
"""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


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


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

_a														=		5_0003
_a														=		5_0002






@require_sentencepiece
@require_tokenizers
class     _UpperCAmelCase(    lowerCamelCase      ,     unittest.TestCase	):
		lowercase__						=      PLBartTokenizer
		lowercase__						=      None
		lowercase__						=      False



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''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 [2_85, 46, 10, 1_70, 3_82]]				,		)

							_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, 6_02, 3_47, 3_47, 3_47, 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)       ->       Union[str, Any]:






							'''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 [2_85, 46, 10, 1_70, 3_82]]				,		)

							_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, 6_02, 3_47, 3_47, 3_47, 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     _UpperCAmelCase(    unittest.TestCase	):
		lowercase__						=      'uclanlp/plbart-python-en_XX'
		lowercase__						=      [
		    '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__						=      [
		    'Returns the maximum value of a b c.',
		    'Sums the values of a b c.',
		]
		lowercase__						=      [
		    1_34,
		    54_52,
		    3_34_60,
		    3_34_41,
		    3_34_63,
		    3_34_65,
		    3_34_63,
		    3_34_49,
		    9_88,
		    20,
		    3_34_56,
		    19,
		    3_34_56,
		    7_71,
		    39,
		    42_58,
		    8_89,
		    33_18,
		    3_34_41,
		    3_34_63,
		    3_34_65,
		    3_34_63,
		    3_34_49,
		    24_71,
		    2,
		    PYTHON_CODE,
		]



		@classmethod
		def 						UpperCAmelCase							(					cls)       ->       int:






							'''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)       ->       Optional[int]:






							'''simple docstring'''
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']				,		5_00_01)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']				,		5_00_02)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']				,		5_00_03)



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''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[Any]:






							'''simple docstring'''
							self.assertIn(__a				,		self.tokenizer.all_special_ids)
							_UpperCamelCase							 =  [EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 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)       ->       List[str]:






							'''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)       ->       Union[str, Any]:






							'''simple docstring'''
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__'''])				,		[5_00_04, 5_00_01])



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''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)       ->       Optional[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)       ->       str:






							'''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''': [[1_50, 2_42, 2, 5_00_03]],
							        '''attention_mask''': [[1, 1, 1, 1]],
							        # java
							        '''forced_bos_token_id''': 5_00_01,
							    }				,		)








"""simple docstring"""





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       Optional[int]:






							'''simple docstring'''
							super().__init__()
							_UpperCamelCase							 =  value_function
							_UpperCamelCase							 =  unet
							_UpperCamelCase							 =  scheduler
							_UpperCamelCase							 =  env
							_UpperCamelCase							 =  env.get_dataset()
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].mean()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].std()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  env.observation_space.shape[0]
							_UpperCamelCase							 =  env.action_space.shape[0]



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							return (x_in - self.means[key]) / self.stds[key]



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       List[str]:






							'''simple docstring'''
							return x_in * self.stds[key] + self.means[key]



		def 						UpperCAmelCase							(					self				,		__a)       ->       Union[str, Any]:






							'''simple docstring'''
							if type(__a) is dict:
												return {k: self.to_torch(__a) for k, v in x_in.items()}
							elif torch.is_tensor(__a):
												return x_in.to(self.unet.device)
							return torch.tensor(__a				,		device=self.unet.device)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       str:






							'''simple docstring'''
							for key, val in cond.items():
												_UpperCamelCase							 =  val.clone()
							return x_in



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  x.shape[0]
							_UpperCamelCase							 =  None
							for i in tqdm.tqdm(self.scheduler.timesteps):
												# create batch of timesteps to pass into model
												_UpperCamelCase							 =  torch.full((batch_size,)				,		__a				,		device=self.unet.device				,		dtype=torch.long)
												for _ in range(__a):
																	with torch.enable_grad():
																						x.requires_grad_()

																						# permute to match dimension for pre-trained models
																						_UpperCamelCase							 =  self.value_function(x.permute(0				,		2				,		1)				,		__a).sample
																						_UpperCamelCase							 =  torch.autograd.grad([y.sum()]				,		[x])[0]

																						_UpperCamelCase							 =  self.scheduler._get_variance(__a)
																						_UpperCamelCase							 =  torch.exp(0.5 * posterior_variance)
																						_UpperCamelCase							 =  model_std * grad

																	_UpperCamelCase							 =  0
																	_UpperCamelCase							 =  x.detach()
																	_UpperCamelCase							 =  x + scale * grad
																	_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)

												_UpperCamelCase							 =  self.unet(x.permute(0				,		2				,		1)				,		__a).sample.permute(0				,		2				,		1)

												# TODO: verify deprecation of this kwarg
												_UpperCamelCase							 =  self.scheduler.step(__a				,		__a				,		__a				,		predict_epsilon=__a)['''prev_sample''']

												# apply conditions to the trajectory (set the initial state)
												_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
												_UpperCamelCase							 =  self.to_torch(__a)
							return x, y




		def __call__(					self				,		__a				,		__a=64				,		__a=32				,		__a=2				,		__a=0.1)       ->       Optional[Any]:






							'''simple docstring'''
							# normalize the observations and create  batch dimension
							_UpperCamelCase							 =  self.normalize(__a				,		'''observations''')
							_UpperCamelCase							 =  obs[None].repeat(__a				,		axis=0)

							_UpperCamelCase							 =  {0: self.to_torch(__a)}
							_UpperCamelCase							 =  (batch_size, planning_horizon, self.state_dim + self.action_dim)

							# generate initial noise and apply our conditions (to make the trajectories start at current state)
							_UpperCamelCase							 =  randn_tensor(__a				,		device=self.unet.device)
							_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
							_UpperCamelCase							 =  self.to_torch(__a)

							# run the diffusion process
							_UpperCamelCase ,  _UpperCamelCase							 =  self.run_diffusion(__a				,		__a				,		__a				,		__a)

							# sort output trajectories by value
							_UpperCamelCase							 =  y.argsort(0				,		descending=__a).squeeze()
							_UpperCamelCase							 =  x[sorted_idx]
							_UpperCamelCase							 =  sorted_values[:, :, : self.action_dim]
							_UpperCamelCase							 =  actions.detach().cpu().numpy()
							_UpperCamelCase							 =  self.de_normalize(__a				,		key='''actions''')

							# select the action with the highest value
							if y is not None:
												_UpperCamelCase							 =  0
							else:
												# if we didn't run value guiding, select a random action
												_UpperCamelCase							 =  np.random.randint(0				,		__a)

							_UpperCamelCase							 =  denorm_actions[selected_index, 0]
							return denorm_actions






