





"""simple docstring"""




import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


_UpperCAmelCase             =							'sshleifer/mar_enro_6_3_student'






class        snake_case_    (		_a						):






  def 				UpperCAmelCase__						(							self			:					int      )->Optional[int]:






   '''simple docstring'''

   super().setUp()

   __lowerCAmelCase					:	Tuple      =       cached_path(
       """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""       ,  extract_compressed_file=__lowerCAmelCase       ,  )
   __lowerCAmelCase					:	Tuple      =       F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






  @slow
  @require_torch_gpu
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Tuple:






   '''simple docstring'''

   MarianMTModel.from_pretrained(__lowerCAmelCase      )






  @slow
  @require_torch_gpu
  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       {
       """$MAX_LEN""": 64,
       """$BS""": 64,
       """$GAS""": 1,
       """$ENRO_DIR""": self.data_dir,
       """facebook/mbart-large-cc25""": MARIAN_MODEL,
       # "val_check_interval=0.25": "val_check_interval=1.0",
       """--learning_rate=3e-5""": """--learning_rate 3e-4""",
       """--num_train_epochs 6""": """--num_train_epochs 1""",
   }

   # Clean up bash script
   __lowerCAmelCase					:	Any      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""      )[1].strip()
   __lowerCAmelCase					:	List[str]      =       bash_script.replace("""\\\n"""       ,  """"""      ).strip().replace("""\"$@\""""       ,  """"""      )
   for k, v in env_vars_to_replace.items():
    __lowerCAmelCase					:	int      =       bash_script.replace(__lowerCAmelCase       ,  str(__lowerCAmelCase      )      )
   __lowerCAmelCase					:	Union[str, Any]      =       self.get_auto_remove_tmp_dir()

   # bash_script = bash_script.replace("--fp16 ", "")
   __lowerCAmelCase					:	Optional[Any]      =       F'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
   # XXX: args.gpus > 1 : handle multi_gpu in the future

   __lowerCAmelCase					:	List[Any]      =       ["""finetune.py"""] + bash_script.split() + args
   with patch.object(__lowerCAmelCase       ,  """argv"""       ,  __lowerCAmelCase      ):
    __lowerCAmelCase					:	Optional[Any]      =       argparse.ArgumentParser()
    __lowerCAmelCase					:	str      =       pl.Trainer.add_argparse_args(__lowerCAmelCase      )
    __lowerCAmelCase					:	Optional[int]      =       SummarizationModule.add_model_specific_args(__lowerCAmelCase       ,  os.getcwd()      )
    __lowerCAmelCase					:	int      =       parser.parse_args()
    __lowerCAmelCase					:	Tuple      =       main(__lowerCAmelCase      )

   # Check metrics
   __lowerCAmelCase					:	int      =       load_json(model.metrics_save_path      )
   __lowerCAmelCase					:	Union[str, Any]      =       metrics["""val"""][0]
   __lowerCAmelCase					:	List[str]      =       metrics["""val"""][-1]
   self.assertEqual(len(metrics["""val"""]      )       ,  (args.max_epochs / args.val_check_interval)      )
   assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']       ,  __lowerCAmelCase      )

   self.assertGreater(last_step_stats["""val_avg_gen_time"""]       ,  0.01      )
   # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
   self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]       ,  1.0      )

   # test learning requirements:

   # 1. BLEU improves over the course of training by more than 2 pts
   self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]       ,  2      )

   # 2. BLEU finishes above 17
   self.assertGreater(last_step_stats["""val_avg_bleu"""]       ,  17      )

   # 3. test BLEU and val BLEU within ~1.1 pt.
   self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]      )       ,  1.1      )

   # check lightning ckpt can be loaded and has a reasonable statedict
   __lowerCAmelCase					:	Union[str, Any]      =       os.listdir(__lowerCAmelCase      )
   __lowerCAmelCase					:	Union[str, Any]      =       [x for x in contents if x.endswith(""".ckpt"""      )][0]
   __lowerCAmelCase					:	Optional[Any]      =       os.path.join(args.output_dir       ,  __lowerCAmelCase      )
   __lowerCAmelCase					:	Optional[int]      =       torch.load(__lowerCAmelCase       ,  map_location="""cpu"""      )
   __lowerCAmelCase					:	Union[str, Any]      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
   assert expected_key in ckpt["state_dict"]
   assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

   # TODO: turn on args.do_predict when PL bug fixed.
   if args.do_predict:
    __lowerCAmelCase					:	Tuple      =       {os.path.basename(__lowerCAmelCase      ) for p in contents}
    assert "test_generations.txt" in contents
    assert "test_results.txt" in contents
    # assert len(metrics["val"]) ==  desired_n_evals
    assert len(metrics["""test"""]      ) == 1






class        snake_case_    (		_a						):






  @timeout_decorator.timeout(600      )
  @slow
  @require_torch_gpu
  def 				UpperCAmelCase__						(							self			:					Any      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
   __lowerCAmelCase					:	Dict      =       {
       """--fp16_opt_level=O1""": """""",
       """$MAX_LEN""": 128,
       """$BS""": 16,
       """$GAS""": 1,
       """$ENRO_DIR""": data_dir,
       """$m""": """sshleifer/student_marian_en_ro_6_1""",
       """val_check_interval=0.25""": """val_check_interval=1.0""",
   }

   # Clean up bash script
   __lowerCAmelCase					:	str      =       (
       (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""      )[1].strip()
   )
   __lowerCAmelCase					:	Union[str, Any]      =       bash_script.replace("""\\\n"""       ,  """"""      ).strip().replace("""\"$@\""""       ,  """"""      )
   __lowerCAmelCase					:	Union[str, Any]      =       bash_script.replace("""--fp16 """       ,  """ """      )

   for k, v in env_vars_to_replace.items():
    __lowerCAmelCase					:	List[str]      =       bash_script.replace(__lowerCAmelCase       ,  str(__lowerCAmelCase      )      )
   __lowerCAmelCase					:	Union[str, Any]      =       self.get_auto_remove_tmp_dir()
   __lowerCAmelCase					:	Optional[Any]      =       bash_script.replace("""--fp16"""       ,  """"""      )
   __lowerCAmelCase					:	Optional[Any]      =       6
   __lowerCAmelCase					:	Any      =       (
       ["""distillation.py"""]
       + bash_script.split()
       + [
           F'''--output_dir={output_dir}''',
           """--gpus=1""",
           """--learning_rate=1e-3""",
           F'''--num_train_epochs={epochs}''',
           """--warmup_steps=10""",
           """--val_check_interval=1.0""",
           """--do_predict""",
       ]
   )
   with patch.object(__lowerCAmelCase       ,  """argv"""       ,  __lowerCAmelCase      ):
    __lowerCAmelCase					:	Any      =       argparse.ArgumentParser()
    __lowerCAmelCase					:	List[str]      =       pl.Trainer.add_argparse_args(__lowerCAmelCase      )
    __lowerCAmelCase					:	List[Any]      =       SummarizationDistiller.add_model_specific_args(__lowerCAmelCase       ,  os.getcwd()      )
    __lowerCAmelCase					:	List[str]      =       parser.parse_args()
    # assert args.gpus == gpus THIS BREAKS for multi_gpu

    __lowerCAmelCase					:	List[Any]      =       distill_main(__lowerCAmelCase      )

   # Check metrics
   __lowerCAmelCase					:	Optional[Any]      =       load_json(model.metrics_save_path      )
   __lowerCAmelCase					:	str      =       metrics["""val"""][0]
   __lowerCAmelCase					:	str      =       metrics["""val"""][-1]
   assert len(metrics["""val"""]      ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

   assert last_step_stats["val_avg_gen_time"] >= 0.01

   assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
   assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
   assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']       ,  __lowerCAmelCase      )

   # check lightning ckpt can be loaded and has a reasonable statedict
   __lowerCAmelCase					:	List[Any]      =       os.listdir(__lowerCAmelCase      )
   __lowerCAmelCase					:	int      =       [x for x in contents if x.endswith(""".ckpt"""      )][0]
   __lowerCAmelCase					:	Tuple      =       os.path.join(args.output_dir       ,  __lowerCAmelCase      )
   __lowerCAmelCase					:	List[str]      =       torch.load(__lowerCAmelCase       ,  map_location="""cpu"""      )
   __lowerCAmelCase					:	Any      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
   assert expected_key in ckpt["state_dict"]
   assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

   # TODO: turn on args.do_predict when PL bug fixed.
   if args.do_predict:
    __lowerCAmelCase					:	int      =       {os.path.basename(__lowerCAmelCase      ) for p in contents}
    assert "test_generations.txt" in contents
    assert "test_results.txt" in contents
    # assert len(metrics["val"]) ==  desired_n_evals
    assert len(metrics["""test"""]      ) == 1






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()