



__UpperCAmelCase	       =	'''0.18.2'''

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
    if not is_onnx_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_onnx_objects import *  # noqa F403
else:
    from .pipelines import OnnxRuntimeModel

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_pt_objects import *  # noqa F403
else:
    from .models import (
        AutoencoderKL,
        ControlNetModel,
        ModelMixin,
        PriorTransformer,
        TaFilmDecoder,
        TransformeraDModel,
        UNetaDModel,
        UNetaDConditionModel,
        UNetaDModel,
        UNetaDConditionModel,
        VQModel,
    )
    from .optimization import (
        get_constant_schedule,
        get_constant_schedule_with_warmup,
        get_cosine_schedule_with_warmup,
        get_cosine_with_hard_restarts_schedule_with_warmup,
        get_linear_schedule_with_warmup,
        get_polynomial_decay_schedule_with_warmup,
        get_scheduler,
    )
    from .pipelines import (
        AudioPipelineOutput,
        ConsistencyModelPipeline,
        DanceDiffusionPipeline,
        DDIMPipeline,
        DDPMPipeline,
        DiffusionPipeline,
        DiTPipeline,
        ImagePipelineOutput,
        KarrasVePipeline,
        LDMPipeline,
        LDMSuperResolutionPipeline,
        PNDMPipeline,
        RePaintPipeline,
        ScoreSdeVePipeline,
    )
    from .schedulers import (
        CMStochasticIterativeScheduler,
        DDIMInverseScheduler,
        DDIMParallelScheduler,
        DDIMScheduler,
        DDPMParallelScheduler,
        DDPMScheduler,
        DEISMultistepScheduler,
        DPMSolverMultistepInverseScheduler,
        DPMSolverMultistepScheduler,
        DPMSolverSinglestepScheduler,
        EulerAncestralDiscreteScheduler,
        EulerDiscreteScheduler,
        HeunDiscreteScheduler,
        IPNDMScheduler,
        KarrasVeScheduler,
        KDPMaAncestralDiscreteScheduler,
        KDPMaDiscreteScheduler,
        PNDMScheduler,
        RePaintScheduler,
        SchedulerMixin,
        ScoreSdeVeScheduler,
        UnCLIPScheduler,
        UniPCMultistepScheduler,
        VQDiffusionScheduler,
    )
    from .training_utils import EMAModel

try:
    if not (is_torch_available() and is_scipy_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
    from .schedulers import LMSDiscreteScheduler

try:
    if not (is_torch_available() and is_torchsde_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
    from .schedulers import DPMSolverSDEScheduler

try:
    if not (is_torch_available() and is_transformers_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipelines import (
        AltDiffusionImgaImgPipeline,
        AltDiffusionPipeline,
        AudioLDMPipeline,
        CycleDiffusionPipeline,
        IFImgaImgPipeline,
        IFImgaImgSuperResolutionPipeline,
        IFInpaintingPipeline,
        IFInpaintingSuperResolutionPipeline,
        IFPipeline,
        IFSuperResolutionPipeline,
        ImageTextPipelineOutput,
        KandinskyImgaImgPipeline,
        KandinskyInpaintPipeline,
        KandinskyPipeline,
        KandinskyPriorPipeline,
        KandinskyVaaControlnetImgaImgPipeline,
        KandinskyVaaControlnetPipeline,
        KandinskyVaaImgaImgPipeline,
        KandinskyVaaInpaintPipeline,
        KandinskyVaaPipeline,
        KandinskyVaaPriorEmbaEmbPipeline,
        KandinskyVaaPriorPipeline,
        LDMTextToImagePipeline,
        PaintByExamplePipeline,
        SemanticStableDiffusionPipeline,
        ShapEImgaImgPipeline,
        ShapEPipeline,
        StableDiffusionAttendAndExcitePipeline,
        StableDiffusionControlNetImgaImgPipeline,
        StableDiffusionControlNetInpaintPipeline,
        StableDiffusionControlNetPipeline,
        StableDiffusionDepthaImgPipeline,
        StableDiffusionDiffEditPipeline,
        StableDiffusionImageVariationPipeline,
        StableDiffusionImgaImgPipeline,
        StableDiffusionInpaintPipeline,
        StableDiffusionInpaintPipelineLegacy,
        StableDiffusionInstructPixaPixPipeline,
        StableDiffusionLatentUpscalePipeline,
        StableDiffusionLDMaDPipeline,
        StableDiffusionModelEditingPipeline,
        StableDiffusionPanoramaPipeline,
        StableDiffusionParadigmsPipeline,
        StableDiffusionPipeline,
        StableDiffusionPipelineSafe,
        StableDiffusionPixaPixZeroPipeline,
        StableDiffusionSAGPipeline,
        StableDiffusionUpscalePipeline,
        StableUnCLIPImgaImgPipeline,
        StableUnCLIPPipeline,
        TextToVideoSDPipeline,
        TextToVideoZeroPipeline,
        UnCLIPImageVariationPipeline,
        UnCLIPPipeline,
        UniDiffuserModel,
        UniDiffuserPipeline,
        UniDiffuserTextDecoder,
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
        VideoToVideoSDPipeline,
        VQDiffusionPipeline,
    )

try:
    if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
    from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
    if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
    from .pipelines import StableDiffusionKDiffusionPipeline

try:
    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
    from .pipelines import (
        OnnxStableDiffusionImgaImgPipeline,
        OnnxStableDiffusionInpaintPipeline,
        OnnxStableDiffusionInpaintPipelineLegacy,
        OnnxStableDiffusionPipeline,
        OnnxStableDiffusionUpscalePipeline,
        StableDiffusionOnnxPipeline,
    )

try:
    if not (is_torch_available() and is_librosa_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
    from .pipelines import AudioDiffusionPipeline, Mel

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .pipelines import SpectrogramDiffusionPipeline

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_flax_objects import *  # noqa F403
else:
    from .models.controlnet_flax import FlaxControlNetModel
    from .models.modeling_flax_utils import FlaxModelMixin
    from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .models.vae_flax import FlaxAutoencoderKL
    from .pipelines import FlaxDiffusionPipeline
    from .schedulers import (
        FlaxDDIMScheduler,
        FlaxDDPMScheduler,
        FlaxDPMSolverMultistepScheduler,
        FlaxKarrasVeScheduler,
        FlaxLMSDiscreteScheduler,
        FlaxPNDMScheduler,
        FlaxSchedulerMixin,
        FlaxScoreSdeVeScheduler,
    )


try:
    if not (is_flax_available() and is_transformers_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
    from .pipelines import (
        FlaxStableDiffusionControlNetPipeline,
        FlaxStableDiffusionImgaImgPipeline,
        FlaxStableDiffusionInpaintPipeline,
        FlaxStableDiffusionPipeline,
    )

try:
    if not (is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils.dummy_note_seq_objects import *  # noqa F403
else:
    from .pipelines import MidiProcessor








import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
    )



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=99,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=37,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=4,      )					->		Dict:
          UpperCamelCase   :  Optional[int]						     =     parent
          UpperCamelCase   :  Optional[int]						     =     batch_size
          UpperCamelCase   :  Optional[int]						     =     seq_length
          UpperCamelCase   :  Any						     =     is_training
          UpperCamelCase   :  Tuple						     =     use_attention_mask
          UpperCamelCase   :  Dict						     =     use_token_type_ids
          UpperCamelCase   :  Union[str, Any]						     =     use_labels
          UpperCamelCase   :  Any						     =     vocab_size
          UpperCamelCase   :  Any						     =     hidden_size
          UpperCamelCase   :  str						     =     num_hidden_layers
          UpperCamelCase   :  Optional[int]						     =     num_attention_heads
          UpperCamelCase   :  Union[str, Any]						     =     intermediate_size
          UpperCamelCase   :  List[Any]						     =     hidden_act
          UpperCamelCase   :  Any						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  int						     =     type_vocab_size
          UpperCamelCase   :  Optional[int]						     =     type_sequence_label_size
          UpperCamelCase   :  str						     =     initializer_range
          UpperCamelCase   :  Tuple						     =     num_choices
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Tuple						     =     ids_tensor([self.batch_size, self.seq_length],      self.vocab_size	)

          UpperCamelCase   :  Dict						     =     None
          if self.use_attention_mask:
             UpperCamelCase   :  Union[str, Any]						     =     random_attention_mask([self.batch_size, self.seq_length]	)

          UpperCamelCase   :  int						     =     None
          if self.use_token_type_ids:
             UpperCamelCase   :  str						     =     ids_tensor([self.batch_size, self.seq_length],      self.type_vocab_size	)

          UpperCamelCase   :  Optional[int]						     =     RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      intermediate_size=self.intermediate_size,      hidden_act=self.hidden_act,      hidden_dropout_prob=self.hidden_dropout_prob,      attention_probs_dropout_prob=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      type_vocab_size=self.type_vocab_size,      is_decoder=SCREAMING_SNAKE_CASE_,      initializer_range=self.initializer_range,      )

          return config, input_ids, token_type_ids, attention_mask
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Any						     =     config_and_inputs
          UpperCamelCase   :  Union[str, Any]						     =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     config_and_inputs

          UpperCamelCase   :  Dict						     =     True
          UpperCamelCase   :  List[Any]						     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
          UpperCamelCase   :  List[Any]						     =     ids_tensor([self.batch_size, self.seq_length],      vocab_size=2	)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  True

       UpperCAmelCase__    : Optional[Any]       					=  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     FlaxRobertaPreLayerNormModelTester(self	)






       @slow
       def  snake_case_	(      self	)					->		List[str]:
          for model_class_name in self.all_model_classes:
             UpperCamelCase   :  List[str]						     =     model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[Any]						     =     model(np.ones((1, 1)	)	)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)






@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Union[str, Any]						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          UpperCamelCase   :  List[Any]						     =     [1, 11, 5_0265]
          self.assertEqual(list(output.shape	),      SCREAMING_SNAKE_CASE_	)
          # compare the actual values for a slice.
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)






       @slow
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Dict						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          # compare the actual values for a slice.
          UpperCamelCase   :  Any						     =     np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)



