




"""simple docstring"""


import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class    A_     (					lowerCAmelCase__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       (VQDiffusionScheduler,)





							def 				UpperCAmelCase__							(  self  :List[str]					,							**lowerCamelCase_  :Optional[int]	):





										"""simple docstring"""

										lowerCamelCase__     :					str		={
										    "num_vec_classes": 4_097,
										    "num_train_timesteps": 100,
										}

										config.update(**UpperCamelCase__	)
										return config





							def 				UpperCAmelCase__							(  self  :Dict					,							lowerCamelCase_  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Tuple		=4
										lowerCamelCase__     :					List[str]		=8
										lowerCamelCase__     :					Optional[Any]		=8

										lowerCamelCase__     :					Union[str, Any]		=torch.randint(0					,							UpperCamelCase__					,							(batch_size, height * width)	)

										return sample





							@property
							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										assert False





							def 				UpperCAmelCase__							(  self  :Any					,							lowerCamelCase_  :Any	):





										"""simple docstring"""

										def model(lowerCamelCase_  :List[str]					,							lowerCamelCase_  :Optional[Any]					,							*lowerCamelCase_  :Tuple	):
													lowerCamelCase__     :					Any		=sample.shape
													lowerCamelCase__     :					Tuple		=torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)	)
													lowerCamelCase__     :					Tuple		=F.log_softmax(logits.double()					,							dim=1	).float()
													return return_value

										return model





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										for timesteps in [2, 5, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=UpperCamelCase__	)





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										for num_vec_classes in [5, 100, 1_000, 4_000]:
													self.check_over_configs(num_vec_classes=UpperCamelCase__	)





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										for t in [0, 50, 99]:
													self.check_over_forward(time_step=UpperCamelCase__	)





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										pass

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase								=						{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""DebertaV2TokenizerFast"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDebertaV2ForMaskedLM""",
							    """TFDebertaV2ForQuestionAnswering""",
							    """TFDebertaV2ForSequenceClassification""",
							    """TFDebertaV2ForTokenClassification""",
							    """TFDebertaV2Model""",
							    """TFDebertaV2PreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaV2ForMaskedLM""",
							    """DebertaV2ForMultipleChoice""",
							    """DebertaV2ForQuestionAnswering""",
							    """DebertaV2ForSequenceClassification""",
							    """DebertaV2ForTokenClassification""",
							    """DebertaV2Model""",
							    """DebertaV2PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)