



"""simple docstring"""
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					(UnCLIPScheduler,)






							def snake_case_  (   self			,     **a_      ):
									a_     :      Union[str, Any] 						=     {
									    "num_train_timesteps": 1_0_0_0,
									    "variance_type": "fixed_small_log",
									    "clip_sample": True,
									    "clip_sample_range": 1.0,
									    "prediction_type": "epsilon",
									}

									config.update(**a_      )
									return config






							def snake_case_  (   self      ):
									for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=a_      )






							def snake_case_  (   self      ):
									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=a_      )






							def snake_case_  (   self      ):
									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=a_      )






							def snake_case_  (   self      ):
									for clip_sample_range in [1, 5, 1_0, 2_0]:
											self.check_over_configs(clip_sample_range=a_      )






							def snake_case_  (   self      ):
									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=a_      )






							def snake_case_  (   self      ):
									for time_step in [0, 5_0_0, 9_9_9]:
											for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=a_			,     prev_timestep=a_      )






							def snake_case_  (   self      ):
									a_     :      str 						=     self.scheduler_classes[0]
									a_     :      Tuple 						=     self.get_scheduler_config(variance_type="fixed_small_log"      )
									a_     :      Tuple 						=     scheduler_class(**a_      )

									assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0000e-10      )      ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ) - 0.0_549_625      )      ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ) - 0.9_994_987      )      ) < 1e-5






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.scheduler_classes[0]
									a_     :      Dict 						=     self.get_scheduler_config(variance_type="learned_range"      )
									a_     :      Union[str, Any] 						=     scheduler_class(**a_      )

									a_     :      Dict 						=     0.5

									assert scheduler._get_variance(1			,     predicted_variance=a_      ) - -10.1_712_790 < 1e-5
									assert scheduler._get_variance(4_8_7			,     predicted_variance=a_      ) - -5.7_998_052 < 1e-5
									assert scheduler._get_variance(9_9_9			,     predicted_variance=a_      ) - -0.0_010_011 < 1e-5






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.scheduler_classes[0]
									a_     :      Union[str, Any] 						=     self.get_scheduler_config()
									a_     :      str 						=     scheduler_class(**a_      )

									a_     :      Tuple 						=     scheduler.timesteps

									a_     :      Tuple 						=     self.dummy_model()
									a_     :      Union[str, Any] 						=     self.dummy_sample_deter
									a_     :      str 						=     torch.manual_seed(0      )

									for i, t in enumerate(a_      ):
											# 1. predict noise residual
											a_     :      str 						=     model(a_			,     a_      )

											# 2. predict previous mean of sample x_t-1
											a_     :      Optional[Any] 						=     scheduler.step(a_			,     a_			,     a_			,     generator=a_      ).prev_sample

											a_     :      int 						=     pred_prev_sample

									a_     :      Dict 						=     torch.sum(torch.abs(a_      )      )
									a_     :      Optional[Any] 						=     torch.mean(torch.abs(a_      )      )

									assert abs(result_sum.item() - 252.2_682_495      ) < 1e-2
									assert abs(result_mean.item() - 0.3_284_743      ) < 1e-3






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.scheduler_classes[0]
									a_     :      Optional[int] 						=     self.get_scheduler_config()
									a_     :      Any 						=     scheduler_class(**a_      )

									scheduler.set_timesteps(2_5      )

									a_     :      Dict 						=     scheduler.timesteps

									a_     :      int 						=     self.dummy_model()
									a_     :      int 						=     self.dummy_sample_deter
									a_     :      str 						=     torch.manual_seed(0      )

									for i, t in enumerate(a_      ):
											# 1. predict noise residual
											a_     :      Any 						=     model(a_			,     a_      )

											if i + 1 == timesteps.shape[0]:
													a_     :      Union[str, Any] 						=     None
											else:
													a_     :      Dict 						=     timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											a_     :      int 						=     scheduler.step(
											    a_			,     a_			,     a_			,     prev_timestep=a_			,     generator=a_      ).prev_sample

											a_     :      Dict 						=     pred_prev_sample

									a_     :      Optional[int] 						=     torch.sum(torch.abs(a_      )      )
									a_     :      Tuple 						=     torch.mean(torch.abs(a_      )      )

									assert abs(result_sum.item() - 258.2_044_983      ) < 1e-2
									assert abs(result_mean.item() - 0.3_362_038      ) < 1e-3






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									pass




"""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


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					GPTSwaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False






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

									# We have a SentencePiece fixture for testing
									a_     :      Optional[int] 						=     GPTSwaTokenizer(a_			,     eos_token="<unk>"			,     bos_token="<unk>"			,     pad_token="<unk>"      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     "This is a test"
									a_     :      Tuple 						=     "This is a test"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "<s>"
									a_     :      Any 						=     1

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






							def snake_case_  (   self      ):
									a_     :      int 						=     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(a_      )			,     2_0_0_0      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     2_0_0_0      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     GPTSwaTokenizer(a_      )

									a_     :      str 						=     tokenizer.tokenize("This is a test"      )
									self.assertListEqual(a_			,     ["▁This", "▁is", "▁a", "▁t", "est"]      )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]      )

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

									a_     :      List[str] 						=     tokenizer.convert_tokens_to_ids(a_      )
									self.assertListEqual(
									    a_			,     [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]			,     )

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






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     GPTSwaTokenizer(a_      )
									a_     :      List[Any] 						=     ["This is a test", "I was born in 92000, and this is falsé."]
									a_     :      Optional[Any] 						=     [
									    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
									    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
									]

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

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






							@slow
							def snake_case_  (   self      ):
									a_     :      Dict 						=     [
									    "<|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
									a_     :      Union[str, Any] 						=     {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 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=a_			,     model_name="AI-Sweden/gpt-sw3-126m"			,     sequences=a_			,     )