'''simple docstring'''




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   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   @property
   def 			snake_case	(							self:    Optional[Any]	):
    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 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					'cpu'
    __UpperCAmelCase         =					self.dummy_uncond_unet
    __UpperCAmelCase         =					DDPMScheduler()

    __UpperCAmelCase         =					DDPMPipeline(unet=a			,scheduler=a	)
    ddpm.to(a	)
    ddpm.set_progress_bar_config(disable=a	)

    __UpperCAmelCase         =					torch.Generator(device=a	).manual_seed(0	)
    __UpperCAmelCase         =					ddpm(generator=a			,num_inference_steps=2			,output_type='numpy'	).images

    __UpperCAmelCase         =					torch.Generator(device=a	).manual_seed(0	)
    __UpperCAmelCase         =					ddpm(generator=a			,num_inference_steps=2			,output_type='numpy'			,return_dict=a	)[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(
        [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 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					self.dummy_uncond_unet
    __UpperCAmelCase         =					DDPMScheduler(prediction_type='sample'	)

    __UpperCAmelCase         =					DDPMPipeline(unet=a			,scheduler=a	)
    ddpm.to(a	)
    ddpm.set_progress_bar_config(disable=a	)

    __UpperCAmelCase         =					torch.manual_seed(0	)
    __UpperCAmelCase         =					ddpm(generator=a			,num_inference_steps=2			,output_type='numpy'	).images

    __UpperCAmelCase         =					torch.manual_seed(0	)
    __UpperCAmelCase         =					ddpm(generator=a			,num_inference_steps=2			,output_type='numpy'	)[0]

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

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase         =					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   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					'google/ddpm-cifar10-32'

    __UpperCAmelCase         =					UNetaDModel.from_pretrained(a	)
    __UpperCAmelCase         =					DDPMScheduler.from_pretrained(a	)

    __UpperCAmelCase         =					DDPMPipeline(unet=a			,scheduler=a	)
    ddpm.to(a	)
    ddpm.set_progress_bar_config(disable=a	)

    __UpperCAmelCase         =					torch.manual_seed(0	)
    __UpperCAmelCase         =					ddpm(generator=a			,output_type='numpy'	).images

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

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase         =					np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2




'''simple docstring'''




from typing import TYPE_CHECKING

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


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

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



