



"""simple docstring"""

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


lowercase__					:						List[str]												=			{
    '''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__":
     lowercase__					:						Tuple												=			'''hopper-medium-v2'''
     lowercase__					:						Optional[int]												=			gym.make(env_name)

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

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

               # execute action in environment
               lowercase__					,lowercase__					,lowercase__					,lowercase__					:						Any												=			env.step(denorm_actions)
               lowercase__					:						str												=			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__					:						Optional[Any]												=			next_observation
     except KeyboardInterrupt:
          pass

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










"""simple docstring"""

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowercase__					:						Tuple												=			get_tests_dir('''fixtures''')
lowercase__					:						List[str]												=			get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
lowercase__					:						Dict												=			get_tests_dir('''fixtures/dummy-config.json''')




class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[Any]       =			0

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int       ):
     with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case_				:  List[Any]       =			WavaVecaConfig()

         # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
         snake_case_				:  Optional[Any]       =			AutoFeatureExtractor.from_pretrained(lowercase_       ).to_dict()

         config_dict.pop('''feature_extractor_type'''       )
         snake_case_				:  Optional[Any]       =			WavaVecaFeatureExtractor(**lowercase_       )

         # save in new folder
         model_config.save_pretrained(lowercase_       )
         config.save_pretrained(lowercase_       )

         snake_case_				:  List[Any]       =			AutoFeatureExtractor.from_pretrained(lowercase_       )

         # make sure private variable is not incorrectly saved
         snake_case_				:  Dict       =			json.loads(config.to_json_string()       )
         self.assertTrue('''_processor_class''' not in dict_as_saved       )

     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: List[Any]       ):
     with self.assertRaisesRegex(
         lowercase_						,  '''bert-base is not a local folder and is not a valid model identifier'''       ):
         snake_case_				:  List[str]       =			AutoFeatureExtractor.from_pretrained('''bert-base'''       )

 def 		_snake_case      (  self	: Any       ):
     with self.assertRaisesRegex(
         lowercase_						,  r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''       ):
         snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained(lowercase_						,  revision='''aaaaaa'''       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     with self.assertRaisesRegex(
         lowercase_						,  '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''						,  ):
         snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''       )

 def 		_snake_case      (  self	: Optional[int]       ):
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(lowercase_       ):
         snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''       )
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(lowercase_       ):
         snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )

     snake_case_				:  List[str]       =			AutoFeatureExtractor.from_pretrained(
         '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )
     self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )

     # Test feature extractor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
         feature_extractor.save_pretrained(lowercase_       )
         snake_case_				:  Optional[int]       =			AutoFeatureExtractor.from_pretrained(lowercase_						,  trust_remote_code=lowercase_       )
     self.assertEqual(reloaded_feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     try:
         AutoConfig.register('''custom'''						,  lowercase_       )
         AutoFeatureExtractor.register(lowercase_						,  lowercase_       )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(lowercase_       ):
             AutoFeatureExtractor.register(lowercase_						,  lowercase_       )

         # Now that the config is registered, it can be used as any other config with the auto-API
         snake_case_				:  List[Any]       =			CustomFeatureExtractor.from_pretrained(lowercase_       )
         with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(lowercase_       )
             snake_case_				:  Any       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
             self.assertIsInstance(lowercase_						,  lowercase_       )

     finally:
         if "custom" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
             del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






 def 		_snake_case      (  self	: Union[str, Any]       ):




     class  _UpperCAmelCase (    lowerCAmelCase__):
      _lowerCAmelCase     :    str	      =	True

     try:
         AutoConfig.register('''custom'''						,  lowercase_       )
         AutoFeatureExtractor.register(lowercase_						,  lowercase_       )
         # If remote code is not set, the default is to use local
         snake_case_				:  List[Any]       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''       )
         self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )
         self.assertTrue(feature_extractor.is_local       )

         # If remote code is disabled, we load the local one.
         snake_case_				:  Union[str, Any]       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )
         self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )
         self.assertTrue(feature_extractor.is_local       )

         # If remote is enabled, we load from the Hub
         snake_case_				:  Tuple       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )
         self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )
         self.assertTrue(not hasattr(lowercase_						,  '''is_local'''       )       )

     finally:
         if "custom" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
             del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





