



"""simple docstring"""




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


_lowerCAmelCase    : int							=							{
    "n_samples": 64,
    "horizon": 32,
    "num_inference_steps": 20,
    "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__":
 _lowerCAmelCase    : int							=							"hopper-medium-v2"
 _lowerCAmelCase    : Optional[Any]							=							gym.make(env_name)

 _lowerCAmelCase    : int							=							ValueGuidedRLPipeline.from_pretrained(
     "bglick13/hopper-medium-v2-value-function-hor32",
     env=env,
 )

 env.seed(0)
 _lowerCAmelCase    : Dict							=							env.reset()
 _lowerCAmelCase    : Optional[Any]							=							0
 _lowerCAmelCase    : List[str]							=							0
 _lowerCAmelCase    : int							=							10_00
 _lowerCAmelCase    : int							=							[obs.copy()]
 try:
  for t in tqdm.tqdm(range(T)):
   # call the policy
   _lowerCAmelCase    : int							=							pipeline(obs, planning_horizon=32)

   # execute action in environment
   _lowerCAmelCase,						_lowerCAmelCase,						_lowerCAmelCase,						_lowerCAmelCase    : Tuple							=							env.step(denorm_actions)
   _lowerCAmelCase    : Union[str, Any]							=							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())

   _lowerCAmelCase    : Optional[Any]							=							next_observation
 except KeyboardInterrupt:
  pass

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





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowerCAmelCase    : Union[str, Any]							=							get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 _lowerCAmelCase    : Tuple							=							json.load(f)






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           return FSMTTokenizer.from_pretrained(A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   FSMTForConditionalGeneration.from_pretrained(A						).to(A						)
           if torch_device == "cuda":
                model.half()
           return model



      @parameterized.expand(
          [
              ["en-ru", 26.0],
              ["ru-en", 22.0],
              ["en-de", 22.0],
              ["de-en", 29.0],
          ]						)
      @slow
      def 						snake_case_ (					self					:     Any					,     A					:     Dict					,     A					:     List[str]						):
           # note: this test is not testing the best performance since it only evals a small batch
           # but it should be enough to detect a regression in the output quality
           _UpperCAmelCase      :     Any        =   f'facebook/wmt19-{pair}'
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer(A						)
           _UpperCAmelCase      :     Optional[int]        =   self.get_model(A						)

           _UpperCAmelCase      :     int        =   bleu_data[pair]["src"]
           _UpperCAmelCase      :     Optional[int]        =   bleu_data[pair]["tgt"]

           _UpperCAmelCase      :     List[str]        =   tokenizer(A					,     return_tensors="pt"					,     truncation=A					,     padding="longest"						).to(A						)
           _UpperCAmelCase      :     List[str]        =   model.generate(
               input_ids=batch.input_ids					,     num_beams=8					,     )
           _UpperCAmelCase      :     Any        =   tokenizer.batch_decode(
               A					,     skip_special_tokens=A					,     clean_up_tokenization_spaces=A						)
           _UpperCAmelCase      :     Any        =   calculate_bleu(A					,     A						)
           print(A						)
           self.assertGreaterEqual(scores["bleu"]					,     A						)
