





import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers import AutoModelForCausalLM






@require_torch
class        snake_case_    (		unittest.TestCase						):






  def 				UpperCAmelCase__						(							self			:					Dict      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
   __lowerCAmelCase					:	str      =       AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(_snake_case      )
   __lowerCAmelCase					:	str      =       -1

   __lowerCAmelCase					:	Union[str, Any]      =       ids_tensor((1, 5)       ,  vocab_size=model.config.vocab_size      ).to(_snake_case      )
   __lowerCAmelCase					:	str      =       model.generate(_snake_case       ,  max_new_tokens=10       ,  do_sample=_snake_case      )
   __lowerCAmelCase					:	int      =       tokenizer.decode(greedy_ids[0]      )

   with CaptureStdout() as cs:
    __lowerCAmelCase					:	Union[str, Any]      =       TextStreamer(_snake_case      )
    model.generate(_snake_case       ,  max_new_tokens=10       ,  do_sample=_snake_case       ,  streamer=_snake_case      )
   # The greedy text should be printed to stdout, except for the final "\n" in the streamer
   __lowerCAmelCase					:	Dict      =       cs.out[:-1]

   self.assertEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
   __lowerCAmelCase					:	Dict      =       AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(_snake_case      )
   __lowerCAmelCase					:	Tuple      =       -1

   __lowerCAmelCase					:	Tuple      =       ids_tensor((1, 5)       ,  vocab_size=model.config.vocab_size      ).to(_snake_case      )
   __lowerCAmelCase					:	Union[str, Any]      =       model.generate(_snake_case       ,  max_new_tokens=10       ,  do_sample=_snake_case      )
   __lowerCAmelCase					:	Tuple      =       tokenizer.decode(greedy_ids[0]      )

   __lowerCAmelCase					:	Optional[int]      =       TextIteratorStreamer(_snake_case      )
   __lowerCAmelCase					:	Optional[Any]      =       {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
   __lowerCAmelCase					:	Optional[Any]      =       Thread(target=model.generate       ,  kwargs=_snake_case      )
   thread.start()
   __lowerCAmelCase					:	str      =       """"""
   for new_text in streamer:
    streamer_text += new_text

   self.assertEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
   __lowerCAmelCase					:	Optional[int]      =       AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(_snake_case      )
   __lowerCAmelCase					:	List[Any]      =       -1

   __lowerCAmelCase					:	str      =       ids_tensor((1, 5)       ,  vocab_size=model.config.vocab_size      ).to(_snake_case      )
   __lowerCAmelCase					:	List[str]      =       model.generate(_snake_case       ,  max_new_tokens=10       ,  do_sample=_snake_case      )
   __lowerCAmelCase					:	List[str]      =       greedy_ids[:, input_ids.shape[1] :]
   __lowerCAmelCase					:	Optional[int]      =       tokenizer.decode(new_greedy_ids[0]      )

   with CaptureStdout() as cs:
    __lowerCAmelCase					:	str      =       TextStreamer(_snake_case       ,  skip_prompt=_snake_case      )
    model.generate(_snake_case       ,  max_new_tokens=10       ,  do_sample=_snake_case       ,  streamer=_snake_case      )
   # The greedy text should be printed to stdout, except for the final "\n" in the streamer
   __lowerCAmelCase					:	Any      =       cs.out[:-1]

   self.assertEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					str      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       AutoTokenizer.from_pretrained("""distilgpt2"""      )
   __lowerCAmelCase					:	Any      =       AutoModelForCausalLM.from_pretrained("""distilgpt2"""      ).to(_snake_case      )
   __lowerCAmelCase					:	str      =       -1

   __lowerCAmelCase					:	str      =       torch.ones((1, 5)       ,  device=_snake_case      ).long() * model.config.bos_token_id
   with CaptureStdout() as cs:
    __lowerCAmelCase					:	Union[str, Any]      =       TextStreamer(_snake_case       ,  skip_special_tokens=_snake_case      )
    model.generate(_snake_case       ,  max_new_tokens=1       ,  do_sample=_snake_case       ,  streamer=_snake_case      )

   # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
   # re-tokenized, must only contain one token
   __lowerCAmelCase					:	Tuple      =       cs.out[:-1]  # Remove the final "\n"
   __lowerCAmelCase					:	Optional[Any]      =       tokenizer(_snake_case       ,  return_tensors="""pt"""      )
   self.assertEqual(streamer_text_tokenized.input_ids.shape       ,  (1, 1)      )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
   __lowerCAmelCase					:	List[Any]      =       AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(_snake_case      )
   __lowerCAmelCase					:	str      =       -1

   __lowerCAmelCase					:	Tuple      =       ids_tensor((1, 5)       ,  vocab_size=model.config.vocab_size      ).to(_snake_case      )
   __lowerCAmelCase					:	str      =       TextIteratorStreamer(_snake_case       ,  timeout=0.001      )
   __lowerCAmelCase					:	Any      =       {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
   __lowerCAmelCase					:	Optional[int]      =       Thread(target=model.generate       ,  kwargs=_snake_case      )
   thread.start()

   # The streamer will timeout after 0.001 seconds, so an exception will be raised
   with self.assertRaises(_snake_case      ):
    __lowerCAmelCase					:	Optional[int]      =       """"""
    for new_text in streamer:
     streamer_text += new_text






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()