






"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						)







class        lowercase__      :


			def __init__(				self					:  List[str]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  str=13       ,	snake_case__					:  Tuple=7       ,	snake_case__					:  Optional[Any]=True       ,	snake_case__					:  Tuple=True       ,	snake_case__					:  Tuple=True       ,	snake_case__					:  List[Any]=True       ,	snake_case__					:  Optional[int]=99       ,	snake_case__					:  List[Any]=32       ,	snake_case__					:  int=2       ,	snake_case__					:  List[Any]=4       ,	snake_case__					:  List[str]=37       ,	snake_case__					:  Tuple="gelu"       ,	snake_case__					:  Optional[int]=0.1       ,	snake_case__					:  List[str]=0.1       ,	snake_case__					:  Any=512       ,	snake_case__					:  Any=16       ,	snake_case__					:  List[str]=2       ,	snake_case__					:  Optional[Any]=0.02       ,	snake_case__					:  Dict=False       ,	snake_case__					:  Tuple=True       ,	snake_case__					:  Dict="None"       ,	snake_case__					:  int=3       ,	snake_case__					:  Tuple=4       ,	snake_case__					:  Optional[Any]=None       ,	):
										lowerCamelCase_	:							str									=parent
										lowerCamelCase_	:							Dict									=batch_size
										lowerCamelCase_	:							Optional[Any]									=seq_length
										lowerCamelCase_	:							Dict									=is_training
										lowerCamelCase_	:							Optional[Any]									=use_input_mask
										lowerCamelCase_	:							int									=use_token_type_ids
										lowerCamelCase_	:							str									=use_labels
										lowerCamelCase_	:							Any									=vocab_size
										lowerCamelCase_	:							Optional[Any]									=hidden_size
										lowerCamelCase_	:							List[str]									=num_hidden_layers
										lowerCamelCase_	:							Union[str, Any]									=num_attention_heads
										lowerCamelCase_	:							Optional[int]									=intermediate_size
										lowerCamelCase_	:							str									=hidden_act
										lowerCamelCase_	:							Optional[Any]									=hidden_dropout_prob
										lowerCamelCase_	:							Dict									=attention_probs_dropout_prob
										lowerCamelCase_	:							str									=max_position_embeddings
										lowerCamelCase_	:							Union[str, Any]									=type_vocab_size
										lowerCamelCase_	:							Tuple									=type_sequence_label_size
										lowerCamelCase_	:							List[str]									=initializer_range
										lowerCamelCase_	:							int									=num_labels
										lowerCamelCase_	:							Union[str, Any]									=num_choices
										lowerCamelCase_	:							int									=relative_attention
										lowerCamelCase_	:							Dict									=position_biased_input
										lowerCamelCase_	:							Dict									=pos_att_type
										lowerCamelCase_	:							Tuple									=scope


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

										lowerCamelCase_	:							Optional[int]									=None
										if self.use_input_mask:
																	lowerCamelCase_	:							Optional[int]									=random_attention_mask([self.batch_size, self.seq_length] )

										lowerCamelCase_	:							Any									=None
										if self.use_token_type_ids:
																	lowerCamelCase_	:							Union[str, Any]									=ids_tensor([self.batch_size, self.seq_length]       ,	self.type_vocab_size )

										lowerCamelCase_	:							Any									=None
										lowerCamelCase_	:							Union[str, Any]									=None
										lowerCamelCase_	:							Dict									=None
										if self.use_labels:
																	lowerCamelCase_	:							Dict									=ids_tensor([self.batch_size]       ,	self.type_sequence_label_size )
																	lowerCamelCase_	:							Optional[Any]									=ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels )

										lowerCamelCase_	:							Optional[Any]									=DebertaVaConfig(
										    vocab_size=self.vocab_size       ,	hidden_size=self.hidden_size       ,	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       ,	relative_attention=self.relative_attention       ,	position_biased_input=self.position_biased_input       ,	initializer_range=self.initializer_range       ,	return_dict=snake_case__       ,	)

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


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Tuple       ,	snake_case__					:  Any       ,	snake_case__					:  Tuple       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  Optional[int] ):
										lowerCamelCase_	:							List[Any]									=TFDebertaVaModel(config=snake_case__ )
										lowerCamelCase_	:							List[Any]									={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										lowerCamelCase_	:							List[str]									=[input_ids, input_mask]
										lowerCamelCase_	:							Union[str, Any]									=model(snake_case__ )

										lowerCamelCase_	:							Any									=model(snake_case__ )

										self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size) )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[str]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  int       ,	snake_case__					:  int       ,	snake_case__					:  str ):
										lowerCamelCase_	:							Any									=TFDebertaVaForMaskedLM(config=snake_case__ )
										lowerCamelCase_	:							Optional[Any]									={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										lowerCamelCase_	:							List[str]									=model(snake_case__ )
										self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size) )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  Any       ,	snake_case__					:  List[str]       ,	snake_case__					:  str       ,	snake_case__					:  Dict       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  int ):
										lowerCamelCase_	:							str									=self.num_labels
										lowerCamelCase_	:							Optional[Any]									=TFDebertaVaForSequenceClassification(config=snake_case__ )
										lowerCamelCase_	:							List[Any]									={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										lowerCamelCase_	:							Dict									=model(snake_case__ )
										self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels) )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  str       ,	snake_case__					:  int ):
										lowerCamelCase_	:							List[Any]									=self.num_labels
										lowerCamelCase_	:							Dict									=TFDebertaVaForTokenClassification(config=snake_case__ )
										lowerCamelCase_	:							int									={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										lowerCamelCase_	:							Tuple									=model(snake_case__ )
										self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels) )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  int       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]       ,	snake_case__					:  Union[str, Any] ):
										lowerCamelCase_	:							Optional[int]									=TFDebertaVaForQuestionAnswering(config=snake_case__ )
										lowerCamelCase_	:							int									={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										lowerCamelCase_	:							Any									=model(snake_case__ )
										self.parent.assertEqual(result.start_logits.shape       ,	(self.batch_size, self.seq_length) )
										self.parent.assertEqual(result.end_logits.shape       ,	(self.batch_size, self.seq_length) )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							str									=self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						
										)	:							str									=config_and_inputs
										lowerCamelCase_	:							Dict									={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_tf
class        lowercase__      (							snake_case__,       snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:List[str]             =			(
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			_UpperCAmelCase		:Optional[int]             =			(
			    {
			        "feature-extraction": TFDebertaVaModel,
			        "fill-mask": TFDebertaVaForMaskedLM,
			        "question-answering": TFDebertaVaForQuestionAnswering,
			        "text-classification": TFDebertaVaForSequenceClassification,
			        "token-classification": TFDebertaVaForTokenClassification,
			        "zero-shot": TFDebertaVaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			_UpperCAmelCase		:Tuple             =			False
			_UpperCAmelCase		:Union[str, Any]             =			False


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Union[str, Any]									=TFDebertaVaModelTester(self )
										lowerCamelCase_	:							Optional[int]									=ConfigTester(self       ,	config_class=snake_case__       ,	hidden_size=37 )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										self.config_tester.run_common_tests()


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Any									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[int]									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*snake_case__ )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							int									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Optional[Any]									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*snake_case__ )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							int									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*snake_case__ )


			@slow
			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Tuple									=TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
										self.assertIsNotNone(snake_case__ )







@require_tf
class        lowercase__      (							unittest.TestCase       ):


			@unittest.skip(reason="Model not available yet" )
			def      UpperCAmelCase__						(				self					:  List[str] ):
										pass


			@slow
			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							int									=TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
										lowerCamelCase_	:							Union[str, Any]									=tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
										lowerCamelCase_	:							Any									=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
										lowerCamelCase_	:							str									=model(snake_case__       ,	attention_mask=snake_case__ )[0]

										lowerCamelCase_	:							int									=tf.constant(
										    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
										tf.debugging.assert_near(output[:, 1:4, 1:4]       ,	snake_case__       ,	atol=1E-4 )











"""simple docstring"""



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


A__	:    int			 = {
    '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__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

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

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [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__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = 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__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

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



