"""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        _snake_case   (    UpperCamelCase_						):






						def __init__(				self					:	Union[str, Any]							,    UpperCAmelCase					:	UNetaDModel							,    UpperCAmelCase					:	UNetaDModel							,    UpperCAmelCase					:	DDPMScheduler							,    UpperCAmelCase					:	Dict							,    ):
													super().__init__()
													__lowerCamelCase    :						int						      =   value_function
													__lowerCamelCase    :						List[str]						      =   unet
													__lowerCamelCase    :						Tuple						      =   scheduler
													__lowerCamelCase    :						Dict						      =   env
													__lowerCamelCase    :						Optional[int]						      =   env.get_dataset()
													__lowerCamelCase    :						str						      =   {}
													for key in self.data.keys():
																				try:
																											__lowerCamelCase    :						Dict						      =   self.data[key].mean()
																				except:  # noqa: E722
																											pass
													__lowerCamelCase    :						Union[str, Any]						      =   {}
													for key in self.data.keys():
																				try:
																											__lowerCamelCase    :						List[Any]						      =   self.data[key].std()
																				except:  # noqa: E722
																											pass
													__lowerCamelCase    :						str						      =   env.observation_space.shape[0]
													__lowerCamelCase    :						Optional[int]						      =   env.action_space.shape[0]






						def 					lowerCamelCase__   (				self					:	Any							,    UpperCAmelCase					:	List[Any]							,    UpperCAmelCase					:	int			):
													return (x_in - self.means[key]) / self.stds[key]






						def 					lowerCamelCase__   (				self					:	Optional[Any]							,    UpperCAmelCase					:	List[Any]							,    UpperCAmelCase					:	str			):
													return x_in * self.stds[key] + self.means[key]






						def 					lowerCamelCase__   (				self					:	str							,    UpperCAmelCase					:	Optional[int]			):




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






						def 					lowerCamelCase__   (				self					:	int							,    UpperCAmelCase					:	Optional[Any]							,    UpperCAmelCase					:	Union[str, Any]							,    UpperCAmelCase					:	str			):




													for key, val in cond.items():
																				__lowerCamelCase    :						List[str]						      =   val.clone()
													return x_in






						def 					lowerCamelCase__   (				self					:	Any							,    UpperCAmelCase					:	Optional[int]							,    UpperCAmelCase					:	Any							,    UpperCAmelCase					:	Dict							,    UpperCAmelCase					:	Optional[Any]			):
													__lowerCamelCase    :						Union[str, Any]						      =   x.shape[0]
													__lowerCamelCase    :						Optional[int]						      =   None
													for i in tqdm.tqdm(self.scheduler.timesteps			):
																				# create batch of timesteps to pass into model
																				__lowerCamelCase    :						int						      =   torch.full((batch_size,)							,    lowercase_							,    device=self.unet.device							,    dtype=torch.long			)
																				for _ in range(lowercase_			):
																											with torch.enable_grad():
																																		x.requires_grad_()

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

																																		__lowerCamelCase    :						str						      =   self.scheduler._get_variance(lowercase_			)
																																		__lowerCamelCase    :						str						      =   torch.exp(0.5 * posterior_variance			)
																																		__lowerCamelCase    :						List[str]						      =   model_std * grad

																											__lowerCamelCase    :						Any						      =   0
																											__lowerCamelCase    :						Optional[int]						      =   x.detach()
																											__lowerCamelCase    :						Tuple						      =   x + scale * grad
																											__lowerCamelCase    :						Optional[int]						      =   self.reset_xa(lowercase_							,    lowercase_							,    self.action_dim			)

																				__lowerCamelCase    :						List[str]						      =   self.unet(x.permute(0							,    2							,    1			)							,    lowercase_			).sample.permute(0							,    2							,    1			)

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

																				# apply conditions to the trajectory (set the initial state)
																				__lowerCamelCase    :						int						      =   self.reset_xa(lowercase_							,    lowercase_							,    self.action_dim			)
																				__lowerCamelCase    :						List[str]						      =   self.to_torch(lowercase_			)
													return x, y







						def __call__(				self					:	Dict							,    UpperCAmelCase					:	Any							,    UpperCAmelCase					:	Union[str, Any]=64							,    UpperCAmelCase					:	Union[str, Any]=32							,    UpperCAmelCase					:	Any=2							,    UpperCAmelCase					:	Tuple=0.1			):
													__lowerCamelCase    :						List[str]						      =   self.normalize(lowercase_							,    "observations"			)
													__lowerCamelCase    :						List[str]						      =   obs[None].repeat(lowercase_							,    axis=0			)

													__lowerCamelCase    :						List[str]						      =   {0: self.to_torch(lowercase_			)}
													__lowerCamelCase    :						Dict						      =   (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)
													__lowerCamelCase    :						Dict						      =   randn_tensor(lowercase_							,    device=self.unet.device			)
													__lowerCamelCase    :						int						      =   self.reset_xa(lowercase_							,    lowercase_							,    self.action_dim			)
													__lowerCamelCase    :						Dict						      =   self.to_torch(lowercase_			)

													# run the diffusion process
													__lowerCamelCase    :						Any						      =   self.run_diffusion(lowercase_							,    lowercase_							,    lowercase_							,    lowercase_			)

													# sort output trajectories by value
													__lowerCamelCase    :						List[Any]						      =   y.argsort(0							,    descending=lowercase_			).squeeze()
													__lowerCamelCase    :						Any						      =   x[sorted_idx]
													__lowerCamelCase    :						str						      =   sorted_values[:, :, : self.action_dim]
													__lowerCamelCase    :						Any						      =   actions.detach().cpu().numpy()
													__lowerCamelCase    :						List[str]						      =   self.de_normalize(lowercase_							,    key="actions"			)

													# select the action with the highest value
													if y is not None:
																				__lowerCamelCase    :						Dict						      =   0
													else:
																				# if we didn't run value guiding, select a random action
																				__lowerCamelCase    :						Optional[int]						      =   np.random.randint(0							,    lowercase_			)

													__lowerCamelCase    :						List[str]						      =   denorm_actions[selected_index, 0]
													return denorm_actions



import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase											=       get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  str      =XLMProphetNetTokenizer
      UpperCamelCase__							:  Any      =False
      UpperCamelCase__							:  Optional[Any]      =True






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[Any]:
             """simple docstring"""




             super().setUp()

             # We have a SentencePiece fixture for testing
             _lowerCamelCase       :		Union[str, Any]		=XLMProphetNetTokenizer(lowercase_			,   keep_accents=lowercase_ )
             tokenizer.save_pretrained(self.tmpdirname )






      def        lowerCamelCase       (					self						:     int )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		='[PAD]'
             _lowerCamelCase       :		Dict		=0

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ )			,   lowercase_ )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ )			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		int		=list(self.get_tokenizer().get_vocab().keys() )

             self.assertEqual(vocab_keys[0]			,   '[PAD]' )
             self.assertEqual(vocab_keys[1]			,   '[CLS]' )
             self.assertEqual(vocab_keys[-1]			,   'j' )
             self.assertEqual(len(lowercase_ )			,   1012 )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Any:
             """simple docstring"""




             self.assertEqual(self.get_tokenizer().vocab_size			,   1012 )






      def        lowerCamelCase       (					self						:     Dict )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=XLMProphetNetTokenizer(lowercase_			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		Any		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

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

             _lowerCamelCase       :		Dict		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     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       :		Union[str, Any]		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
                 ]			,   )

             _lowerCamelCase       :		int		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     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]',
                     '.',
                 ]			,   )






      @cached_property
      def        lowerCamelCase       (					self						:     Any )					->						Optional[Any]:
             """simple docstring"""




             return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased' )






      @slow
      def        lowerCamelCase       (					self						:     List[str] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		='Hello World!'
             _lowerCamelCase       :		Optional[int]		=[3_5389, 6672, 49, 2]
             self.assertListEqual(lowercase_			,   self.big_tokenizer.encode(lowercase_ ) )






      @slow
      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Dict		={'input_ids': [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase_			,   model_name='microsoft/xprophetnet-large-wiki100-cased'			,   revision='1acad1643ddd54a44df6a1b797ada8373685d90e'			,   )


