
'''simple docstring'''

import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


snake_case									=    os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'{bindir}/../../examples/pytorch/translation'):
     from run_translation import main  # noqa


set_seed(42)
snake_case									=    """sshleifer/student_marian_en_ro_6_1"""
snake_case									=    """sshleifer/tiny-mbart"""




@require_torch
class    lowerCAmelCase       (     UpperCamelCase_  ):


 def    _A	(    self  : Union[str, Any]			,							a__  : Optional[int]=False			,							a__  : List[Any]=None			,							a__  : Tuple=True			,							a__  : Any=True			,							a__  : Optional[int]=True			,							a__  : Dict=True			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	self.run_trainer(
      eval_steps=1			,							max_len=12			,							model_name=a__			,							num_train_epochs=1			,							distributed=a__			,							extra_args_str=a__			,							predict_with_generate=a__			,							do_train=a__			,							do_eval=a__			,							do_predict=a__			,							)
  lowerCAmelCase__     :   Tuple     		=	TrainerState.load_from_json(os.path.join(a__			,							"trainer_state.json"    )    ).log_history

  if not do_eval:
   return

  lowerCAmelCase__     :   Optional[Any]     		=	[log for log in logs if "eval_loss" in log.keys()]

  lowerCAmelCase__     :   Tuple     		=	eval_metrics[0]
  if predict_with_generate:
   assert "eval_bleu" in first_step_stats

   lowerCAmelCase__     :   Any     		=	eval_metrics[-1]
   assert isinstance(last_step_stats["eval_bleu"]			,							a__    )
   assert not math.isnan(float(last_step_stats["eval_loss"]    )    ), "eval_loss must not be `nan`"


 @require_torch_non_multi_gpu
 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  self.run_seqaseq_quick()


 @require_torch_multi_gpu
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.run_seqaseq_quick(distributed=a__    )


 @require_torch_multi_gpu
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  self.run_seqaseq_quick(distributed=a__    )


 @unittest.skip("Requires an update of the env running those tests"    )
 @require_torch_multi_gpu
 @require_fairscale
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.run_seqaseq_quick(distributed=a__			,							extra_args_str="--sharded_ddp simple"    )


 @unittest.skip("Requires an update of the env running those tests"    )
 @require_torch_multi_gpu
 @require_fairscale
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.run_seqaseq_quick(distributed=a__			,							extra_args_str="--sharded_ddp simple --fp16"    )


 @unittest.skip("Requires an update of the env running those tests"    )
 @require_torch_multi_gpu
 @require_fairscale
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  self.run_seqaseq_quick(distributed=a__			,							extra_args_str="--sharded_ddp zero_dp_2"			,							predict_with_generate=a__    )


 @unittest.skip("Requires an update of the env running those tests"    )
 @require_torch_multi_gpu
 @require_fairscale
 def    _A	(    self  : str    ):
  '''simple docstring'''

  self.run_seqaseq_quick(
      distributed=a__			,							extra_args_str="--sharded_ddp zero_dp_2 --fp16"			,							predict_with_generate=a__    )


 @require_apex
 @require_torch_gpu
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  self.run_seqaseq_quick(distributed=a__			,							extra_args_str="--fp16 --fp16_backend=apex"    )
  # test 2nd time - was getting eval_loss': nan'
  # to reproduce the problem set distributed=False
  self.run_seqaseq_quick(distributed=a__			,							extra_args_str="--fp16 --fp16_backend=apex"    )


 @parameterized.expand(["base", "low", "high", "mixed"]    )
 @require_torch_multi_gpu
 def    _A	(    self  : str			,							a__  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	{
      # test with the default log_level - should be info and thus log info once
      "base": {"extra_args_str": "", "n_matches": 1},
      # test with low log_level and log_level_replica - should be noisy on all processes
      # now the info string should appear twice on 2 processes
      "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
      # test with high log_level and low log_level_replica
      # now the info string should appear once only on the replica
      "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
      # test with high log_level and log_level_replica - should be quiet on all processes
      "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
  }

  lowerCAmelCase__     :   List[str]     		=	experiments[experiment_id]
  lowerCAmelCase__     :   str     		=	{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
  lowerCAmelCase__     :   str     		=	"Running training"
  with CaptureStderr() as cl:
   self.run_seqaseq_quick(**a__			,							extra_args_str=data["extra_args_str"]    )
  lowerCAmelCase__     :   Optional[int]     		=	len(re.findall(a__			,							cl.err    )    )
  self.assertEqual(a__			,							data["n_matches"]    )


 @slow
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	self.run_trainer(
      eval_steps=2			,							max_len=128			,							model_name=a__			,							learning_rate=3e-4			,							num_train_epochs=10			,							distributed=a__			,							)

  # Check metrics
  lowerCAmelCase__     :   Union[str, Any]     		=	TrainerState.load_from_json(os.path.join(a__			,							"trainer_state.json"    )    ).log_history
  lowerCAmelCase__     :   List[str]     		=	[log for log in logs if "eval_loss" in log.keys()]
  lowerCAmelCase__     :   int     		=	eval_metrics[0]
  lowerCAmelCase__     :   int     		=	eval_metrics[-1]

  assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
  assert isinstance(last_step_stats["eval_bleu"]			,							a__    )

  # test if do_predict saves generations and metrics
  lowerCAmelCase__     :   List[str]     		=	os.listdir(a__    )
  lowerCAmelCase__     :   Any     		=	{os.path.basename(a__    ) for p in contents}
  assert "generated_predictions.txt" in contents
  assert "predict_results.json" in contents


 @slow
 @require_bitsandbytes
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  from transformers.training_args import OptimizerNames

  def train_and_return_metrics(a__  : str    ) -> Tuple[int, float]:
   lowerCAmelCase__     :   List[str]     		=	"--skip_memory_metrics 0"

   lowerCAmelCase__     :   Union[str, Any]     		=	self.run_trainer(
       max_len=128			,							model_name=a__			,							learning_rate=3e-4			,							num_train_epochs=1			,							optim=a__			,							distributed=a__			,							extra_args_str=a__			,							do_eval=a__			,							do_predict=a__			,							n_gpus_to_use=1			,							)

   # Check metrics
   lowerCAmelCase__     :   List[str]     		=	TrainerState.load_from_json(Path(a__			,							"trainer_state.json"    )    ).log_history
   lowerCAmelCase__     :   str     		=	int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20    )
   lowerCAmelCase__     :   int     		=	int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20    )

   lowerCAmelCase__     :   Tuple     		=	logs[0]["train_loss"]
   return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

  lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value    )
  lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value    )

  lowerCAmelCase__     :   List[Any]     		=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

  lowerCAmelCase__     :   List[str]     		=	gpu_peak_mem_orig + gpu_alloc_mem_orig
  lowerCAmelCase__     :   Union[str, Any]     		=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
  lowerCAmelCase__     :   Tuple     		=	gpu_total_mem_orig - gpu_total_mem_bnb

  # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
  # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
  # in 2 bytes and the diff in optim memory usage is derived as so:
  #
  # - normal 25*8=~200MB (8 bytes per param)
  # - bnb    25*2= ~50MB (2 bytes per param)
  #
  # Thus we should expect ~150MB total memory saved.
  #
  # Peak memory should be the same - the total should be different by about that same margin
  #
  # After leaving a small margin to accommodate for differences between gpus let's check
  # that we have at least 120MB in savings
  lowerCAmelCase__     :   str     		=	120

  # uncomment the following if this test starts failing - requires py38 for a new print feature
  # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
  # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
  # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
  # print(f"{gpu_alloc_mem_diff=}MB")
  # print(f"{gpu_peak_mem_diff=}MB")
  # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
  # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

  self.assertGreater(
      a__			,							a__			,							"should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
      F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
      F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''			,							)

  self.assertGreater(
      a__			,							a__			,							"should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
      F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
      F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''			,							)

  self.assertEqual(
      a__			,							a__			,							F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''    )


 def    _A	(    self  : Any			,							a__  : int			,							a__  : str			,							a__  : int			,							a__  : float = 3e-3			,							a__  : str = "adafactor"			,							a__  : bool = False			,							a__  : str = None			,							a__  : int = 0			,							a__  : bool = True			,							a__  : bool = True			,							a__  : bool = True			,							a__  : bool = True			,							a__  : int = None			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
  lowerCAmelCase__     :   Optional[int]     		=	self.get_auto_remove_tmp_dir()
  lowerCAmelCase__     :   str     		=	F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(a__    )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(a__    )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

  lowerCAmelCase__     :   List[str]     		=	F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(a__    )}
        '''.split()

  lowerCAmelCase__     :   Dict     		=	"\n            --do_predict\n        ".split()

  lowerCAmelCase__     :   int     		=	[]
  if do_train:
   args += args_train

  if do_eval:
   args += args_eval

  if do_predict:
   args += args_predict

  if predict_with_generate:
   args += "--predict_with_generate".split()

  if do_train:
   if optim == "adafactor":
    args += "--adafactor".split()
   else:
    args += F'''--optim {optim}'''.split()

  if extra_args_str is not None:
   args += extra_args_str.split()

  if distributed:
   if n_gpus_to_use is None:
    lowerCAmelCase__     :   List[str]     		=	get_gpu_count()
   lowerCAmelCase__     :   str     		=	get_torch_dist_unique_port()
   lowerCAmelCase__     :   Optional[Any]     		=	F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
   lowerCAmelCase__     :   Dict     		=	[sys.executable] + distributed_args + args
   # keep for quick debug
   # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
   execute_subprocess_async(a__			,							env=self.get_env()    )
  else:
   lowerCAmelCase__     :   Optional[Any]     		=	["run_translation.py"] + args
   with patch.object(a__			,							"argv"			,							a__    ):
    main()

  return output_dir







'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	img.shape[0], img.shape[1]

 # converting each pixel's color to its negative
 for i in range(lowerCamelCase_			):
  for j in range(lowerCamelCase_			):
   lowerCAmelCase__     :   Optional[int]     		=	[2_5_5, 2_5_5, 2_5_5] - img[i][j]

 return img


if __name__ == "__main__":
     # read original image
     snake_case									=    imread("""image_data/lena.jpg""", 1)

     # convert to its negative
     snake_case									=    convert_to_negative(img)

     # show result image
     imshow("""negative of original image""", img)
     waitKey(0)
     destroyAllWindows()





