'''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__					:      Any				      =  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__					:      int				      =  '''sshleifer/student_marian_en_ro_6_1'''
snake_case__					:      Tuple				      =  '''sshleifer/tiny-mbart'''






@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def _UpperCamelCase					(			self				,		snake_case_=False				,		snake_case_=None				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.run_trainer(
           eval_steps=1				,		max_len=1_2				,		model_name=snake_case_				,		num_train_epochs=1				,		distributed=snake_case_				,		extra_args_str=snake_case_				,		predict_with_generate=snake_case_				,		do_train=snake_case_				,		do_eval=snake_case_				,		do_predict=snake_case_				,		)
       UpperCAmelCase_							:       List[Any]       =				TrainerState.load_from_json(os.path.join(snake_case_				,		'trainer_state.json'						)						).log_history

       if not do_eval:
            return

       UpperCAmelCase_							:       Tuple       =				[log for log in logs if 'eval_loss' in log.keys()]

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

            UpperCAmelCase_							:       List[str]       =				eval_metrics[-1]
            assert isinstance(last_step_stats['eval_bleu']				,		snake_case_						)
            assert not math.isnan(float(last_step_stats['eval_loss']						)						), "eval_loss must not be `nan`"


  @require_torch_non_multi_gpu
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.run_seqaseq_quick()


  @require_torch_multi_gpu
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.run_seqaseq_quick(distributed=snake_case_						)


  @require_torch_multi_gpu
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.run_seqaseq_quick(distributed=snake_case_						)


  @unittest.skip('Requires an update of the env running those tests'						)
  @require_torch_multi_gpu
  @require_fairscale
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.run_seqaseq_quick(distributed=snake_case_				,		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						):




       '''simple docstring'''

       self.run_seqaseq_quick(distributed=snake_case_				,		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						):




       '''simple docstring'''

       self.run_seqaseq_quick(distributed=snake_case_				,		extra_args_str='--sharded_ddp zero_dp_2'				,		predict_with_generate=snake_case_						)


  @unittest.skip('Requires an update of the env running those tests'						)
  @require_torch_multi_gpu
  @require_fairscale
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.run_seqaseq_quick(
           distributed=snake_case_				,		extra_args_str='--sharded_ddp zero_dp_2 --fp16'				,		predict_with_generate=snake_case_						)


  @require_apex
  @require_torch_gpu
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.run_seqaseq_quick(distributed=snake_case_				,		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=snake_case_				,		extra_args_str='--fp16 --fp16_backend=apex'						)


  @parameterized.expand(['base', 'low', 'high', 'mixed']						)
  @require_torch_multi_gpu
  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, 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},
       }

       UpperCAmelCase_							:       Optional[Any]       =				experiments[experiment_id]
       UpperCAmelCase_							:       Optional[int]       =				{'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
       UpperCAmelCase_							:       Optional[int]       =				'Running training'
       with CaptureStderr() as cl:
            self.run_seqaseq_quick(**snake_case_				,		extra_args_str=data['extra_args_str']						)
       UpperCAmelCase_							:       Tuple       =				len(re.findall(snake_case_				,		cl.err						)						)
       self.assertEqual(snake_case_				,		data['n_matches']						)


  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.run_trainer(
           eval_steps=2				,		max_len=1_2_8				,		model_name=snake_case_				,		learning_rate=3E-4				,		num_train_epochs=1_0				,		distributed=snake_case_				,		)

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

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

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


  @slow
  @require_bitsandbytes
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(snake_case_						) -> Tuple[int, float]:
            UpperCAmelCase_							:       Union[str, Any]       =				'--skip_memory_metrics 0'

            UpperCAmelCase_							:       List[str]       =				self.run_trainer(
                max_len=1_2_8				,		model_name=snake_case_				,		learning_rate=3E-4				,		num_train_epochs=1				,		optim=snake_case_				,		distributed=snake_case_				,		extra_args_str=snake_case_				,		do_eval=snake_case_				,		do_predict=snake_case_				,		n_gpus_to_use=1				,		)

            # Check metrics
            UpperCAmelCase_							:       List[str]       =				TrainerState.load_from_json(Path(snake_case_				,		'trainer_state.json'						)						).log_history
            UpperCAmelCase_							:       Optional[int]       =				int(logs[0]['train_mem_gpu_peaked_delta'] / 2**2_0						)
            UpperCAmelCase_							:       Optional[int]       =				int(logs[0]['train_mem_gpu_alloc_delta'] / 2**2_0						)

            UpperCAmelCase_							:       List[Any]       =				logs[0]['train_loss']
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

       UpperCAmelCase_							:       Any       =				train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value						)
       UpperCAmelCase_							:       Optional[Any]       =				train_and_return_metrics(OptimizerNames.ADAMW_BNB.value						)

       UpperCAmelCase_							:       Optional[int]       =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       UpperCAmelCase_							:       Optional[int]       =				gpu_peak_mem_orig + gpu_alloc_mem_orig
       UpperCAmelCase_							:       List[str]       =				gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       UpperCAmelCase_							:       int       =				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
       UpperCAmelCase_							:       Tuple       =				1_2_0

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






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = 3E-3				,		snake_case_ = "adafactor"				,		snake_case_ = False				,		snake_case_ = None				,		snake_case_ = 0				,		snake_case_ = True				,		snake_case_ = True				,		snake_case_ = True				,		snake_case_ = True				,		snake_case_ = None				,		):




       '''simple docstring'''

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

       UpperCAmelCase_							:       Tuple       =				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(snake_case_						)}
        '''.split()

       UpperCAmelCase_							:       List[str]       =				'\n            --do_predict\n        '.split()

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

       return output_dir







'''simple docstring'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		1


  @register_to_config
  def __init__(			self				,		snake_case_ = 1_0_0_0				,		snake_case_ = None						):




       '''simple docstring'''

       self.set_timesteps(snake_case_						)

       # standard deviation of the initial noise distribution
       UpperCAmelCase_							:       Union[str, Any]       =				1.0

       # For now we only support F-PNDM, i.e. the runge-kutta method
       # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
       # mainly at formula (9), (12), (13) and the Algorithm 2.
       UpperCAmelCase_							:       int       =				4

       # running values
       UpperCAmelCase_							:       str       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				num_inference_steps
       UpperCAmelCase_							:       int       =				torch.linspace(1				,		0				,		num_inference_steps + 1						)[:-1]
       UpperCAmelCase_							:       Tuple       =				torch.cat([steps, torch.tensor([0.0]						)]						)

       if self.config.trained_betas is not None:
            UpperCAmelCase_							:       Optional[int]       =				torch.tensor(self.config.trained_betas				,		dtype=torch.floataa						)
       else:
            UpperCAmelCase_							:       Tuple       =				torch.sin(steps * math.pi / 2						) ** 2

       UpperCAmelCase_							:       Dict       =				(1.0 - self.betas**2) ** 0.5

       UpperCAmelCase_							:       str       =				(torch.atana(self.betas				,		self.alphas						) / math.pi * 2)[:-1]
       UpperCAmelCase_							:       str       =				timesteps.to(snake_case_						)

       UpperCAmelCase_							:       Any       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = True				,		):




       '''simple docstring'''

       if self.num_inference_steps is None:
            raise ValueError(
                'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

       UpperCAmelCase_							:       Any       =				(self.timesteps == timestep).nonzero().item()
       UpperCAmelCase_							:       Optional[Any]       =				timestep_index + 1

       UpperCAmelCase_							:       Dict       =				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(snake_case_						)

       if len(self.ets						) == 1:
            UpperCAmelCase_							:       Tuple       =				self.ets[-1]
       elif len(self.ets						) == 2:
            UpperCAmelCase_							:       Any       =				(3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets						) == 3:
            UpperCAmelCase_							:       List[str]       =				(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

       UpperCAmelCase_							:       Union[str, Any]       =				self._get_prev_sample(snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						)

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return sample


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.alphas[timestep_index]
       UpperCAmelCase_							:       Union[str, Any]       =				self.betas[timestep_index]

       UpperCAmelCase_							:       Any       =				self.alphas[prev_timestep_index]
       UpperCAmelCase_							:       Dict       =				self.betas[prev_timestep_index]

       UpperCAmelCase_							:       List[Any]       =				(sample - sigma * ets) / max(snake_case_				,		1E-8						)
       UpperCAmelCase_							:       Tuple       =				next_alpha * pred + ets * next_sigma

       return prev_sample






  def __len__(			self						):




       '''simple docstring'''

       return self.config.num_train_timesteps






