



'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			tempfile.mkdtemp()

													# fmt: off
													SCREAMING_SNAKE_CASE					:      Any     					=			['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
													# fmt: on
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']				)
													with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''				) as vocab_writer:
																				vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)

													SCREAMING_SNAKE_CASE					:      int     					=			{
													    '''do_resize''': True,
													    '''size''': {'''height''': 18, '''width''': 18},
													    '''do_normalize''': True,
													    '''image_mean''': [0.5, 0.5, 0.5],
													    '''image_std''': [0.5, 0.5, 0.5],
													}
													SCREAMING_SNAKE_CASE					:      str     					=			os.path.join(self.tmpdirname				,    lowerCAmelCase__				)
													with open(self.image_processor_file				,    '''w'''				,    encoding='''utf-8'''				) as fp:
																				json.dump(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return BertTokenizer.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													return ViTImageProcessor.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			[np.random.randint(2_55				,    size=(3, 30, 4_00)				,    dtype=np.uinta				)]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			[Image.fromarray(np.moveaxis(lowerCAmelCase__				,    0				,    -1				)				) for x in image_inputs]

													return image_inputs







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_image_processor()

													SCREAMING_SNAKE_CASE					:      str     					=			VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													processor.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname				)

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,    (BertTokenizer, BertTokenizerFast)				)

													self.assertEqual(processor.image_processor.to_json_string()				,    image_processor.to_json_string()				)
													self.assertIsInstance(processor.image_processor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			VisionTextDualEncoderProcessor(
													    tokenizer=self.get_tokenizer()				,    image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer(bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.get_image_processor(do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			VisionTextDualEncoderProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				,    do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,    (BertTokenizer, BertTokenizerFast)				)

													self.assertEqual(processor.image_processor.to_json_string()				,    image_processor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.image_processor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Any     					=			VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_processor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			processor(images=lowerCAmelCase__				,    return_tensors='''np'''				)

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      int     					=			VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			'''lower newer'''

													SCREAMING_SNAKE_CASE					:      List[str]     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer(lowerCAmelCase__				)

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      str     					=			VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			'''lower newer'''
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,    ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']				)

													# test if it raises when no input is passed
													with self.assertRaises(lowerCAmelCase__				):
																				processor()







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Dict     					=			VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      int     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''lower newer'''
													SCREAMING_SNAKE_CASE					:      int     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      int     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,    processor.model_input_names				)









'''simple docstring'''



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


lowerCAmelCase_     :       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__":
							lowerCAmelCase_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

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

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

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						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_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




