






'''simple docstring'''







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class 		UpperCamelCase__    (__A			):




		'''simple docstring'''





		_UpperCamelCase				  =					(DPMSolverSDEScheduler,)
		_UpperCamelCase				  =					10






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								lowerCamelCase__         =  {
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'noise_sampler_seed': 0,
								}

								config.update(**UpperCamelCase__       )
								return config






		def 			UpperCamelCase_					(      self       ):
								for timesteps in [10, 50, 1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__       )






		def 			UpperCamelCase_					(      self       ):
								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=UpperCamelCase__   ,beta_end=UpperCamelCase__       )






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






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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.scheduler_classes[0]
								lowerCamelCase__         =  self.get_scheduler_config()
								lowerCamelCase__         =  scheduler_class(**UpperCamelCase__       )

								scheduler.set_timesteps(self.num_inference_steps       )

								lowerCamelCase__         =  self.dummy_model()
								lowerCamelCase__         =  self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCamelCase__         =  sample.to(UpperCamelCase__       )

								for i, t in enumerate(scheduler.timesteps       ):
														lowerCamelCase__         =  scheduler.scale_model_input(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  model(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  scheduler.step(UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__       )
														lowerCamelCase__         =  output.prev_sample

								lowerCamelCase__         =  torch.sum(torch.abs(UpperCamelCase__       )       )
								lowerCamelCase__         =  torch.mean(torch.abs(UpperCamelCase__       )       )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75       ) < 1E-2
														assert abs(result_mean.item() - 0.2178_7059_6456_5277       ) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06       ) < 1E-2
														assert abs(result_mean.item() - 0.2_2342_9068_9229_9652       ) < 1E-3
								else:
														assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62       ) < 1E-2
														assert abs(result_mean.item() - 0.211_6195_7085_1326       ) < 1E-3






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.scheduler_classes[0]
								lowerCamelCase__         =  self.get_scheduler_config(prediction_type="""v_prediction"""       )
								lowerCamelCase__         =  scheduler_class(**UpperCamelCase__       )

								scheduler.set_timesteps(self.num_inference_steps       )

								lowerCamelCase__         =  self.dummy_model()
								lowerCamelCase__         =  self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCamelCase__         =  sample.to(UpperCamelCase__       )

								for i, t in enumerate(scheduler.timesteps       ):
														lowerCamelCase__         =  scheduler.scale_model_input(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  model(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  scheduler.step(UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__       )
														lowerCamelCase__         =  output.prev_sample

								lowerCamelCase__         =  torch.sum(torch.abs(UpperCamelCase__       )       )
								lowerCamelCase__         =  torch.mean(torch.abs(UpperCamelCase__       )       )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53       ) < 1E-2
														assert abs(result_mean.item() - 0.1_6226_2890_1481_6284       ) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03       ) < 1E-2
														assert abs(result_mean.item() - 0.1_6688_3260_0116_7297       ) < 1E-3
								else:
														assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25       ) < 1E-2
														assert abs(result_mean.item() - 0.1560_5306_6253_6621       ) < 1E-3






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.scheduler_classes[0]
								lowerCamelCase__         =  self.get_scheduler_config()
								lowerCamelCase__         =  scheduler_class(**UpperCamelCase__       )

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

								lowerCamelCase__         =  self.dummy_model()
								lowerCamelCase__         =  self.dummy_sample_deter.to(UpperCamelCase__       ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
														lowerCamelCase__         =  scheduler.scale_model_input(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  model(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  scheduler.step(UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__       )
														lowerCamelCase__         =  output.prev_sample

								lowerCamelCase__         =  torch.sum(torch.abs(UpperCamelCase__       )       )
								lowerCamelCase__         =  torch.mean(torch.abs(UpperCamelCase__       )       )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38       ) < 1E-2
														assert abs(result_mean.item() - 0.2_1805_9346_0798_2635       ) < 1E-3
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12       ) < 1E-2
														assert abs(result_mean.item() - 0.2_2342_9083_8241_5771       ) < 1E-3
								else:
														assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62       ) < 1E-2
														assert abs(result_mean.item() - 0.211_6195_7085_1326       ) < 1E-3






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.scheduler_classes[0]
								lowerCamelCase__         =  self.get_scheduler_config()
								lowerCamelCase__         =  scheduler_class(**UpperCamelCase__   ,use_karras_sigmas=UpperCamelCase__       )

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

								lowerCamelCase__         =  self.dummy_model()
								lowerCamelCase__         =  self.dummy_sample_deter.to(UpperCamelCase__       ) * scheduler.init_noise_sigma
								lowerCamelCase__         =  sample.to(UpperCamelCase__       )

								for t in scheduler.timesteps:
														lowerCamelCase__         =  scheduler.scale_model_input(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  model(UpperCamelCase__   ,UpperCamelCase__       )

														lowerCamelCase__         =  scheduler.step(UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__       )
														lowerCamelCase__         =  output.prev_sample

								lowerCamelCase__         =  torch.sum(torch.abs(UpperCamelCase__       )       )
								lowerCamelCase__         =  torch.mean(torch.abs(UpperCamelCase__       )       )

								if torch_device in ["mps"]:
														assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88       ) < 1E-2
														assert abs(result_mean.item() - 0.2_3003_8727_3098_1811       ) < 1E-2
								elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25       ) < 1E-2
														assert abs(result_mean.item() - 0.2_3003_8727_3098_1811       ) < 1E-2
								else:
														assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72       ) < 1E-2
														assert abs(result_mean.item() - 0.2_3003_8727_3098_1811       ) < 1E-2









'''simple docstring'''







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase    :      List[Any]  			=		get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					GPTSwaTokenizer
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					True
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase   ,eos_token="""<unk>"""   ,bos_token="""<unk>"""   ,pad_token="""<unk>"""       )

								tokenizer.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  """This is a test"""
								lowerCamelCase__         =  """This is a test"""
								return input_text, output_text






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """<s>"""
								lowerCamelCase__         =  1

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  list(self.get_tokenizer().get_vocab().keys()       )

								self.assertEqual(vocab_keys[0]   ,"""<unk>"""       )
								self.assertEqual(vocab_keys[1]   ,"""<s>"""       )
								self.assertEqual(vocab_keys[-1]   ,"""j"""       )
								self.assertEqual(len(_lowerCAmelCase       )   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								self.assertEqual(self.get_tokenizer().vocab_size   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer.tokenize("""This is a test"""       )
								self.assertListEqual(_lowerCAmelCase   ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )   ,[4_65, 2_87, 2_65, 6_31, 8_42]       )

								lowerCamelCase__         =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]   ,)
								# fmt: on

								lowerCamelCase__         =  tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )
								self.assertListEqual(
								    _lowerCAmelCase   ,[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]   ,)

								lowerCamelCase__         =  tokenizer.convert_ids_to_tokens(_lowerCAmelCase       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]       )
								# fmt: on






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )
								lowerCamelCase__         =  ["""This is a test""", """I was born in 92000, and this is falsé."""]
								lowerCamelCase__         =  [
								    [4_65, 2_87, 2_65, 6_31, 8_42],
								    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
								]

								# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
								for text, expected_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertListEqual(tokenizer.encode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )

								# Test that decode_fast returns the input text
								for text, token_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertEqual(tokenizer.decode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [
								    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
								    """Hey there, how are you doing this fine day?""",
								    """This is a text with a trailing spaces followed by a dot     .""",
								    """Häj sväjs lillebrör! =)""",
								    """Det är inget fel på Mr. Cool""",
								]

								# fmt: off
								lowerCamelCase__         =  {"""input_ids""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 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, 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], [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], [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]]}
								# fmt: on
								self.tokenizer_integration_test_util(
								    expected_encoding=_lowerCAmelCase   ,model_name="""AI-Sweden/gpt-sw3-126m"""   ,sequences=_lowerCAmelCase   ,)

