
"""simple docstring"""




import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, 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


_UpperCAmelCase :			Any              =	get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
					from transformers.models.plbart.modeling_plbart import shift_tokens_right

_UpperCAmelCase :			List[Any]              =	50_003
_UpperCAmelCase :			Any              =	50_002


@require_sentencepiece
@require_tokenizers
class     lowercase (		__SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    PLBartTokenizer
		__lowercase	:	Tuple     					=    None
		__lowercase	:	str     					=    False



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCamelCase          =					PLBartTokenizer(_a    ,				language_codes='base'    ,				keep_accents=_a	)
				tokenizer.save_pretrained(self.tmpdirname	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					PLBartTokenizer(_a    ,				language_codes='base'    ,				keep_accents=_a	)

				UpperCamelCase          =					tokenizer.tokenize('This is a test'	)
				self.assertListEqual(_a    ,				['▁This', '▁is', '▁a', '▁t', 'est']	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(_a	)    ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,				)

				UpperCamelCase          =					tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
				self.assertListEqual(
				    _a    ,				[
				        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',
				        '.',
				    ]    ,				)
				UpperCamelCase          =					tokenizer.convert_tokens_to_ids(_a	)
				self.assertListEqual(
				    _a    ,				[
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]    ,				)

				UpperCamelCase          =					tokenizer.convert_ids_to_tokens(_a	)
				self.assertListEqual(
				    _a    ,				[
				        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>',
				        '.',
				    ]    ,				)

				UpperCamelCase          =					tokenizer.vocab_size
				UpperCamelCase          =					[tokenizer.convert_ids_to_tokens(_a	) for x in range(end - 4    ,				_a	)]

				self.assertListEqual(_a    ,				['__java__', '__python__', '__en_XX__', '<mask>']	)

				UpperCamelCase          =					'java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
				UpperCamelCase          =					tokenizer(_a	).input_ids
				self.assertEqual(
				    tokenizer.decode(_a    ,				skip_special_tokens=_a    ,				clean_up_tokenization_spaces=_a	)    ,				_a    ,				)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					PLBartTokenizer(_a    ,				language_codes='multi'    ,				keep_accents=_a	)

				UpperCamelCase          =					tokenizer.tokenize('This is a test'	)
				self.assertListEqual(_a    ,				['▁This', '▁is', '▁a', '▁t', 'est']	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(_a	)    ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,				)

				UpperCamelCase          =					tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
				self.assertListEqual(
				    _a    ,				[
				        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',
				        '.',
				    ]    ,				)
				UpperCamelCase          =					tokenizer.convert_tokens_to_ids(_a	)
				self.assertListEqual(
				    _a    ,				[
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]    ,				)

				UpperCamelCase          =					tokenizer.convert_ids_to_tokens(_a	)
				self.assertListEqual(
				    _a    ,				[
				        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>',
				        '.',
				    ]    ,				)
				UpperCamelCase          =					tokenizer.vocab_size
				UpperCamelCase          =					[tokenizer.convert_ids_to_tokens(_a	) for x in range(end - 7    ,				_a	)]

				self.assertListEqual(
				    _a    ,				['__java__', '__python__', '__en_XX__', '__javascript__', '__php__', '__ruby__', '__go__']	)
				UpperCamelCase          =					'java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
				UpperCamelCase          =					tokenizer(_a	).input_ids
				self.assertEqual(
				    tokenizer.decode(_a    ,				skip_special_tokens=_a    ,				clean_up_tokenization_spaces=_a	)    ,				_a    ,				)






@require_torch
@require_sentencepiece
@require_tokenizers
class     lowercase (		unittest.TestCase       ):
		__lowercase	:	List[Any]     					=    'uclanlp/plbart-python-en_XX'
		__lowercase	:	str     					=    [
		    'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
		    'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
		]
		__lowercase	:	str     					=    [
		    'Returns the maximum value of a b c.',
		    'Sums the values of a b c.',
		]
		__lowercase	:	Optional[int]     					=    [
		    134,
		    5_452,
		    33_460,
		    33_441,
		    33_463,
		    33_465,
		    33_463,
		    33_449,
		    988,
		    20,
		    33_456,
		    19,
		    33_456,
		    771,
		    39,
		    4_258,
		    889,
		    3_318,
		    33_441,
		    33_463,
		    33_465,
		    33_463,
		    33_449,
		    2_471,
		    2,
		    PYTHON_CODE,
		]



		@classmethod
		def __UpperCamelCase			(			cls	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					PLBartTokenizer.from_pretrained(
				    cls.checkpoint_name    ,				language_codes='base'    ,				src_lang='python'    ,				tgt_lang='en_XX'	)
				UpperCamelCase          =					1
				return cls



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__']    ,				50_001	)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__']    ,				50_002	)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__']    ,				50_003	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
				self.assertListEqual(self.expected_src_tokens    ,				_a	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				self.assertIn(_a    ,				self.tokenizer.all_special_ids	)
				UpperCamelCase          =					[EN_CODE, 9_037, 33_442, 57, 752, 153, 14, 56, 18, 9, 2]
				UpperCamelCase          =					self.tokenizer.decode(_a    ,				skip_special_tokens=_a	)
				UpperCamelCase          =					self.tokenizer.decode(generated_ids[1:]    ,				skip_special_tokens=_a	)
				self.assertEqual(_a    ,				_a	)
				self.assertNotIn(self.tokenizer.eos_token    ,				_a	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 20]
				self.assertIsInstance(src_text[0]    ,				_a	)
				UpperCamelCase          =					10
				UpperCamelCase          =					self.tokenizer(_a    ,				max_length=_a    ,				truncation=_a	).input_ids[0]
				self.assertEqual(ids[-2]    ,				2	)
				self.assertEqual(ids[-1]    ,				_a	)
				self.assertEqual(len(_a	)    ,				_a	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__']	)    ,				[50_004, 50_001]	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					tempfile.mkdtemp()
				UpperCamelCase          =					self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(_a	)
				UpperCamelCase          =					PLBartTokenizer.from_pretrained(_a	)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,				_a	)



		@require_torch
		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer(self.src_text    ,				text_target=self.tgt_text    ,				padding=_a    ,				return_tensors='pt'	)
				UpperCamelCase          =					shift_tokens_right(batch['labels']    ,				self.tokenizer.pad_token_id	)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				self.assertEqual(batch.input_ids[1][-2:].tolist()    ,				[2, PYTHON_CODE]	)
				self.assertEqual(batch.decoder_input_ids[1][0]    ,				_a	)
				self.assertEqual(batch.decoder_input_ids[1][-1]    ,				2	)
				self.assertEqual(batch.labels[1][-2:].tolist()    ,				[2, EN_CODE]	)



		@require_torch
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer(
				    self.src_text    ,				text_target=self.tgt_text    ,				padding=_a    ,				truncation=_a    ,				max_length=len(self.expected_src_tokens	)    ,				return_tensors='pt'    ,				)
				UpperCamelCase          =					shift_tokens_right(batch['labels']    ,				self.tokenizer.pad_token_id	)

				self.assertIsInstance(_a    ,				_a	)

				self.assertEqual((2, 26)    ,				batch.input_ids.shape	)
				self.assertEqual((2, 26)    ,				batch.attention_mask.shape	)
				UpperCamelCase          =					batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens    ,				_a	)
				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, PYTHON_CODE]	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer(self.src_text    ,				padding=_a    ,				truncation=_a    ,				max_length=3    ,				return_tensors='pt'	)
				UpperCamelCase          =					self.tokenizer(
				    text_target=self.tgt_text    ,				padding=_a    ,				truncation=_a    ,				max_length=10    ,				return_tensors='pt'	)
				UpperCamelCase          =					targets['input_ids']
				UpperCamelCase          =					shift_tokens_right(_a    ,				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 __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer._build_translation_inputs(
				    'A test'    ,				return_tensors='pt'    ,				src_lang='en_XX'    ,				tgt_lang='java'	)

				self.assertEqual(
				    nested_simplify(_a	)    ,				{
				        # A, test, EOS, en_XX
				        'input_ids': [[150, 242, 2, 50_003]],
				        'attention_mask': [[1, 1, 1, 1]],
				        # java
				        'forced_bos_token_id': 50_001,
				    }    ,				)


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


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

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

					env.seed(0)
					_UpperCAmelCase :			Optional[Any]              =	env.reset()
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Dict              =	1_000
					_UpperCAmelCase :			Tuple              =	[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase :			int              =	pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase :			List[Any]              =	env.step(denorm_actions)
															_UpperCAmelCase :			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())

															_UpperCAmelCase :			Union[str, Any]              =	next_observation
					except KeyboardInterrupt:
										pass

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