


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


SCREAMING_SNAKE_CASE			: str				   =					os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"{bindir}/../../examples/pytorch/translation"):
 from run_translation import main  # noqa


set_seed(42)
SCREAMING_SNAKE_CASE			: int				   =					"sshleifer/student_marian_en_ro_6_1"
SCREAMING_SNAKE_CASE			: str				   =					"sshleifer/tiny-mbart"






@require_torch
class _lowerCamelCase(		_a  ):
       def      UpperCamelCase	(   self, lowerCamelCase=False, lowerCamelCase=None, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=True, )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.run_trainer(
               eval_steps=1, max_len=12, model_name=lowerCamelCase, num_train_epochs=1, distributed=lowerCamelCase, extra_args_str=lowerCamelCase, predict_with_generate=lowerCamelCase, do_train=lowerCamelCase, do_eval=lowerCamelCase, do_predict=lowerCamelCase, )
           _lowercase			:				List[str] 						=						TrainerState.load_from_json(os.path.join(lowerCamelCase, 'trainer_state.json')).log_history

           if not do_eval:
               return

           _lowercase			:				Dict 						=						[log for log in logs if 'eval_loss' in log.keys()]

           _lowercase			:				List[str] 						=						eval_metrics[0]
           if predict_with_generate:
               assert "eval_bleu" in first_step_stats

               _lowercase			:				Optional[int] 						=						eval_metrics[-1]
               assert isinstance(last_step_stats['eval_bleu'], lowerCamelCase)
               assert not math.isnan(float(last_step_stats['eval_loss'])), "eval_loss must not be `nan`"
       @require_torch_non_multi_gpu
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           self.run_seqaseq_quick()
       @require_torch_multi_gpu
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           self.run_seqaseq_quick(distributed=lowerCamelCase)
       @require_torch_multi_gpu
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           self.run_seqaseq_quick(distributed=lowerCamelCase)
       @unittest.skip('Requires an update of the env running those tests')
       @require_torch_multi_gpu
       @require_fairscale
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           self.run_seqaseq_quick(distributed=lowerCamelCase, extra_args_str='--sharded_ddp simple')
       @unittest.skip('Requires an update of the env running those tests')
       @require_torch_multi_gpu
       @require_fairscale
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           self.run_seqaseq_quick(distributed=lowerCamelCase, 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      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           self.run_seqaseq_quick(distributed=lowerCamelCase, extra_args_str='--sharded_ddp zero_dp_2', predict_with_generate=lowerCamelCase)
       @unittest.skip('Requires an update of the env running those tests')
       @require_torch_multi_gpu
       @require_fairscale
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           self.run_seqaseq_quick(
               distributed=lowerCamelCase, extra_args_str='--sharded_ddp zero_dp_2 --fp16', predict_with_generate=lowerCamelCase)
       @require_apex
       @require_torch_gpu
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           self.run_seqaseq_quick(distributed=lowerCamelCase, 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=lowerCamelCase, extra_args_str='--fp16 --fp16_backend=apex')
       @parameterized.expand(['base', 'low', 'high', 'mixed'])
       @require_torch_multi_gpu
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Any 						=						{
               # 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},
           }

           _lowercase			:				Tuple 						=						experiments[experiment_id]
           _lowercase			:				List[str] 						=						{'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
           _lowercase			:				List[Any] 						=						'Running training'
           with CaptureStderr() as cl:
               self.run_seqaseq_quick(**lowerCamelCase, extra_args_str=data['extra_args_str'])
           _lowercase			:				List[str] 						=						len(re.findall(lowerCamelCase, cl.err))
           self.assertEqual(lowerCamelCase, data['n_matches'])
       @slow
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.run_trainer(
               eval_steps=2, max_len=1_28, model_name=lowerCamelCase, learning_rate=3E-4, num_train_epochs=10, distributed=lowerCamelCase, )

           # Check metrics
           _lowercase			:				Optional[Any] 						=						TrainerState.load_from_json(os.path.join(lowerCamelCase, 'trainer_state.json')).log_history
           _lowercase			:				Any 						=						[log for log in logs if 'eval_loss' in log.keys()]
           _lowercase			:				Union[str, Any] 						=						eval_metrics[0]
           _lowercase			:				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'], lowerCamelCase)

           # test if do_predict saves generations and metrics
           _lowercase			:				List[Any] 						=						os.listdir(lowerCamelCase)
           _lowercase			:				str 						=						{os.path.basename(lowerCamelCase) for p in contents}
           assert "generated_predictions.txt" in contents
           assert "predict_results.json" in contents
       @slow
       @require_bitsandbytes
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(lowerCamelCase) -> Tuple[int, float]:
               _lowercase			:				Optional[Any] 						=						'--skip_memory_metrics 0'

               _lowercase			:				Any 						=						self.run_trainer(
                   max_len=1_28, model_name=lowerCamelCase, learning_rate=3E-4, num_train_epochs=1, optim=lowerCamelCase, distributed=lowerCamelCase, extra_args_str=lowerCamelCase, do_eval=lowerCamelCase, do_predict=lowerCamelCase, n_gpus_to_use=1, )

               # Check metrics
               _lowercase			:				int 						=						TrainerState.load_from_json(Path(lowerCamelCase, 'trainer_state.json')).log_history
               _lowercase			:				str 						=						int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20)
               _lowercase			:				Optional[int] 						=						int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20)

               _lowercase			:				int 						=						logs[0]['train_loss']
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

           _lowercase			:				Union[str, Any] 						=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           _lowercase			:				Any 						=						gpu_peak_mem_orig + gpu_alloc_mem_orig
           _lowercase			:				Dict 						=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           _lowercase			:				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
           _lowercase			:				List[str] 						=						1_20

           # 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(
               lowerCamelCase, lowerCamelCase, '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(
               lowerCamelCase, lowerCamelCase, '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(
               lowerCamelCase, lowerCamelCase, F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''')




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = 3E-3, lowerCamelCase = "adafactor", lowerCamelCase = False, lowerCamelCase = None, lowerCamelCase = 0, lowerCamelCase = True, lowerCamelCase = True, lowerCamelCase = True, lowerCamelCase = True, lowerCamelCase = None, )			->  Any:




           """simple docstring"""

           _lowercase			:				str 						=						self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
           _lowercase			:				int 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				List[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(lowerCamelCase)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCamelCase)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

           _lowercase			:				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(lowerCamelCase)}
        '''.split()

           _lowercase			:				Dict 						=						'\n            --do_predict\n        '.split()

           _lowercase			:				Optional[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:
                   _lowercase			:				Any 						=						get_gpu_count()
               _lowercase			:				List[Any] 						=						get_torch_dist_unique_port()
               _lowercase			:				str 						=						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()
               _lowercase			:				List[Any] 						=						[sys.executable] + distributed_args + args
               # keep for quick debug
               # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
               execute_subprocess_async(lowerCamelCase, env=self.get_env())
           else:
               _lowercase			:				int 						=						['run_translation.py'] + args
               with patch.object(lowerCamelCase, 'argv', lowerCamelCase):
                   main()

           return output_dir






from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					1.0_5457_1817E-34  # unit of ℏ : J * s

SCREAMING_SNAKE_CASE			: int				   =					3E8  # unit of c : m * s^-1



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		dict[str, float]:

    if (force, area, distance).count(0					) != 1:
        raise ValueError('One and only one argument must be 0'					)
    if force < 0:
        raise ValueError('Magnitude of force can not be negative'					)
    if distance < 0:
        raise ValueError('Distance can not be negative'					)
    if area < 0:
        raise ValueError('Area can not be negative'					)
    if force == 0:
        _lowercase			:				int 						=						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
            240 * (distance) ** 4
        )
        return {"force": force}
    elif area == 0:
        _lowercase			:				List[Any] 						=						(240 * force * (distance) ** 4) / (
            REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
        )
        return {"area": area}
    elif distance == 0:
        _lowercase			:				List[Any] 						=						(
            (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
        ) ** (1 / 4)
        return {"distance": distance}
    raise ValueError('One and only one argument must be 0'					)


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()


