




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils




class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@patch("diffusers.utils.hub_utils.get_full_repo_name"							)
				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					Mock							) ->					str:
							a   =      "full_repo_name"
							with TemporaryDirectory() as tmpdir:
										# Dummy args values
										a   =      Mock()
										a   =      tmpdir
										a   =      0
										a   =      "hub_token"
										a   =      "dataset_name"
										a   =      0.01
										a   =      10_00_00
										a   =      1_00_00
										a   =      0.01
										a   =      0.02
										a   =      0.03
										a   =      0.0_005
										a   =      0.000_001
										a   =      1
										a   =      10
										a   =      0.001
										a   =      0.1
										a   =      0.2
										a   =      True

										# Model card mush be rendered and saved
										diffusers.utils.hub_utils.create_model_card(UpperCamelCase_ ,				model_name="model_name"							)
										self.assertTrue((Path(UpperCamelCase_							) / "README.md").is_file()							)












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Dict										= (VQDiffusionScheduler,)





				def 							__UpperCAmelCase							(				self    :					Tuple ,				**__lowerCamelCase    :					Optional[int]							) ->					Optional[Any]:
							a   =      {
							    "num_vec_classes": 40_97,
							    "num_train_timesteps": 1_00,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Any							) ->					Union[str, Any]:
							a   =      4
							a   =      8
							a   =      8

							a   =      torch.randint(0 ,				__lowerCamelCase ,				(batch_size, height * width)							)

							return sample





				@property
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[Any]:
							assert False





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Optional[int]							) ->					int:
							def model(__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any ,				*__lowerCamelCase    :					Optional[Any]							):
										a   ,		a   =      sample.shape
										a   =      torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)							)
										a   =      F.log_softmax(logits.double() ,				dim=1							).float()
										return return_value

							return model





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Any:
							for timesteps in [2, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							for num_vec_classes in [5, 1_00, 10_00, 40_00]:
										self.check_over_configs(num_vec_classes=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[Any]:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[Any]:
							pass






