



import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers.models.esm.modeling_esmfold import EsmForProteinFolding



class 				__snake_case						:




							def __init__(       self     :       List[str]				,				A_     :       str				,				A_     :       List[Any]=1_3				,				A_     :       List[str]=7				,				A_     :       List[Any]=False				,				A_     :       int=True				,				A_     :       int=False				,				A_     :       str=False				,				A_     :       Optional[Any]=1_9				,				A_     :       Optional[int]=3_2				,				A_     :       Any=5				,				A_     :       Union[str, Any]=4				,				A_     :       Dict=3_7				,				A_     :       Optional[int]="gelu"				,				A_     :       Tuple=0.1				,				A_     :       List[Any]=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       Optional[Any]=1_6				,				A_     :       List[Any]=2				,				A_     :       List[Any]=0.02				,				A_     :       List[Any]=3				,				A_     :       str=4				,				A_     :       Tuple=None				,				):
									lowerCAmelCase_			:     List[Any]									= parent
									lowerCAmelCase_			:     int									= batch_size
									lowerCAmelCase_			:     Optional[Any]									= seq_length
									lowerCAmelCase_			:     Dict									= is_training
									lowerCAmelCase_			:     List[str]									= use_input_mask
									lowerCAmelCase_			:     Union[str, Any]									= use_token_type_ids
									lowerCAmelCase_			:     Optional[Any]									= use_labels
									lowerCAmelCase_			:     int									= vocab_size
									lowerCAmelCase_			:     List[Any]									= hidden_size
									lowerCAmelCase_			:     str									= num_hidden_layers
									lowerCAmelCase_			:     List[Any]									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Optional[Any]									= hidden_act
									lowerCAmelCase_			:     str									= hidden_dropout_prob
									lowerCAmelCase_			:     List[str]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[int]									= max_position_embeddings
									lowerCAmelCase_			:     Optional[Any]									= type_vocab_size
									lowerCAmelCase_			:     Dict									= type_sequence_label_size
									lowerCAmelCase_			:     Union[str, Any]									= initializer_range
									lowerCAmelCase_			:     Dict									= num_labels
									lowerCAmelCase_			:     Union[str, Any]									= num_choices
									lowerCAmelCase_			:     str									= scope




							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     Dict									= ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size)

									lowerCAmelCase_			:     Union[str, Any]									= None
									if self.use_input_mask:
											lowerCAmelCase_			:     List[str]									= random_attention_mask([self.batch_size, self.seq_length])

									lowerCAmelCase_			:     List[str]									= None
									lowerCAmelCase_			:     Any									= None
									lowerCAmelCase_			:     str									= None
									if self.use_labels:
											lowerCAmelCase_			:     List[str]									= ids_tensor([self.batch_size]				,				self.type_sequence_label_size)
											lowerCAmelCase_			:     int									= ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels)
											lowerCAmelCase_			:     Dict									= ids_tensor([self.batch_size]				,				self.num_choices)

									lowerCAmelCase_			:     Any									= self.get_config()

									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     List[str]									= EsmConfig(
									    vocab_size=3_3				,				hidden_size=self.hidden_size				,				pad_token_id=1				,				num_hidden_layers=self.num_hidden_layers				,				num_attention_heads=self.num_attention_heads				,				intermediate_size=self.intermediate_size				,				hidden_act=self.hidden_act				,				hidden_dropout_prob=self.hidden_dropout_prob				,				attention_probs_dropout_prob=self.attention_probs_dropout_prob				,				max_position_embeddings=self.max_position_embeddings				,				type_vocab_size=self.type_vocab_size				,				initializer_range=self.initializer_range				,				is_folding_model=A_				,				esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}				,				)
									return config




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       List[Any]				,				A_     :       Dict				,				A_     :       List[Any]				,				A_     :       str				,				A_     :       str				,				A_     :       Dict):
									lowerCAmelCase_			:     List[Any]									= EsmForProteinFolding(config=A_).float()
									model.to(A_)
									model.eval()
									lowerCAmelCase_			:     Optional[Any]									= model(A_				,				attention_mask=A_)
									lowerCAmelCase_			:     Union[str, Any]									= model(A_)
									lowerCAmelCase_			:     List[Any]									= model(A_)

									self.parent.assertEqual(result.positions.shape				,				(8, self.batch_size, self.seq_length, 1_4, 3))
									self.parent.assertEqual(result.angles.shape				,				(8, self.batch_size, self.seq_length, 7, 2))







							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Union[str, Any]									= self.prepare_config_and_inputs()
									(
									    (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  
									)			:     Union[str, Any]									= config_and_inputs
									lowerCAmelCase_			:     List[Any]									= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict





@require_torch
class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a			      = False

							_a			      = (EsmForProteinFolding,) if is_torch_available() else ()
							_a			      = ()
							_a			      = {} if is_torch_available() else {}
							_a			      = False




							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     Optional[Any]									= EsmFoldModelTester(self)
									lowerCAmelCase_			:     Union[str, Any]									= ConfigTester(self				,				config_class=A_				,				hidden_size=3_7)




							def    UpperCAmelCase__			(       self     :       Dict):
									self.config_tester.run_common_tests()




							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Dict									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A_)




							@unittest.skip('''Does not support attention outputs''')
							def    UpperCAmelCase__			(       self     :       Any):
									pass




							@unittest.skip
							def    UpperCAmelCase__			(       self     :       Tuple):
									pass




							@unittest.skip('''Esm does not support embedding resizing''')
							def    UpperCAmelCase__			(       self     :       Any):
									pass




							@unittest.skip('''Esm does not support embedding resizing''')
							def    UpperCAmelCase__			(       self     :       List[Any]):
									pass




							@unittest.skip('''ESMFold does not support passing input embeds!''')
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									pass




							@unittest.skip('''ESMFold does not support head pruning.''')
							def    UpperCAmelCase__			(       self     :       Tuple):
									pass




							@unittest.skip('''ESMFold does not support head pruning.''')
							def    UpperCAmelCase__			(       self     :       Dict):
									pass




							@unittest.skip('''ESMFold does not support head pruning.''')
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									pass




							@unittest.skip('''ESMFold does not support head pruning.''')
							def    UpperCAmelCase__			(       self     :       str):
									pass




							@unittest.skip('''ESMFold does not support head pruning.''')
							def    UpperCAmelCase__			(       self     :       List[str]):
									pass




							@unittest.skip('''ESMFold does not output hidden states in the normal way.''')
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									pass




							@unittest.skip('''ESMfold does not output hidden states in the normal way.''')
							def    UpperCAmelCase__			(       self     :       List[Any]):
									pass




							@unittest.skip('''ESMFold only has one output format.''')
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									pass




							@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality''')
							def    UpperCAmelCase__			(       self     :       Tuple):
									pass




							@unittest.skip('''ESMFold does not support input chunking.''')
							def    UpperCAmelCase__			(       self     :       str):
									pass




							@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.''')
							def    UpperCAmelCase__			(       self     :       str):
									pass




							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''')
							def    UpperCAmelCase__			(       self     :       Dict):
									pass




							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''')
							def    UpperCAmelCase__			(       self     :       List[str]):
									pass




							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''')
							def    UpperCAmelCase__			(       self     :       List[Any]):
									pass




							@unittest.skip('''ESMFold doesn\'t support data parallel.''')
							def    UpperCAmelCase__			(       self     :       List[str]):
									pass







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
							def    UpperCAmelCase__			(       self     :       Any):
									pass





@require_torch
class 				__snake_case						(     UpperCamelCase_						):




							@slow
							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1''').float()
									model.eval()
									lowerCAmelCase_			:     Optional[Any]									= torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]])
									lowerCAmelCase_			:     Dict									= model(A_)['''positions''']
									lowerCAmelCase_			:     Any									= torch.tensor([2.5828, 0.7993, -10.9334]				,				dtype=torch.floataa)
									self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]				,				A_				,				atol=1e-4))










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


A__  :  Union[str, Any]								=					{
    '''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__  :  int								=					gym.make(env_name)

						A__  :  Optional[int]								=					ValueGuidedRLPipeline.from_pretrained(
						    '''bglick13/hopper-medium-v2-value-function-hor32''',
						    env=env,
						)

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

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					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__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

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





