tuple      =       [
         (torch.zeros(_snake_case      ), torch.zeros(_snake_case      )) for _ in range(self.num_layers      )
     ]

   __lowerCAmelCase					:	int      =       common_inputs["""attention_mask"""]
   if self.use_past:
    __lowerCAmelCase					:	Union[str, Any]      =       ordered_inputs["""attention_mask"""].dtype
    __lowerCAmelCase					:	List[str]      =       torch.cat(
        [ordered_inputs["""attention_mask"""], torch.ones(_snake_case       ,  _snake_case       ,  dtype=_snake_case      )]       ,  dim=1      )

   return ordered_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return 13