



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 				_snake_case			(					__UpperCAmelCase):
				UpperCamelCase__				:					Union[str, Any]     		=(CMStochasticIterativeScheduler,)
				UpperCamelCase__				:					str     		=1_0




				def 							A__      (						self					:						Optional[int],					**__lowercase					:						Optional[Any]						):
							lowercase__					  =				{
							    "num_train_timesteps": 201,
							    "sigma_min": 0.002,
							    "sigma_max": 80.0,
							}

							config.update(**UpperCAmelCase_						)
							return config




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				10

							lowercase__					  =				self.get_scheduler_config()
							lowercase__					  =				self.scheduler_classes[0](**UpperCAmelCase_						)

							scheduler.set_timesteps(UpperCAmelCase_						)

							lowercase__					  =				scheduler.timesteps[0]
							lowercase__					  =				scheduler.timesteps[1]

							lowercase__					  =				self.dummy_sample
							lowercase__					  =				0.1 * sample

							lowercase__					  =				scheduler.step(UpperCAmelCase_,					UpperCAmelCase_,					UpperCAmelCase_						).prev_sample
							lowercase__					  =				scheduler.step(UpperCAmelCase_,					UpperCAmelCase_,					UpperCAmelCase_						).prev_sample

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




				def 							A__      (						self					:						str						):
							for timesteps in [10, 50, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase_						)




				def 							A__      (						self					:						int						):
							for clip_denoised in [True, False]:
										self.check_over_configs(clip_denoised=UpperCAmelCase_						)




				def 							A__      (						self					:						Any						):
							lowercase__					  =				self.scheduler_classes[0]
							lowercase__					  =				self.get_scheduler_config()
							lowercase__					  =				scheduler_class(**UpperCAmelCase_						)

							lowercase__					  =				1
							scheduler.set_timesteps(UpperCAmelCase_						)
							lowercase__					  =				scheduler.timesteps

							lowercase__					  =				torch.manual_seed(0						)

							lowercase__					  =				self.dummy_model()
							lowercase__					  =				self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(UpperCAmelCase_						):
										# 1. scale model input
										lowercase__					  =				scheduler.scale_model_input(UpperCAmelCase_,					UpperCAmelCase_						)

										# 2. predict noise residual
										lowercase__					  =				model(UpperCAmelCase_,					UpperCAmelCase_						)

										# 3. predict previous sample x_t-1
										lowercase__					  =				scheduler.step(UpperCAmelCase_,					UpperCAmelCase_,					UpperCAmelCase_,					generator=UpperCAmelCase_						).prev_sample

										lowercase__					  =				pred_prev_sample

							lowercase__					  =				torch.sum(torch.abs(UpperCAmelCase_						)						)
							lowercase__					  =				torch.mean(torch.abs(UpperCAmelCase_						)						)

							assert abs(result_sum.item() - 192.7614						) < 1e-2
							assert abs(result_mean.item() - 0.2510						) < 1e-3




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				self.scheduler_classes[0]
							lowercase__					  =				self.get_scheduler_config()
							lowercase__					  =				scheduler_class(**UpperCAmelCase_						)

							lowercase__					  =				[106, 0]
							scheduler.set_timesteps(timesteps=UpperCAmelCase_						)
							lowercase__					  =				scheduler.timesteps

							lowercase__					  =				torch.manual_seed(0						)

							lowercase__					  =				self.dummy_model()
							lowercase__					  =				self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
										# 1. scale model input
										lowercase__					  =				scheduler.scale_model_input(UpperCAmelCase_,					UpperCAmelCase_						)

										# 2. predict noise residual
										lowercase__					  =				model(UpperCAmelCase_,					UpperCAmelCase_						)

										# 3. predict previous sample x_t-1
										lowercase__					  =				scheduler.step(UpperCAmelCase_,					UpperCAmelCase_,					UpperCAmelCase_,					generator=UpperCAmelCase_						).prev_sample

										lowercase__					  =				pred_prev_sample

							lowercase__					  =				torch.sum(torch.abs(UpperCAmelCase_						)						)
							lowercase__					  =				torch.mean(torch.abs(UpperCAmelCase_						)						)

							assert abs(result_sum.item() - 347.6357						) < 1e-2
							assert abs(result_mean.item() - 0.4527						) < 1e-3




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				self.scheduler_classes[0]
							lowercase__					  =				self.get_scheduler_config()
							lowercase__					  =				scheduler_class(**UpperCAmelCase_						)

							lowercase__					  =				[39, 30, 12, 15, 0]

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




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				self.scheduler_classes[0]
							lowercase__					  =				self.get_scheduler_config()
							lowercase__					  =				scheduler_class(**UpperCAmelCase_						)

							lowercase__					  =				[39, 30, 12, 1, 0]
							lowercase__					  =				len(UpperCAmelCase_						)

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




				def 							A__      (						self					:						Any						):
							lowercase__					  =				self.scheduler_classes[0]
							lowercase__					  =				self.get_scheduler_config()
							lowercase__					  =				scheduler_class(**UpperCAmelCase_						)

							lowercase__					  =				[scheduler.config.num_train_timesteps]

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









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Dict     		=TransfoXLTokenizer
				UpperCamelCase__				:					List[Any]     		=False
				UpperCamelCase__				:					List[Any]     		=False




				def 							A__      (						self					:						Union[str, Any]						):
							super().setUp()

							lowercase__					  =				[
							    "<unk>",
							    "[CLS]",
							    "[SEP]",
							    "want",
							    "unwanted",
							    "wa",
							    "un",
							    "running",
							    ",",
							    "low",
							    "l",
							]
							lowercase__					  =				os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES["vocab_file"]						)
							with open(self.vocab_file,					"w",					encoding="utf-8"						) as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)




				def 							A__      (						self					:						Union[str, Any],					**__lowercase					:						Any						):
							lowercase__					  =				True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname,					**__lowercase						)




				def 							A__      (						self					:						Tuple,					__lowercase					:						Optional[int]						):
							lowercase__					  =				"<unk> UNwanted , running"
							lowercase__					  =				"<unk> unwanted, running"
							return input_text, output_text




				def 							A__      (						self					:						str						):
							lowercase__					  =				TransfoXLTokenizer(vocab_file=self.vocab_file,					lower_case=__lowercase						)

							lowercase__					  =				tokenizer.tokenize("<unk> UNwanted , running"						)
							self.assertListEqual(__lowercase,					["<unk>", "unwanted", ",", "running"]						)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						),					[0, 4, 8, 7]						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				TransfoXLTokenizer(lower_case=__lowercase						)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "						),					["hello", "!", "how", "are", "you", "?"]						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				TransfoXLTokenizer(lower_case=__lowercase						)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "						),					["HeLLo", "!", "how", "Are", "yoU", "?"]						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				TransfoXLTokenizer(lower_case=__lowercase						)
							lowercase__					  =				"Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
							lowercase__					  =				[
							    "Hello",
							    "(",
							    "bracket",
							    ")",
							    "and",
							    "side",
							    "@-@",
							    "scrolled",
							    "[",
							    "and",
							    "]",
							    "Henry",
							    "'s",
							    "$",
							    "5",
							    "@,@",
							    "000",
							    "with",
							    "3",
							    "@.@",
							    "34",
							    "m",
							    ".",
							    "What",
							    "'s",
							    "up",
							    "!",
							    "?",
							]

							self.assertListEqual(tokenizer.tokenize(__lowercase						),					__lowercase						)

							self.assertEqual(tokenizer.convert_tokens_to_string(__lowercase						),					__lowercase						)




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.get_tokenizer()
							lowercase__					  =				len(__lowercase						)

							tokenizer.add_tokens(["new1", "new2"]						)
							tokenizer.move_added_token("new1",					1						)

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(__lowercase						),					original_len + 2						)
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode("new1"						),					[1]						)
							self.assertEqual(tokenizer.decode([1]						),					"new1"						)




