

import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						)



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					List[str]       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Union[str, Any]=14       ,						lowerCAmelCase__  :					Optional[Any]=7       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					str=True       ,						lowerCAmelCase__  :					str=True       ,						lowerCAmelCase__  :					Any=True       ,						lowerCAmelCase__  :					Any=True       ,						lowerCAmelCase__  :					Optional[int]=99       ,						lowerCAmelCase__  :					Union[str, Any]=32       ,						lowerCAmelCase__  :					Optional[Any]=5       ,						lowerCAmelCase__  :					List[Any]=4       ,						lowerCAmelCase__  :					Tuple=37       ,						lowerCAmelCase__  :					List[Any]="gelu"       ,						lowerCAmelCase__  :					Tuple=0.1       ,						lowerCAmelCase__  :					List[str]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=512       ,						lowerCAmelCase__  :					int=16       ,						lowerCAmelCase__  :					Optional[int]=2       ,						lowerCAmelCase__  :					List[Any]=0.02       ,						lowerCAmelCase__  :					Optional[int]=3       ,						lowerCAmelCase__  :					List[str]=4       ,						lowerCAmelCase__  :					Optional[int]=None       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	parent
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	batch_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	seq_length
									SCREAMING_SNAKE_CASE_:			str  		=	is_training
									SCREAMING_SNAKE_CASE_:			int  		=	use_token_type_ids
									SCREAMING_SNAKE_CASE_:			Any  		=	use_input_mask
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	use_labels
									SCREAMING_SNAKE_CASE_:			List[str]  		=	use_mc_token_ids
									SCREAMING_SNAKE_CASE_:			Dict  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			List[str]  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Any  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			str  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			int  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			Dict  		=	attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:			str  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			Tuple  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			str  		=	type_sequence_label_size
									SCREAMING_SNAKE_CASE_:			Any  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Any  		=	num_labels
									SCREAMING_SNAKE_CASE_:			str  		=	num_choices
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	scope
									SCREAMING_SNAKE_CASE_:			int  		=	self.vocab_size - 1



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Dict  		=	ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size)

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

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	None
									if self.use_token_type_ids:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor([self.batch_size, self.seq_length]       ,						self.type_vocab_size)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	None
									if self.use_mc_token_ids:
															SCREAMING_SNAKE_CASE_:			int  		=	ids_tensor([self.batch_size, self.num_choices]       ,						self.seq_length)

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

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.get_config()

									SCREAMING_SNAKE_CASE_:			Dict  		=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,						2)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									return CTRLConfig(
									    vocab_size=self.vocab_size       ,						n_embd=self.hidden_size       ,						n_layer=self.num_hidden_layers       ,						n_head=self.num_attention_heads       ,						n_positions=self.max_position_embeddings       ,						pad_token_id=self.pad_token_id       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[Any]       ,						*lowerCAmelCase__  :					Any):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	CTRLModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									model(lowerCAmelCase__       ,						token_type_ids=lowerCAmelCase__       ,						head_mask=lowerCAmelCase__)
									model(lowerCAmelCase__       ,						token_type_ids=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(lowerCAmelCase__)
									self.parent.assertEqual(result.last_hidden_state.shape       ,						(self.batch_size, self.seq_length, self.hidden_size))
									self.parent.assertEqual(len(result.past_key_values)       ,						config.n_layer)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Optional[Any]       ,						*lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	CTRLLMHeadModel(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(lowerCAmelCase__       ,						token_type_ids=lowerCAmelCase__       ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(result.loss.shape       ,						())
									self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.vocab_size))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.prepare_config_and_inputs()

									(
									    (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   
									):			Optional[Any]  		=	config_and_inputs

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

									return config, inputs_dict




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					Any       ,						*lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Any  		=	self.num_labels
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CTRLForSequenceClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ids_tensor([self.batch_size]       ,						self.type_sequence_label_size)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(lowerCAmelCase__       ,						token_type_ids=lowerCAmelCase__       ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels))







@require_torch
class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
			_UpperCAmelCase       :							Tuple      							=					(CTRLLMHeadModel,) if is_torch_available() else ()
			_UpperCAmelCase       :							Any      							=					(
			    {
			        '''feature-extraction''': CTRLModel,
			        '''text-classification''': CTRLForSequenceClassification,
			        '''text-generation''': CTRLLMHeadModel,
			        '''zero-shot''': CTRLForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			_UpperCAmelCase       :							Optional[Any]      							=					True
			_UpperCAmelCase       :							Union[str, Any]      							=					False
			_UpperCAmelCase       :							Dict      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Dict):
									if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
															# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
															# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
															# config could not be created.
															return True

									return False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	CTRLModelTester(self)
									SCREAMING_SNAKE_CASE_:			Dict  		=	ConfigTester(self       ,						config_class=lowerCAmelCase__       ,						n_embd=37)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									self.config_tester.run_common_tests()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__)



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



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	CTRLModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)




			@unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									pass







@require_torch
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									torch.cuda.empty_cache()




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Any  		=	CTRLLMHeadModel.from_pretrained("ctrl")
									model.to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.tensor(
									    [[1_1859, 0, 1611, 8]]       ,						dtype=torch.long       ,						device=lowerCAmelCase__)  # Legal the president is
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[
									    1_1859,
									    0,
									    1611,
									    8,
									    5,
									    150,
									    2_6449,
									    2,
									    19,
									    348,
									    469,
									    3,
									    2595,
									    48,
									    2_0740,
									    24_6533,
									    24_6533,
									    19,
									    30,
									    5,
									]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

									SCREAMING_SNAKE_CASE_:			Dict  		=	model.generate(lowerCAmelCase__       ,						do_sample=lowerCAmelCase__)
									self.assertListEqual(output_ids[0].tolist()       ,						lowerCAmelCase__)









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


lowerCAmelCase							:  Tuple            =						{
    """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							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

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

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

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						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							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

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






