


"""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


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 	A_ (	SCREAMING_SNAKE_CASE__   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	str						,				a:	int=False						,				a:	Any=None						,				a:	Any=True						,				a:	Optional[int]=True						,				a:	Union[str, Any]=True						,				a:	Optional[Any]=True						,				):
       __lowerCamelCase		:							Dict		 =						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		:							Dict		 =						TrainerState.load_from_json(os.path.join(a_						,				'trainer_state.json'    )    ).log_history

       if not do_eval:
        return

       __lowerCamelCase		:							Tuple		 =						[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		:							Optional[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 					_snake_case     (    self:	Union[str, Any]    ):
       self.run_seqaseq_quick()




      @require_torch_multi_gpu
      def 					_snake_case     (    self:	Union[str, Any]    ):
       self.run_seqaseq_quick(distributed=a_    )




      @require_torch_multi_gpu
      def 					_snake_case     (    self:	Dict    ):
       self.run_seqaseq_quick(distributed=a_    )




      @unittest.skip('Requires an update of the env running those tests'    )
      @require_torch_multi_gpu
      @require_fairscale
      def 					_snake_case     (    self:	Tuple    ):
       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 					_snake_case     (    self:	List[str]    ):
       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 					_snake_case     (    self:	Tuple    ):
       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 					_snake_case     (    self:	str    ):
       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 					_snake_case     (    self:	str    ):
       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 					_snake_case     (    self:	List[Any]						,				a:	Any    ):
       __lowerCamelCase		:							List[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},
       }

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




      @slow
      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Tuple		 =						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		:							Any		 =						TrainerState.load_from_json(os.path.join(a_						,				'trainer_state.json'    )    ).log_history
       __lowerCamelCase		:							Tuple		 =						[log for log in logs if 'eval_loss' in log.keys()]
       __lowerCamelCase		:							Optional[Any]		 =						eval_metrics[0]
       __lowerCamelCase		:							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']						,				a_    )

       # test if do_predict saves generations and metrics
       __lowerCamelCase		:							Dict		 =						os.listdir(a_    )
       __lowerCamelCase		:							Dict		 =						{os.path.basename(a_    ) for p in contents}
       assert "generated_predictions.txt" in contents
       assert "predict_results.json" in contents




      @slow
      @require_bitsandbytes
      def 					_snake_case     (    self:	str    ):
       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(a:	str    ) -> Tuple[int, float]:
        __lowerCamelCase		:							Tuple		 =						'--skip_memory_metrics 0'

        __lowerCamelCase		:							List[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		:							Any		 =						TrainerState.load_from_json(Path(a_						,				'trainer_state.json'    )    ).log_history
        __lowerCamelCase		:							int		 =						int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20    )
        __lowerCamelCase		:							Dict		 =						int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20    )

        __lowerCamelCase		:							Optional[Any]		 =						logs[0]['train_loss']
        return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

       __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value    )
       __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							Tuple		 =						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value    )

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

       __lowerCamelCase		:							int		 =						gpu_peak_mem_orig + gpu_alloc_mem_orig
       __lowerCamelCase		:							Any		 =						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       __lowerCamelCase		:							Any		 =						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		:							Tuple		 =						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 					_snake_case     (    self:	Union[str, 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						,				):
       __lowerCamelCase		:							str		 =						self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
       __lowerCamelCase		:							str		 =						self.get_auto_remove_tmp_dir()
       __lowerCamelCase		:							Union[str, Any]		 =						F'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(a_    )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(a_    )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

       __lowerCamelCase		:							Optional[Any]		 =						F'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(a_    )}\n        '.split()

       __lowerCamelCase		:							Optional[Any]		 =						'\n            --do_predict\n        '.split()

       __lowerCamelCase		:							Union[str, 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:
         __lowerCamelCase		:							List[Any]		 =						get_gpu_count()
        __lowerCamelCase		:							Union[str, Any]		 =						get_torch_dist_unique_port()
        __lowerCamelCase		:							List[str]		 =						F'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.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		:							Dict		 =						['run_translation.py'] + args
        with patch.object(a_						,				'argv'						,				a_    ):
         main()

       return output_dir





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

       lowercase_	   =     ValueGuidedRLPipeline.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32',
           env=env,
       )

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
                         F""" {total_score}"""
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

       print(F"""Total reward: {total_reward}""")

