



"""simple docstring"""

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


lowercase__     :   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__":
	lowercase__     :   Tuple			   =  "hopper-medium-v2"
	lowercase__     :   List[Any]			   =  gym.make(env_name)

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

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

			# execute action in environment
			lowercase__   ,   lowercase__   ,   lowercase__   ,   lowercase__     :   Optional[int]			   =  env.step(denorm_actions)
			lowercase__     :   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())

			lowercase__     :   Dict			   =  next_observation
	except KeyboardInterrupt:
		pass

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






"""simple docstring"""

import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					LEDTokenizer
						_lowerCAmelCase							=					LEDTokenizerFast
						_lowerCAmelCase							=					True



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().setUp()
													__a		:		str									=       [
													    """l""",
													    """o""",
													    """w""",
													    """e""",
													    """r""",
													    """s""",
													    """t""",
													    """i""",
													    """d""",
													    """n""",
													    """\u0120""",
													    """\u0120l""",
													    """\u0120n""",
													    """\u0120lo""",
													    """\u0120low""",
													    """er""",
													    """\u0120lowest""",
													    """\u0120newer""",
													    """\u0120wider""",
													    """<unk>""",
													]
													__a		:		int									=       dict(zip(_lowercase						,     range(len(_lowercase							)							)							)							)
													__a		:		Optional[int]									=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
													__a		:		List[Any]									=       {"""unk_token""": """<unk>"""}

													__a		:		Union[str, Any]									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]							)
													__a		:		Union[str, Any]									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""merges_file"""]							)
													with open(self.vocab_file						,     """w"""						,     encoding="""utf-8"""							) as fp:
																				fp.write(json.dumps(_lowercase							) + """\n"""							)
													with open(self.merges_file						,     """w"""						,     encoding="""utf-8"""							) as fp:
																				fp.write("""\n""".join(_lowercase							)							)



						def     lowerCAmelCase__(self						,     **_lowercase							):





													'''simple docstring'''

													kwargs.update(self.special_tokens_map							)
													return self.tokenizer_class.from_pretrained(self.tmpdirname						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     **_lowercase							):





													'''simple docstring'''

													kwargs.update(self.special_tokens_map							)
													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													return "lower newer", "lower newer"



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""							)



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													__a		:		List[str]									=       [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Optional[int]									=       tokenizer(_lowercase						,     max_length=len(_lowercase							)						,     padding=_lowercase						,     return_tensors="""pt"""							)
																				self.assertIsInstance(_lowercase						,     _lowercase							)

																				self.assertEqual((2, 9)						,     batch.input_ids.shape							)
																				self.assertEqual((2, 9)						,     batch.attention_mask.shape							)
																				__a		:		Dict									=       batch.input_ids.tolist()[0]
																				self.assertListEqual(_lowercase						,     _lowercase							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Tuple									=       tokenizer(_lowercase						,     padding=_lowercase						,     return_tensors="""pt"""							)
																				self.assertIn("""input_ids"""						,     _lowercase							)
																				self.assertIn("""attention_mask"""						,     _lowercase							)
																				self.assertNotIn("""labels"""						,     _lowercase							)
																				self.assertNotIn("""decoder_attention_mask"""						,     _lowercase							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       [
													    """Summary of the text.""",
													    """Another summary.""",
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Dict									=       tokenizer(text_target=_lowercase						,     max_length=32						,     padding="""max_length"""						,     return_tensors="""pt"""							)
																				self.assertEqual(32						,     targets["""input_ids"""].shape[1]							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Optional[int]									=       tokenizer(
																				    ["""I am a small frog""" * 1024, """I am a small frog"""]						,     padding=_lowercase						,     truncation=_lowercase						,     return_tensors="""pt"""							)
																				self.assertIsInstance(_lowercase						,     _lowercase							)
																				self.assertEqual(batch.input_ids.shape						,     (2, 5122)							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       ["""A long paragraph for summarization."""]
													__a		:		Dict									=       [
													    """Summary of the text.""",
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		int									=       tokenizer(_lowercase						,     return_tensors="""pt"""							)
																				__a		:		Dict									=       tokenizer(text_target=_lowercase						,     return_tensors="""pt"""							)
																				__a		:		List[str]									=       inputs["""input_ids"""]
																				__a		:		List[Any]									=       targets["""input_ids"""]
																				self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
																				self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
																				self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
																				self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Optional[Any]									=       ["""Summary of the text.""", """Another summary."""]
																				__a		:		List[Any]									=       [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																				__a		:		Union[str, Any]									=       tokenizer(_lowercase						,     padding=_lowercase							)
																				__a		:		Tuple									=       [[0] * len(_lowercase							) for x in encoded_output["""input_ids"""]]
																				__a		:		Union[str, Any]									=       tokenizer.pad(_lowercase							)
																				self.assertSequenceEqual(outputs["""global_attention_mask"""]						,     _lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																											__a		:		Dict									=       self.rust_tokenizer_class.from_pretrained(_lowercase						,     **_lowercase							)
																											__a		:		Union[str, Any]									=       self.tokenizer_class.from_pretrained(_lowercase						,     **_lowercase							)
																											__a		:		Union[str, Any]									=       """A, <mask> AllenNLP sentence."""
																											__a		:		Dict									=       tokenizer_r.encode_plus(_lowercase						,     add_special_tokens=_lowercase						,     return_token_type_ids=_lowercase							)
																											__a		:		Tuple									=       tokenizer_p.encode_plus(_lowercase						,     add_special_tokens=_lowercase						,     return_token_type_ids=_lowercase							)
																											self.assertEqual(sum(tokens_r["""token_type_ids"""]							)						,     sum(tokens_p["""token_type_ids"""]							)							)
																											self.assertEqual(
																											    sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)						,     sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)						,     )

																											__a		:		Tuple									=       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
																											__a		:		Any									=       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)
																											self.assertSequenceEqual(tokens_p["""input_ids"""]						,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
																											self.assertSequenceEqual(tokens_r["""input_ids"""]						,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

																											self.assertSequenceEqual(
																											    _lowercase						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
																											self.assertSequenceEqual(
																											    _lowercase						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)

