



"""simple docstring"""





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       (KDPMaDiscreteScheduler,)
		_lowerCamelCase			  =       10





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : int						=			{
								    """num_train_timesteps""": 1_1_0_0,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_lowercase   )
								return config





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=_lowercase   )





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







								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,    [0.0002, 0.002, 0.02]   ):
														self.check_over_configs(beta_start=_lowercase							,    beta_end=_lowercase   )





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







								'''simple docstring'''

								for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=_lowercase   )





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







								'''simple docstring'''

								snake_case_   : str						=			self.scheduler_classes[0]
								snake_case_   : Union[str, Any]						=			self.get_scheduler_config(prediction_type="""v_prediction"""   )
								snake_case_   : Union[str, Any]						=			scheduler_class(**_lowercase   )

								scheduler.set_timesteps(self.num_inference_steps   )

								snake_case_   : Any						=			self.dummy_model()
								snake_case_   : int						=			self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_   : Optional[int]						=			sample.to(_lowercase   )

								for i, t in enumerate(scheduler.timesteps   ):
														snake_case_   : Tuple						=			scheduler.scale_model_input(_lowercase							,    _lowercase   )

														snake_case_   : List[str]						=			model(_lowercase							,    _lowercase   )

														snake_case_   : Optional[Any]						=			scheduler.step(_lowercase							,    _lowercase							,    _lowercase   )
														snake_case_   : int						=			output.prev_sample

								snake_case_   : List[str]						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : Tuple						=			torch.mean(torch.abs(_lowercase   )   )

								if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6934E-07   ) < 1E-2
														assert abs(result_mean.item() - 6.1112E-10   ) < 1E-3
								else:
														# CUDA
														assert abs(result_sum.item() - 4.693428650170972E-07   ) < 1E-2
														assert abs(result_mean.item() - 0.0002   ) < 1E-3





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								if torch_device == "mps":
														return
								snake_case_   : Optional[Any]						=			self.scheduler_classes[0]
								snake_case_   : str						=			self.get_scheduler_config()
								snake_case_   : List[str]						=			scheduler_class(**_lowercase   )

								scheduler.set_timesteps(self.num_inference_steps   )

								snake_case_   : int						=			self.dummy_model()
								snake_case_   : str						=			self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_   : List[str]						=			sample.to(_lowercase   )

								for i, t in enumerate(scheduler.timesteps   ):
														snake_case_   : List[Any]						=			scheduler.scale_model_input(_lowercase							,    _lowercase   )

														snake_case_   : str						=			model(_lowercase							,    _lowercase   )

														snake_case_   : str						=			scheduler.step(_lowercase							,    _lowercase							,    _lowercase   )
														snake_case_   : int						=			output.prev_sample

								snake_case_   : int						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : Optional[Any]						=			torch.mean(torch.abs(_lowercase   )   )

								if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 20.4125   ) < 1E-2
														assert abs(result_mean.item() - 0.0266   ) < 1E-3
								else:
														# CUDA
														assert abs(result_sum.item() - 20.4125   ) < 1E-2
														assert abs(result_mean.item() - 0.0266   ) < 1E-3





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







								'''simple docstring'''

								if torch_device == "mps":
														return
								snake_case_   : Tuple						=			self.scheduler_classes[0]
								snake_case_   : Optional[Any]						=			self.get_scheduler_config()
								snake_case_   : Dict						=			scheduler_class(**_lowercase   )

								scheduler.set_timesteps(self.num_inference_steps							,    device=_lowercase   )

								snake_case_   : str						=			self.dummy_model()
								snake_case_   : List[Any]						=			self.dummy_sample_deter.to(_lowercase   ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
														snake_case_   : Tuple						=			scheduler.scale_model_input(_lowercase							,    _lowercase   )

														snake_case_   : List[Any]						=			model(_lowercase							,    _lowercase   )

														snake_case_   : str						=			scheduler.step(_lowercase							,    _lowercase							,    _lowercase   )
														snake_case_   : Optional[Any]						=			output.prev_sample

								snake_case_   : int						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : str						=			torch.mean(torch.abs(_lowercase   )   )

								if str(_lowercase   ).startswith("""cpu"""   ):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 20.4125   ) < 1E-2
														assert abs(result_mean.item() - 0.0266   ) < 1E-3
								else:
														# CUDA
														assert abs(result_sum.item() - 20.4125   ) < 1E-2
														assert abs(result_mean.item() - 0.0266   ) < 1E-3






"""simple docstring"""





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       MgpstrTokenizer
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       {}
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								super().setUp()

								# fmt: off
								snake_case_   : Optional[Any]						=			["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
								# fmt: on
								snake_case_   : str						=			dict(zip(_lowercase							,    range(len(_lowercase   )   )   )   )

								snake_case_   : Tuple						=			os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES["""vocab_file"""]   )
								with open(self.vocab_file							,    """w"""							,    encoding="""utf-8"""   ) as fp:
														fp.write(json.dumps(_lowercase   ) + """\n"""   )





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								return MgpstrTokenizer.from_pretrained(self.tmpdirname							,    **_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			"""tester"""
								snake_case_   : Tuple						=			"""tester"""
								return input_text, output_text





		@unittest.skip("""MGP-STR always lower cases letters."""   )
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Dict						=			self.get_tokenizers(do_lower_case=_lowercase   )
								for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																				snake_case_   : Optional[Any]						=			"""[SPECIAL_TOKEN]"""

																				tokenizer.add_special_tokens({"""cls_token""": special_token}   )
																				snake_case_   : Union[str, Any]						=			tokenizer.encode([special_token]							,    add_special_tokens=_lowercase   )
																				self.assertEqual(len(_lowercase   )							,    1   )

																				snake_case_   : List[Any]						=			tokenizer.decode(_lowercase							,    skip_special_tokens=_lowercase   )
																				self.assertTrue(special_token not in decoded   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.get_tokenizers()
								for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																				snake_case_			,		snake_case_   : Union[str, Any]						=			self.get_input_output_texts(_lowercase   )

																				snake_case_   : List[Any]						=			tokenizer.tokenize(_lowercase   )
																				snake_case_   : Union[str, Any]						=			tokenizer.convert_tokens_to_ids(_lowercase   )
																				snake_case_   : Union[str, Any]						=			tokenizer.encode(_lowercase							,    add_special_tokens=_lowercase   )
																				self.assertListEqual(_lowercase							,    _lowercase   )

																				snake_case_   : List[str]						=			tokenizer.convert_ids_to_tokens(_lowercase   )
																				self.assertNotEqual(len(_lowercase   )							,    0   )
																				snake_case_   : str						=			tokenizer.decode(_lowercase   )
																				self.assertIsInstance(_lowercase							,    _lowercase   )

																				self.assertEqual(text_a.replace(""" """							,    """"""   )							,    _lowercase   )





		@unittest.skip("""MGP-STR tokenizer only handles one sequence."""   )
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								pass





		@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""   )
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								pass

