



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   UpperCAmelCase_    (  UpperCamelCase_       ,       unittest.TestCase			):

						'''simple docstring'''




						UpperCamelCase__     :					int							    =    MgpstrTokenizer
						UpperCamelCase__     :					List[Any]							    =    False
						UpperCamelCase__     :					Optional[int]							    =    {}
						UpperCamelCase__     :					Any							    =    False






						def 			_A      (       self						):







													'''simple docstring'''

													super().setUp()

													# fmt: off
													__SCREAMING_SNAKE_CASE										=   ['[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
													__SCREAMING_SNAKE_CASE										=   dict(zip(a_ ,				range(len(a_						)						)						)						)

													__SCREAMING_SNAKE_CASE										=   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(a_						) + '\n'						)






						def 			_A      (       self ,				**_A						):







													'''simple docstring'''

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






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'tester'
													__SCREAMING_SNAKE_CASE										=   'tester'
													return input_text, output_text






						@unittest.skip('MGP-STR always lower cases letters.'						)
						def 			_A      (       self						):







													'''simple docstring'''

													pass






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.get_tokenizers(do_lower_case=a_						)
													for tokenizer in tokenizers:
																				with self.subTest(f"""{tokenizer.__class__.__name__}"""						):
																											__SCREAMING_SNAKE_CASE										=   '[SPECIAL_TOKEN]'

																											tokenizer.add_special_tokens({'cls_token': special_token}						)
																											__SCREAMING_SNAKE_CASE										=   tokenizer.encode([special_token] ,				add_special_tokens=a_						)
																											self.assertEqual(len(a_						) ,				1						)

																											__SCREAMING_SNAKE_CASE										=   tokenizer.decode(a_ ,				skip_special_tokens=a_						)
																											self.assertTrue(special_token not in decoded						)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(f"""{tokenizer.__class__.__name__}"""						):
																											__SCREAMING_SNAKE_CASE										=   self.get_input_output_texts(a_						)

																											__SCREAMING_SNAKE_CASE										=   tokenizer.tokenize(a_						)
																											__SCREAMING_SNAKE_CASE										=   tokenizer.convert_tokens_to_ids(a_						)
																											__SCREAMING_SNAKE_CASE										=   tokenizer.encode(a_ ,				add_special_tokens=a_						)
																											self.assertListEqual(a_ ,				a_						)

																											__SCREAMING_SNAKE_CASE										=   tokenizer.convert_ids_to_tokens(a_						)
																											self.assertNotEqual(len(a_						) ,				0						)
																											__SCREAMING_SNAKE_CASE										=   tokenizer.decode(a_						)
																											self.assertIsInstance(a_ ,				a_						)

																											self.assertEqual(text_a.replace(' ' ,				''						) ,				a_						)






						@unittest.skip('MGP-STR tokenizer only handles one sequence.'						)
						def 			_A      (       self						):







													'''simple docstring'''

													pass






						@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'						)
						def 			_A      (       self						):







													'''simple docstring'''

													pass











'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

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

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

										with self.assertRaises(a_      ,						msg='`timesteps` must be in descending order.'			):
													scheduler.set_timesteps(timesteps=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

										with self.assertRaises(a_      ,						msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
													scheduler.set_timesteps(num_inference_steps=a_      ,						timesteps=a_			)




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    a_      ,						msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      ,						):
													scheduler.set_timesteps(timesteps=a_			)




