

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  (UnCLIPScheduler,)




  def   A__     (				self							,   **lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       ={
            'num_train_timesteps': 1_000,
            'variance_type': 'fixed_small_log',
            'clip_sample': True,
            'clip_sample_range': 1.0,
            'prediction_type': 'epsilon',
        }

        config.update(**lowerCAmelCase					)
        return config




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowerCAmelCase					)




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=lowerCAmelCase					)




  def   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowerCAmelCase					)




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        for clip_sample_range in [1, 5, 10, 20]:
              self.check_over_configs(clip_sample_range=lowerCAmelCase					)




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=lowerCAmelCase					)




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        for time_step in [0, 500, 999]:
              for prev_timestep in [None, 5, 100, 250, 500, 750]:
                    if prev_timestep is not None and prev_timestep >= time_step:
                          continue

                    self.check_over_forward(time_step=lowerCAmelCase							,   prev_timestep=lowerCAmelCase					)




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config(variance_type='fixed_small_log'					)
        _lowercase	       =scheduler_class(**lowerCAmelCase					)

        assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0000e-10					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0549625					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9994987					)					) < 1e-5




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config(variance_type='learned_range'					)
        _lowercase	       =scheduler_class(**lowerCAmelCase					)

        _lowercase	       =0.5

        assert scheduler._get_variance(1							,   predicted_variance=lowerCAmelCase					) - -10.1712790 < 1e-5
        assert scheduler._get_variance(487							,   predicted_variance=lowerCAmelCase					) - -5.7998052 < 1e-5
        assert scheduler._get_variance(999							,   predicted_variance=lowerCAmelCase					) - -0.0010011 < 1e-5




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**lowerCAmelCase					)

        _lowercase	       =scheduler.timesteps

        _lowercase	       =self.dummy_model()
        _lowercase	       =self.dummy_sample_deter
        _lowercase	       =torch.manual_seed(0					)

        for i, t in enumerate(lowerCAmelCase					):
              # 1. predict noise residual
              _lowercase	       =model(lowerCAmelCase							,   lowerCAmelCase					)

              # 2. predict previous mean of sample x_t-1
              _lowercase	       =scheduler.step(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   generator=lowerCAmelCase					).prev_sample

              _lowercase	       =pred_prev_sample

        _lowercase	       =torch.sum(torch.abs(lowerCAmelCase					)					)
        _lowercase	       =torch.mean(torch.abs(lowerCAmelCase					)					)

        assert abs(result_sum.item() - 252.2682495					) < 1e-2
        assert abs(result_mean.item() - 0.3284743					) < 1e-3




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**lowerCAmelCase					)

        scheduler.set_timesteps(25					)

        _lowercase	       =scheduler.timesteps

        _lowercase	       =self.dummy_model()
        _lowercase	       =self.dummy_sample_deter
        _lowercase	       =torch.manual_seed(0					)

        for i, t in enumerate(lowerCAmelCase					):
              # 1. predict noise residual
              _lowercase	       =model(lowerCAmelCase							,   lowerCAmelCase					)

              if i + 1 == timesteps.shape[0]:
                    _lowercase	       =None
              else:
                    _lowercase	       =timesteps[i + 1]

              # 2. predict previous mean of sample x_t-1
              _lowercase	       =scheduler.step(
                  lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   prev_timestep=lowerCAmelCase							,   generator=lowerCAmelCase					).prev_sample

              _lowercase	       =pred_prev_sample

        _lowercase	       =torch.sum(torch.abs(lowerCAmelCase					)					)
        _lowercase	       =torch.mean(torch.abs(lowerCAmelCase					)					)

        assert abs(result_sum.item() - 258.2044983					) < 1e-2
        assert abs(result_mean.item() - 0.3362038					) < 1e-3




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        pass




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        pass





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


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


set_seed(4_2)
lowercase_									=   'sshleifer/student_marian_en_ro_6_1'
lowercase_									=   'sshleifer/tiny-mbart'





@require_torch
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self							,   lowerCAmelCase=False							,   lowerCAmelCase=None							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   )  -> Dict:
        '''simple docstring'''






        _lowercase	       =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	       =TrainerState.load_from_json(os.path.join(lowerCAmelCase							,   'trainer_state.json'					)					).log_history

        if not do_eval:
              return

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

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

              _lowercase	       =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   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        self.run_seqaseq_quick()




  @require_torch_multi_gpu
  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        self.run_seqaseq_quick(distributed=lowerCAmelCase					)




  @require_torch_multi_gpu
  def   A__     (				self					)  -> Optional[Any]:
        '''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   A__     (				self					)  -> Optional[Any]:
        '''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   A__     (				self					)  -> Optional[int]:
        '''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   A__     (				self					)  -> Dict:
        '''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   A__     (				self					)  -> Optional[int]:
        '''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   A__     (				self					)  -> List[Any]:
        '''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   A__     (				self							,   lowerCAmelCase					)  -> Any:
        '''simple docstring'''






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




  @slow
  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.run_trainer(
            eval_steps=2							,   max_len=128							,   model_name=lowerCAmelCase							,   learning_rate=3e-4							,   num_train_epochs=10							,   distributed=lowerCAmelCase							,   )

        # Check metrics
        _lowercase	       =TrainerState.load_from_json(os.path.join(lowerCAmelCase							,   'trainer_state.json'					)					).log_history
        _lowercase	       =[log for log in logs if 'eval_loss' in log.keys()]
        _lowercase	       =eval_metrics[0]
        _lowercase	       =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	       =os.listdir(lowerCAmelCase					)
        _lowercase	       ={os.path.basename(lowerCAmelCase					) 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(lowerCAmelCase					) -> Tuple[int, float]:
              _lowercase	       ='--skip_memory_metrics 0'

              _lowercase	       =self.run_trainer(
                  max_len=128							,   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	       =TrainerState.load_from_json(Path(lowerCAmelCase							,   'trainer_state.json'					)					).log_history
              _lowercase	       =int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20					)
              _lowercase	       =int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20					)

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

        _lowercase       ,       _lowercase       ,       _lowercase	       =train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value					)
        _lowercase       ,       _lowercase       ,       _lowercase	       =train_and_return_metrics(OptimizerNames.ADAMW_BNB.value					)

        _lowercase	       =gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        _lowercase	       =gpu_peak_mem_orig + gpu_alloc_mem_orig
        _lowercase	       =gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        _lowercase	       =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	       =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(
            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   A__     (				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							,   )  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
        _lowercase	       =self.get_auto_remove_tmp_dir()
        _lowercase	       =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	       =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	       ='\n            --do_predict\n        '.split()

        _lowercase	       =[]
        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	       =get_gpu_count()
              _lowercase	       =get_torch_dist_unique_port()
              _lowercase	       =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	       =[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	       =['run_translation.py'] + args
              with patch.object(lowerCAmelCase							,   'argv'							,   lowerCAmelCase					):
                    main()

        return output_dir


