

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__a    :str      =			False

class   _a					(			unittest.TestCase				):




      """simple docstring"""

      pass




@nightly
@require_torch_gpu
class   _a					(			unittest.TestCase				):




      """simple docstring"""

      def 		__A	(   self							:				List[str]	):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"	)
            # remove text_unet
            pipe.remove_unused_weights()
            pipe.to(UpperCAmelCase	)
            pipe.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				"A painting of a squirrel eating a burger "
            A_						  =				torch.manual_seed(0	)
            A_						  =				pipe(
                prompt=UpperCAmelCase						,			generator=UpperCAmelCase						,			guidance_scale=7.5						,			num_inference_steps=2						,			output_type="numpy"	).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                  pipe.save_pretrained(UpperCAmelCase	)
                  A_						  =				VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCAmelCase	)
            pipe.to(UpperCAmelCase	)
            pipe.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				generator.manual_seed(0	)
            A_						  =				pipe(
                prompt=UpperCAmelCase						,			generator=UpperCAmelCase						,			guidance_scale=7.5						,			num_inference_steps=2						,			output_type="numpy"	).images

            assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







      def 		__A	(   self							:				Optional[int]	):
            A_						  =				VersatileDiffusionTextToImagePipeline.from_pretrained(
                "shi-labs/versatile-diffusion"						,			torch_dtype=torch.floataa	)
            pipe.to(UpperCAmelCase	)
            pipe.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				"A painting of a squirrel eating a burger "
            A_						  =				torch.manual_seed(0	)
            A_						  =				pipe(
                prompt=UpperCAmelCase						,			generator=UpperCAmelCase						,			guidance_scale=7.5						,			num_inference_steps=50						,			output_type="numpy"	).images

            A_						  =				image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            A_						  =				np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]	)

            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_torch_available,
    is_vision_available,
)


UpperCamelCase__		 =     {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     ['''ConvNextFeatureExtractor''']
    UpperCamelCase__		 =     ['''ConvNextImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextForImageClassification''',
        '''ConvNextModel''',
        '''ConvNextPreTrainedModel''',
        '''ConvNextBackbone''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''TFConvNextForImageClassification''',
        '''TFConvNextModel''',
        '''TFConvNextPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_convnext import ConvNextFeatureExtractor
        from .image_processing_convnext import ConvNextImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnext import (
            CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextBackbone,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
    import sys

    UpperCamelCase__		 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

