import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, 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      __a						(   self      :    Any				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_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      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.dummy_uncond_unet
     SCREAMING_SNAKE_CASE__      							=				PNDMScheduler()

     SCREAMING_SNAKE_CASE__      							=				PNDMPipeline(unet=_lowercase	,     scheduler=_lowercase				)
     pndm.to(_lowercase				)
     pndm.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pndm(generator=_lowercase	,     num_inference_steps=20	,     output_type="""numpy"""				).images

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pndm(generator=_lowercase	,     num_inference_steps=20	,     output_type="""numpy"""	,     return_dict=_lowercase				)[0]

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

     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE__      							=				np.array([1.0, 1.0, 0.0, 1.0, 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      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""google/ddpm-cifar10-32"""

     SCREAMING_SNAKE_CASE__      							=				UNetaDModel.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				PNDMScheduler()

     SCREAMING_SNAKE_CASE__      							=				PNDMPipeline(unet=_lowercase	,     scheduler=_lowercase				)
     pndm.to(_lowercase				)
     pndm.set_progress_bar_config(disable=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pndm(generator=_lowercase	,     output_type="""numpy"""				).images

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

     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE__      							=				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]				)

     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,
)


__lowerCamelCase      :			Optional[Any]							       =		{
    '''configuration_efficientformer''': [
        '''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientFormerConfig''',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Dict							       =		['''EfficientFormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''EfficientFormerForImageClassification''',
       '''EfficientFormerForImageClassificationWithTeacher''',
       '''EfficientFormerModel''',
       '''EfficientFormerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFEfficientFormerForImageClassification''',
       '''TFEfficientFormerForImageClassificationWithTeacher''',
       '''TFEfficientFormerModel''',
       '''TFEfficientFormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

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