

from typing import TYPE_CHECKING

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


lowerCAmelCase						:   Tuple										=    {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase						:   Optional[Any]										=    ['''SegformerFeatureExtractor''']
       lowerCAmelCase						:   Optional[Any]										=    ['''SegformerImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase						:   int										=    [
           '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''SegformerDecodeHead''',
           '''SegformerForImageClassification''',
           '''SegformerForSemanticSegmentation''',
           '''SegformerLayer''',
           '''SegformerModel''',
           '''SegformerPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase						:   Union[str, Any]										=    [
           '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFSegformerDecodeHead''',
           '''TFSegformerForImageClassification''',
           '''TFSegformerForSemanticSegmentation''',
           '''TFSegformerModel''',
           '''TFSegformerPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_segformer import SegformerFeatureExtractor
              from .image_processing_segformer import SegformerImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_segformer import (
                  SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SegformerDecodeHead,
                  SegformerForImageClassification,
                  SegformerForSemanticSegmentation,
                  SegformerLayer,
                  SegformerModel,
                  SegformerPreTrainedModel,
              )
       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_segformer import (
                  TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFSegformerDecodeHead,
                  TFSegformerForImageClassification,
                  TFSegformerForSemanticSegmentation,
                  TFSegformerModel,
                  TFSegformerPreTrainedModel,
              )

else:
       import sys

       lowerCAmelCase						:   Optional[Any]										=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       @property
       def UpperCAmelCase_						(						self       :						Any							)			->    List[str]:
             torch.manual_seed(0							)
             snake_case__					=   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 UpperCAmelCase_						(						self       :						Optional[int]							)			->    int:
             snake_case__					=   """cpu"""
             snake_case__					=   self.dummy_uncond_unet
             snake_case__					=   DDPMScheduler()

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							).images

             snake_case__					=   torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""    ,		return_dict=lowerCAmelCase__							)[0]

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

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   np.array(
                 [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]							)

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

       def UpperCAmelCase_						(						self       :						Tuple							)			->    str:
             snake_case__					=   self.dummy_uncond_unet
             snake_case__					=   DDPMScheduler(prediction_type="""sample"""							)

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							).images

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							)[0]

             snake_case__					=   image[0, -3:, -3:, -1]
             snake_case__					=   image_eps[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   1E-2 if torch_device != """mps""" else 3E-2
             assert np.abs(image_slice.flatten() - image_eps_slice.flatten()							).max() < tolerance



@slow
@require_torch_gpu
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       def UpperCAmelCase_						(						self       :						Tuple							)			->    Optional[int]:
             snake_case__					=   """google/ddpm-cifar10-32"""

             snake_case__					=   UNetaDModel.from_pretrained(lowerCAmelCase__							)
             snake_case__					=   DDPMScheduler.from_pretrained(lowerCAmelCase__							)

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		output_type="""numpy"""							).images

             snake_case__					=   image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]							)
             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2






