
"""simple docstring"""







import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__           =	get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCamelCase__           =	250_004
lowerCamelCase__           =	250_020




@require_sentencepiece
@require_tokenizers
class 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: List[str]            =			MBartTokenizer
		A_		: Union[str, Any]            =			MBartTokenizerFast
		A_		: Any            =			True
		A_		: int            =			True

		def 							__lowerCamelCase							(   self				):
			super().setUp()

			# We have a SentencePiece fixture for testing
			__lowerCAmelCase    :		Tuple		    =   MBartTokenizer(_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE				)
			tokenizer.save_pretrained(self.tmpdirname				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   MBartTokenizer(_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   tokenizer.tokenize('This is a test'				)
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	['▁This', '▁is', '▁a', '▁t', 'est']				)

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				) ,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,	)

			__lowerCAmelCase    :		Any		    =   tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        SPIECE_UNDERLINE + 'I',
			        SPIECE_UNDERLINE + 'was',
			        SPIECE_UNDERLINE + 'b',
			        'or',
			        'n',
			        SPIECE_UNDERLINE + 'in',
			        SPIECE_UNDERLINE + '',
			        '9',
			        '2',
			        '0',
			        '0',
			        '0',
			        ',',
			        SPIECE_UNDERLINE + 'and',
			        SPIECE_UNDERLINE + 'this',
			        SPIECE_UNDERLINE + 'is',
			        SPIECE_UNDERLINE + 'f',
			        'al',
			        's',
			        'é',
			        '.',
			    ] ,	)
			__lowerCAmelCase    :		str		    =   tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        value + tokenizer.fairseq_offset
			        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
			        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
			    ] ,	)

			__lowerCAmelCase    :		List[str]		    =   tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        SPIECE_UNDERLINE + 'I',
			        SPIECE_UNDERLINE + 'was',
			        SPIECE_UNDERLINE + 'b',
			        'or',
			        'n',
			        SPIECE_UNDERLINE + 'in',
			        SPIECE_UNDERLINE + '',
			        '<unk>',
			        '2',
			        '0',
			        '0',
			        '0',
			        ',',
			        SPIECE_UNDERLINE + 'and',
			        SPIECE_UNDERLINE + 'this',
			        SPIECE_UNDERLINE + 'is',
			        SPIECE_UNDERLINE + 'f',
			        'al',
			        's',
			        '<unk>',
			        '.',
			    ] ,	)







		def 							__lowerCamelCase							(   self				):
			if not self.test_slow_tokenizer:
				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
				return

			__lowerCAmelCase    :		Union[str, Any]		    =   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
					__lowerCAmelCase    :		Dict		    =   self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		int		    =   self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		Optional[int]		    =   tempfile.mkdtemp()

					__lowerCAmelCase    :		int		    =   tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Dict		    =   tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

					# Checks it save with the same files + the tokenizer.json file for the fast one
					self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)
					__lowerCAmelCase    :		List[Any]		    =   tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f				)
					self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					# Checks everything loads correctly in the same way
					__lowerCAmelCase    :		Any		    =   tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Dict		    =   tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)
						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

					shutil.rmtree(_SCREAMING_SNAKE_CASE				)

					# Save tokenizer rust, legacy_format=True
					__lowerCAmelCase    :		List[str]		    =   tempfile.mkdtemp()

					__lowerCAmelCase    :		Tuple		    =   tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,	legacy_format=_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Optional[Any]		    =   tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

					# Checks it save with the same files
					self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					# Checks everything loads correctly in the same way
					__lowerCAmelCase    :		Any		    =   tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		str		    =   tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

					shutil.rmtree(_SCREAMING_SNAKE_CASE				)

					# Save tokenizer rust, legacy_format=False
					__lowerCAmelCase    :		List[Any]		    =   tempfile.mkdtemp()

					__lowerCAmelCase    :		Optional[Any]		    =   tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,	legacy_format=_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Union[str, Any]		    =   tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

					# Checks it saved the tokenizer.json file
					self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)

					# Checks everything loads correctly in the same way
					__lowerCAmelCase    :		Union[str, Any]		    =   tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Any		    =   tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

					shutil.rmtree(_SCREAMING_SNAKE_CASE				)




@require_torch
@require_sentencepiece
@require_tokenizers
class 				A__ (			unittest.TestCase):
		A_		: Union[str, Any]            =			'facebook/mbart-large-en-ro'
		A_		: List[Any]            =			[
		    ' UN Chief Says There Is No Military Solution in Syria',
		    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
		]
		A_		: Optional[int]            =			[
		    'Şeful ONU declară că nu există o soluţie militară în Siria',
		    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
		    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
		    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
		]
		A_		: Dict            =			[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]

		@classmethod
		def 							__lowerCamelCase							(   cls				):
			__lowerCAmelCase    :		MBartTokenizer		    =   MBartTokenizer.from_pretrained(
			    cls.checkpoint_name ,	src_lang='en_XX' ,	tgt_lang='ro_RO'				)
			__lowerCAmelCase    :		List[Any]		    =   1
			return cls

		def 							__lowerCamelCase							(   self				):
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,	25_00_01				)
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,	25_00_04				)
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,	25_00_20				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
			self.assertListEqual(self.expected_src_tokens ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self.assertIn(_SCREAMING_SNAKE_CASE ,	self.tokenizer.all_special_ids				)
			__lowerCAmelCase    :		Union[str, Any]		    =   [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
			__lowerCAmelCase    :		List[str]		    =   self.tokenizer.decode(_SCREAMING_SNAKE_CASE ,	skip_special_tokens=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.tokenizer.decode(generated_ids[1:] ,	skip_special_tokens=_SCREAMING_SNAKE_CASE				)
			self.assertEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			self.assertNotIn(self.tokenizer.eos_token ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ['this is gunna be a long sentence ' * 20]
			assert isinstance(src_text[0] ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		int		    =   10
			__lowerCAmelCase    :		int		    =   self.tokenizer(_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE				).input_ids[0]
			self.assertEqual(ids[-2] ,	2				)
			self.assertEqual(ids[-1] ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']				) ,	[25_00_26, 25_00_01]				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   tempfile.mkdtemp()
			__lowerCAmelCase    :		Tuple		    =   self.tokenizer.fairseq_tokens_to_ids
			self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   MBartTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
			self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,	_SCREAMING_SNAKE_CASE				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.tokenizer(self.src_text ,	text_target=self.tgt_text ,	padding=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				)
			__lowerCAmelCase    :		Optional[int]		    =   shift_tokens_right(batch['labels'] ,	self.tokenizer.pad_token_id				)

			# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
			assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
			assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
			assert batch.decoder_input_ids[1][-1] == 2
			assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.tokenizer(
			    self.src_text ,	text_target=self.tgt_text ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=len(self.expected_src_tokens				) ,	return_tensors='pt' ,	)

			__lowerCAmelCase    :		Dict		    =   shift_tokens_right(batch['labels'] ,	self.tokenizer.pad_token_id				)

			self.assertIsInstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			self.assertEqual((2, 14) ,	batch.input_ids.shape				)
			self.assertEqual((2, 14) ,	batch.attention_mask.shape				)
			__lowerCAmelCase    :		Union[str, Any]		    =   batch.input_ids.tolist()[0]
			self.assertListEqual(self.expected_src_tokens ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(2 ,	batch.decoder_input_ids[0, -1]				)  # EOS
			# Test that special tokens are reset
			self.assertEqual(self.tokenizer.prefix_tokens ,	[]				)
			self.assertEqual(self.tokenizer.suffix_tokens ,	[self.tokenizer.eos_token_id, EN_CODE]				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.tokenizer(self.src_text ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=3 ,	return_tensors='pt'				)
			__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
			    text_target=self.tgt_text ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=10 ,	return_tensors='pt'				)
			__lowerCAmelCase    :		Dict		    =   targets['input_ids']
			__lowerCAmelCase    :		Optional[Any]		    =   shift_tokens_right(_SCREAMING_SNAKE_CASE ,	self.tokenizer.pad_token_id				)

			self.assertEqual(batch.input_ids.shape[1] ,	3				)
			self.assertEqual(batch.decoder_input_ids.shape[1] ,	10				)







		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.tokenizer._build_translation_inputs(
			    'A test' ,	return_tensors='pt' ,	src_lang='en_XX' ,	tgt_lang='ar_AR'				)

			self.assertEqual(
			    nested_simplify(_SCREAMING_SNAKE_CASE				) ,	{
			        # A, test, EOS, en_XX
			        'input_ids': [[62, 30_34, 2, 25_00_04]],
			        'attention_mask': [[1, 1, 1, 1]],
			        # ar_AR
			        'forced_bos_token_id': 25_00_01,
			    } ,	)

"""simple docstring"""







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


lowerCamelCase__           =	{
    """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__           =	"""hopper-medium-v2"""
							lowerCamelCase__           =	gym.make(env_name)

							lowerCamelCase__           =	ValueGuidedRLPipeline.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32""",
							    env=env,
							)

							env.seed(0)
							lowerCamelCase__           =	env.reset()
							lowerCamelCase__           =	0
							lowerCamelCase__           =	0
							lowerCamelCase__           =	1_000
							lowerCamelCase__           =	[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCamelCase__           =	pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__           =	env.step(denorm_actions)
																					lowerCamelCase__           =	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__           =	next_observation
							except KeyboardInterrupt:
														pass

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