




"""simple docstring"""

import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
       import os

       from flax.core.frozen_dict import unfreeze
       from flax.traverse_util import flatten_dict

       from transformers import FlaxBertModel

       a					=			'0.12'  # assumed parallelism: 8





@require_flax
@is_staging_test
class 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''

 @classmethod
 def 			lowerCAmelCase_							( cls       :       Dict		):
       _A					   =   TOKEN
       HfFolder.save_token(A__		)

 @classmethod
 def 			lowerCAmelCase_							( cls       :       Union[str, Any]		):
       try:
             delete_repo(token=cls._token    ,							repo_id='test-model-flax'		)
       except HTTPError:
             pass

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

 def 			lowerCAmelCase_							( self       :       List[str]		):
       _A					   =   BertConfig(
           vocab_size=99    ,							hidden_size=32    ,							num_hidden_layers=5    ,							num_attention_heads=4    ,							intermediate_size=37		)
       _A					   =   FlaxBertModel(A__		)
       model.push_to_hub('test-model-flax'    ,							use_auth_token=self._token		)

       _A					   =   FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax'''		)

       _A					   =   flatten_dict(unfreeze(model.params		)		)
       _A					   =   flatten_dict(unfreeze(new_model.params		)		)

       for key in base_params.keys():
             _A					   =   (base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(A__    ,							1E-3    ,							msg=F'''{key} not identical'''		)

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

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(A__    ,							repo_id='test-model-flax'    ,							push_to_hub=A__    ,							use_auth_token=self._token		)

       _A					   =   FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax'''		)

       _A					   =   flatten_dict(unfreeze(model.params		)		)
       _A					   =   flatten_dict(unfreeze(new_model.params		)		)

       for key in base_params.keys():
             _A					   =   (base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(A__    ,							1E-3    ,							msg=F'''{key} not identical'''		)



 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   BertConfig(
           vocab_size=99    ,							hidden_size=32    ,							num_hidden_layers=5    ,							num_attention_heads=4    ,							intermediate_size=37		)
       _A					   =   FlaxBertModel(A__		)
       model.push_to_hub('valid_org/test-model-flax-org'    ,							use_auth_token=self._token		)

       _A					   =   FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'		)

       _A					   =   flatten_dict(unfreeze(model.params		)		)
       _A					   =   flatten_dict(unfreeze(new_model.params		)		)

       for key in base_params.keys():
             _A					   =   (base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(A__    ,							1E-3    ,							msg=F'''{key} not identical'''		)

       # Reset repo
       delete_repo(token=self._token    ,							repo_id='valid_org/test-model-flax-org'		)

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

       _A					   =   FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'		)

       _A					   =   flatten_dict(unfreeze(model.params		)		)
       _A					   =   flatten_dict(unfreeze(new_model.params		)		)

       for key in base_params.keys():
             _A					   =   (base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(A__    ,							1E-3    ,							msg=F'''{key} not identical'''		)






def 						_snake_case     (					_snake_case							:					Optional[int]				,  _snake_case							:					str							)    ->					List[str]:






      '''simple docstring'''


      _A					   =   True
      _A					   =   flatten_dict(modela.params							)
      _A					   =   flatten_dict(modela.params							)
      for key in flat_params_a.keys():
            if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]							)							) > 1E-4:
                  _A					   =   False

      return models_are_equal







@require_flax
class 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''

 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       _A					   =   BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'		)
       _A					   =   FlaxBertModel(A__		)

       _A					   =   """bert"""
       with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(os.path.join(A__    ,							A__		)		)

             with self.assertRaises(A__		):
                   _A					   =   FlaxBertModel.from_pretrained(A__		)

             _A					   =   FlaxBertModel.from_pretrained(A__    ,							subfolder=A__		)

       self.assertTrue(check_models_equal(A__    ,							A__		)		)

 def 			lowerCAmelCase_							( self       :       Any		):
       _A					   =   BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'		)
       _A					   =   FlaxBertModel(A__		)

       _A					   =   """bert"""
       with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(os.path.join(A__    ,							A__		)    ,							max_shard_size='10KB'		)

             with self.assertRaises(A__		):
                   _A					   =   FlaxBertModel.from_pretrained(A__		)

             _A					   =   FlaxBertModel.from_pretrained(A__    ,							subfolder=A__		)

       self.assertTrue(check_models_equal(A__    ,							A__		)		)

 def 			lowerCAmelCase_							( self       :       Tuple		):
       _A					   =   """bert"""
       _A					   =   """hf-internal-testing/tiny-random-bert-subfolder"""

       with self.assertRaises(A__		):
             _A					   =   FlaxBertModel.from_pretrained(A__		)

       _A					   =   FlaxBertModel.from_pretrained(A__    ,							subfolder=A__		)

       self.assertIsNotNone(A__		)



 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   """bert"""
       _A					   =   """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
       with self.assertRaises(A__		):
             _A					   =   FlaxBertModel.from_pretrained(A__		)

       _A					   =   FlaxBertModel.from_pretrained(A__    ,							subfolder=A__		)

       self.assertIsNotNone(A__		)




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


A_       : 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__":
	A_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

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

	env.seed(0)
	A_       : List[Any]         =			env.reset()
	A_       : Optional[int]         =			0
	A_       : str         =			0
	A_       : Optional[Any]         =			1000
	A_       : Union[str, Any]         =			[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			A_       : Tuple         =			pipeline(obs, planning_horizon=32)

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[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())

			A_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

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



