

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 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   @property
   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     torch.manual_seed(0       )
     a__  =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       )							->    int:






     """simple docstring"""






     a__  =self.dummy_uncond_unet
     a__  =PNDMScheduler()

     a__  =PNDMPipeline(unet=lowercase_,    scheduler=lowercase_       )
     pndm.to(lowercase_       )
     pndm.set_progress_bar_config(disable=lowercase_       )

     a__  =torch.manual_seed(0       )
     a__  =pndm(generator=lowercase_,    num_inference_steps=20,    output_type='''numpy'''       ).images

     a__  =torch.manual_seed(0       )
     a__  =pndm(generator=lowercase_,    num_inference_steps=20,    output_type='''numpy''',    return_dict=lowercase_       )[0]

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

     assert image.shape == (1, 32, 32, 3)
     a__  =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 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     a__  ='''google/ddpm-cifar10-32'''

     a__  =UNetaDModel.from_pretrained(lowercase_       )
     a__  =PNDMScheduler()

     a__  =PNDMPipeline(unet=lowercase_,    scheduler=lowercase_       )
     pndm.to(lowercase_       )
     pndm.set_progress_bar_config(disable=lowercase_       )
     a__  =torch.manual_seed(0       )
     a__  =pndm(generator=lowercase_,    output_type='''numpy'''       ).images

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

     assert image.shape == (1, 32, 32, 3)
     a__  =np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]       )

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''MvpTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''MvpForCausalLM''',
           '''MvpForConditionalGeneration''',
           '''MvpForQuestionAnswering''',
           '''MvpForSequenceClassification''',
           '''MvpModel''',
           '''MvpPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
