





"""simple docstring"""






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


lowerCAmelCase_			=							{
    '''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_			=							'''hopper-medium-v2'''
    lowerCAmelCase_			=							gym.make(env_name)

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

    env.seed(0)
    lowerCAmelCase_			=							env.reset()
    lowerCAmelCase_			=							0
    lowerCAmelCase_			=							0
    lowerCAmelCase_			=							1_000
    lowerCAmelCase_			=							[obs.copy()]
    try:
        for t in tqdm.tqdm(range(T)):
            # call the policy
            lowerCAmelCase_			=							pipeline(obs, planning_horizon=32)

            # execute action in environment
            lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			=							env.step(denorm_actions)
            lowerCAmelCase_			=							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_			=							next_observation
    except KeyboardInterrupt:
        pass

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













"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	lowerCAmelCase					(      unittest.TestCase							):






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'     )
              _UpperCAmelCase     =     AutoTokenizer.from_pretrained('google/mt5-small'     )

              _UpperCAmelCase     =     tokenizer('Hello there'    ,  return_tensors='np'     ).input_ids
              _UpperCAmelCase     =     tokenizer('Hi I am'    ,  return_tensors='np'     ).input_ids

              _UpperCAmelCase     =     shift_tokens_right(a__    ,  model.config.pad_token_id    ,  model.config.decoder_start_token_id     )

              _UpperCAmelCase     =     model(a__    ,  decoder_input_ids=a__     ).logits
              _UpperCAmelCase     =     optax.softmax_cross_entropy(a__    ,  onehot(a__    ,  logits.shape[-1]     )     ).mean()

              _UpperCAmelCase     =     -(labels.shape[-1] * loss.item())

              _UpperCAmelCase     =     -84.9_127
              self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )






