

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 					__lowercase  (						UpperCAmelCase__				):





							"""simple docstring"""

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




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       List[str]:
														return (x_in - self.means[key]) / self.stds[key]




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														return x_in * self.stds[key] + self.means[key]




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Dict:

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




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:

														for key, val in cond.items():
																					A      :					Optional[Any]      		=					val.clone()
														return x_in




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       List[Any]:
														A      :					int      		=					x.shape[0]
														A      :					Dict      		=					None
														for i in tqdm.tqdm(self.scheduler.timesteps						):
																					# create batch of timesteps to pass into model
																					A      :					int      		=					torch.full((batch_size,)    ,						UpperCAmelCase__    ,						device=self.unet.device    ,						dtype=torch.long						)
																					for _ in range(UpperCAmelCase__						):
																												with torch.enable_grad():
																																			x.requires_grad_()

																																			# permute to match dimension for pre-trained models
																																			A      :					Tuple      		=					self.value_function(x.permute(0    ,						2    ,						1						)    ,						UpperCAmelCase__						).sample
																																			A      :					Union[str, Any]      		=					torch.autograd.grad([y.sum()]    ,						[x]						)[0]

																																			A      :					str      		=					self.scheduler._get_variance(UpperCAmelCase__						)
																																			A      :					str      		=					torch.exp(0.5 * posterior_variance						)
																																			A      :					Optional[Any]      		=					model_std * grad

																												A      :					Optional[int]      		=					0
																												A      :					str      		=					x.detach()
																												A      :					Dict      		=					x + scale * grad
																												A      :					int      		=					self.reset_xa(UpperCAmelCase__    ,						UpperCAmelCase__    ,						self.action_dim						)

																					A      :					str      		=					self.unet(x.permute(0    ,						2    ,						1						)    ,						UpperCAmelCase__						).sample.permute(0    ,						2    ,						1						)

																					# TODO: verify deprecation of this kwarg
																					A      :					Optional[int]      		=					self.scheduler.step(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__    ,						predict_epsilon=UpperCAmelCase__						)['''prev_sample''']

																					# apply conditions to the trajectory (set the initial state)
																					A      :					List[Any]      		=					self.reset_xa(UpperCAmelCase__    ,						UpperCAmelCase__    ,						self.action_dim						)
																					A      :					List[Any]      		=					self.to_torch(UpperCAmelCase__						)
														return x, y






							def __call__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=64    ,						__UpperCAmelCase=32    ,						__UpperCAmelCase=2    ,						__UpperCAmelCase=0.1						)		->       List[str]:
														A      :					Any      		=					self.normalize(UpperCAmelCase__    ,						'''observations'''						)
														A      :					Tuple      		=					obs[None].repeat(UpperCAmelCase__    ,						axis=0						)

														A      :					Union[str, Any]      		=					{0: self.to_torch(UpperCAmelCase__						)}
														A      :					Optional[Any]      		=					(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)
														A      :					Union[str, Any]      		=					randn_tensor(UpperCAmelCase__    ,						device=self.unet.device						)
														A      :					Union[str, Any]      		=					self.reset_xa(UpperCAmelCase__    ,						UpperCAmelCase__    ,						self.action_dim						)
														A      :					Any      		=					self.to_torch(UpperCAmelCase__						)

														# run the diffusion process
														A		,		A      :					Any      		=					self.run_diffusion(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__						)

														# sort output trajectories by value
														A      :					Optional[int]      		=					y.argsort(0    ,						descending=UpperCAmelCase__						).squeeze()
														A      :					Any      		=					x[sorted_idx]
														A      :					Tuple      		=					sorted_values[:, :, : self.action_dim]
														A      :					List[Any]      		=					actions.detach().cpu().numpy()
														A      :					Union[str, Any]      		=					self.de_normalize(UpperCAmelCase__    ,						key='''actions'''						)

														# select the action with the highest value
														if y is not None:
																					A      :					str      		=					0
														else:
																					# if we didn't run value guiding, select a random action
																					A      :					List[Any]      		=					np.random.randint(0    ,						UpperCAmelCase__						)

														A      :					Optional[Any]      		=					denorm_actions[selected_index, 0]
														return denorm_actions












import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase      :						Dict							=  get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_sentencepiece_available():
  import sentencepiece as sp


_lowerCamelCase      :						str							=  5
_lowerCamelCase      :						int							=  10


@require_sentencepiece
@require_tokenizers
class 							UpperCamelCase_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = SpeechaTextTokenizer
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = True


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->List[str]:





           '''simple docstring'''

           super().setUp()

           A__ 					=       sp.SentencePieceProcessor()
           spm_model.Load(UpperCAmelCase__)
           A__ 					=       ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']

           vocab += [spm_model.IdToPiece(id_) for id_ in range(len(UpperCAmelCase__))]
           A__ 					=       dict(zip(UpperCAmelCase__   ,					range(len(UpperCAmelCase__))))

           A__ 					=       Path(self.tmpdirname)
           save_json(UpperCAmelCase__   ,					save_dir / VOCAB_FILES_NAMES['''vocab_file'''])
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                  copyfile(UpperCAmelCase__   ,					save_dir / VOCAB_FILES_NAMES['''spm_file'''])

           A__ 					=       SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
           tokenizer.save_pretrained(self.tmpdirname)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       '''<pad>'''
           A__ 					=       1

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Any:





           '''simple docstring'''

           A__ 					=       list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]   ,					'''<s>''')
           self.assertEqual(vocab_keys[1]   ,					'''<pad>''')
           self.assertEqual(vocab_keys[-1]   ,					'''j''')
           self.assertEqual(len(UpperCAmelCase__)   ,					1_001)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[str]:





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size   ,					1_001)


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->List[str]:





           '''simple docstring'''

           A__ 					=       SpeechaTextTokenizer.from_pretrained(self.tmpdirname)

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

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCAmelCase__)   ,					[289, 50, 14, 174, 386]   ,					)

           A__ 					=       tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
           self.assertListEqual(
               UpperCAmelCase__   ,					[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''', '''.''']   ,					)
           A__ 					=       tokenizer.convert_tokens_to_ids(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__   ,					[12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8])

           A__ 					=       tokenizer.convert_ids_to_tokens(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__   ,					[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>''', '''.''']   ,					)





    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->List[Any]:





           '''simple docstring'''

           A__ 					=       {'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 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], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''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, 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, 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, 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, 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=UpperCAmelCase__   ,					model_name='''facebook/s2t-small-mustc-en-de-st'''   ,					revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad'''   ,					)


@require_sentencepiece
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''valhalla/s2t_mustc_multilinguial_medium'''

    UpperCAmelCase__				    = '''C\'est trop cool'''
    UpperCAmelCase__				    = '''Esto es genial'''


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Dict)		->Dict:





           '''simple docstring'''

           A__ 					=       SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name)
           return cls


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Dict:





           '''simple docstring'''

           self.assertEqual(self.tokenizer.lang_code_to_id['''pt''']   ,					4)
           self.assertEqual(self.tokenizer.lang_code_to_id['''ru''']   ,					6)
           self.assertEqual(self.tokenizer.lang_code_to_id['''it''']   ,					9)
           self.assertEqual(self.tokenizer.lang_code_to_id['''de''']   ,					11)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Dict:





           '''simple docstring'''

           self.assertEqual(self.tokenizer.vocab_size   ,					10_000)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Union[str, Any]:





           '''simple docstring'''

           self.assertIn(UpperCAmelCase__   ,					self.tokenizer.all_special_ids)
           A__ 					=       [ES_CODE, 4, 1_601, 47, 7_647, 2]
           A__ 					=       self.tokenizer.decode(UpperCAmelCase__   ,					skip_special_tokens=UpperCAmelCase__)
           A__ 					=       self.tokenizer.decode(generated_ids[1:]   ,					skip_special_tokens=UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__   ,					UpperCAmelCase__)
           self.assertNotIn(self.tokenizer.eos_token   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->str:





           '''simple docstring'''

           A__ 					=       '''fr'''
           A__ 					=       self.tokenizer(self.french_text).input_ids
           self.assertEqual(encoded[0]   ,					UpperCAmelCase__)
           self.assertEqual(encoded[-1]   ,					self.tokenizer.eos_token_id)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->int:





           '''simple docstring'''

           A__ 					=       '''fr'''
           self.assertListEqual(self.tokenizer.prefix_tokens   ,					[FR_CODE])

           A__ 					=       '''es'''
           self.assertListEqual(self.tokenizer.prefix_tokens   ,					[ES_CODE])


