import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


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

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_UpperCAmelCase            =		get_tests_dir("""fixtures""")





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				mock.Mock()
						A_		:    List[str]         =				5_0_0
						A_		:    Any         =				{}
						A_		:    str         =				HTTPError
						A_		:    Any         =				{}

						# Download this model to make sure it's in the cache.
						A_		:    List[Any]         =				WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
						# Under the mock environment we get a 500 error when trying to reach the model.
						with mock.patch('requests.Session.request'		,     return_value=lowercase ) as mock_head:
								A_		:    Union[str, Any]         =				WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
								# This check we did call the fake head request
								mock_head.assert_called()







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				WavaVecaFeatureExtractor.from_pretrained(
						    'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json' )





@is_staging_test
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@classmethod
				def   lowerCAmelCase_       (						cls ):



						"""simple docstring"""





						A_		:    str         =				TOKEN
						HfFolder.save_token(lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls ):



						"""simple docstring"""





						try:
								delete_repo(token=cls._token		,     repo_id='test-feature-extractor' )
						except HTTPError:
								pass

						try:
								delete_repo(token=cls._token		,     repo_id='valid_org/test-feature-extractor-org' )
						except HTTPError:
								pass

						try:
								delete_repo(token=cls._token		,     repo_id='test-dynamic-feature-extractor' )
						except HTTPError:
								pass
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				WavaVecaFeatureExtractor.from_pretrained(lowercase )
						feature_extractor.push_to_hub('test-feature-extractor'		,     use_auth_token=self._token )

						A_		:    str         =				WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
						for k, v in feature_extractor.__dict__.items():
								self.assertEqual(lowercase		,     getattr(lowercase		,     lowercase ) )

						# Reset repo
						delete_repo(token=self._token		,     repo_id='test-feature-extractor' )

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
								feature_extractor.save_pretrained(
								    lowercase		,     repo_id='test-feature-extractor'		,     push_to_hub=lowercase		,     use_auth_token=self._token )

						A_		:    str         =				WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
						for k, v in feature_extractor.__dict__.items():
								self.assertEqual(lowercase		,     getattr(lowercase		,     lowercase ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				WavaVecaFeatureExtractor.from_pretrained(lowercase )
						feature_extractor.push_to_hub('valid_org/test-feature-extractor'		,     use_auth_token=self._token )

						A_		:    int         =				WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor' )
						for k, v in feature_extractor.__dict__.items():
								self.assertEqual(lowercase		,     getattr(lowercase		,     lowercase ) )

						# Reset repo
						delete_repo(token=self._token		,     repo_id='valid_org/test-feature-extractor' )

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
								feature_extractor.save_pretrained(
								    lowercase		,     repo_id='valid_org/test-feature-extractor-org'		,     push_to_hub=lowercase		,     use_auth_token=self._token )

						A_		:    List[Any]         =				WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org' )
						for k, v in feature_extractor.__dict__.items():
								self.assertEqual(lowercase		,     getattr(lowercase		,     lowercase ) )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						CustomFeatureExtractor.register_for_auto_class()
						A_		:    Tuple         =				CustomFeatureExtractor.from_pretrained(lowercase )

						feature_extractor.push_to_hub('test-dynamic-feature-extractor'		,     use_auth_token=self._token )

						# This has added the proper auto_map field to the config
						self.assertDictEqual(
						    feature_extractor.auto_map		,     {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}		,     )

						A_		:    List[Any]         =				AutoFeatureExtractor.from_pretrained(
						    F'''{USER}/test-dynamic-feature-extractor'''		,     trust_remote_code=lowercase )
						# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
						self.assertEqual(new_feature_extractor.__class__.__name__		,     'CustomFeatureExtractor' )







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


_UpperCAmelCase            =		{
    """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__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

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

	env.seed(0)
	_UpperCAmelCase            =		env.reset()
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		1000
	_UpperCAmelCase            =		[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			_UpperCAmelCase            =		pipeline(obs, planning_horizon=32)

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		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())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

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






