




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase		:    Optional[int]	      =		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:    List[str]	      =		[
		    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConvNextV2ForImageClassification',
		    'ConvNextV2Model',
		    'ConvNextV2PreTrainedModel',
		    'ConvNextV2Backbone',
		]


if TYPE_CHECKING:
		from .configuration_convnextva import (
		    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConvNextVaConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convnextva import (
				    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvNextVaBackbone,
				    ConvNextVaForImageClassification,
				    ConvNextVaModel,
				    ConvNextVaPreTrainedModel,
				)

else:
		import sys

		__lowerCAmelCase		:    Tuple	      =		_LazyModule(__name__, globals()['__file__'], _import_structure)












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






