




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class __snake_case			(			unittest.TestCase		):







   @property
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							UNetaDModel(
            block_out_channels=(32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=3					,						out_channels=3					,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,						)
        return model







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.dummy_uncond_unet
        UpperCAmelCase_          =							KarrasVeScheduler()

        UpperCAmelCase_          =							KarrasVePipeline(unet=_snake_case					,						scheduler=_snake_case)
        pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_          =							torch.manual_seed(0)
        UpperCAmelCase_          =							pipe(num_inference_steps=2					,						generator=_snake_case					,						output_type='''numpy''').images

        UpperCAmelCase_          =							torch.manual_seed(0)
        UpperCAmelCase_          =							pipe(num_inference_steps=2					,						generator=_snake_case					,						output_type='''numpy'''					,						return_dict=_snake_case)[0]

        UpperCAmelCase_          =							image[0, -3:, -3:, -1]
        UpperCAmelCase_          =							image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        UpperCAmelCase_          =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







@slow
@require_torch
class __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''google/ncsnpp-celebahq-256'''
        UpperCAmelCase_          =							UNetaDModel.from_pretrained(_snake_case)
        UpperCAmelCase_          =							KarrasVeScheduler()

        UpperCAmelCase_          =							KarrasVePipeline(unet=_snake_case					,						scheduler=_snake_case)
        pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_          =							torch.manual_seed(0)
        UpperCAmelCase_          =							pipe(num_inference_steps=20					,						generator=_snake_case					,						output_type='''numpy''').images

        UpperCAmelCase_          =							image[0, -3:, -3:, -1]
        assert image.shape == (1, 256, 256, 3)
        UpperCAmelCase_          =							np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2












from typing import TYPE_CHECKING

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


snake_case_ : int    		=       {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       ["DebertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaForMaskedLM",
        "DebertaForQuestionAnswering",
        "DebertaForSequenceClassification",
        "DebertaForTokenClassification",
        "DebertaModel",
        "DebertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaForMaskedLM",
        "TFDebertaForQuestionAnswering",
        "TFDebertaForSequenceClassification",
        "TFDebertaForTokenClassification",
        "TFDebertaModel",
        "TFDebertaPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Dict    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






