


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   SCREAMING_SNAKE_CASE_			(unittest.TestCase						):




       '''simple docstring'''



       @property
       def 				_lowerCAmelCase							(						self				:				str							)					->Optional[Any]:
          torch.manual_seed(0							)
          lowerCamelCase_						:   Any		= 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 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Dict:
          lowerCamelCase_						:   Optional[int]		= self.dummy_uncond_unet
          lowerCamelCase_						:   str		= PNDMScheduler()

          lowerCamelCase_						:   List[str]		= PNDMPipeline(unet=__a		,				scheduler=__a							)
          pndm.to(__a							)
          pndm.set_progress_bar_config(disable=__a							)

          lowerCamelCase_						:   int		= torch.manual_seed(0							)
          lowerCamelCase_						:   Optional[int]		= pndm(generator=__a		,				num_inference_steps=20		,				output_type="""numpy"""							).images

          lowerCamelCase_						:   Optional[int]		= torch.manual_seed(0							)
          lowerCamelCase_						:   Tuple		= pndm(generator=__a		,				num_inference_steps=20		,				output_type="""numpy"""		,				return_dict=__a							)[0]

          lowerCamelCase_						:   int		= image[0, -3:, -3:, -1]
          lowerCamelCase_						:   Optional[int]		= image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowerCamelCase_						:   Any		= 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   SCREAMING_SNAKE_CASE_			(unittest.TestCase						):




       '''simple docstring'''



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

          lowerCamelCase_						:   str		= UNetaDModel.from_pretrained(__a							)
          lowerCamelCase_						:   Tuple		= PNDMScheduler()

          lowerCamelCase_						:   str		= PNDMPipeline(unet=__a		,				scheduler=__a							)
          pndm.to(__a							)
          pndm.set_progress_bar_config(disable=__a							)
          lowerCamelCase_						:   Union[str, Any]		= torch.manual_seed(0							)
          lowerCamelCase_						:   Tuple		= pndm(generator=__a		,				output_type="""numpy"""							).images

          lowerCamelCase_						:   List[str]		= image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowerCamelCase_						:   Optional[int]		= np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]							)

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









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



       def 				_lowerCAmelCase							(						self				:				str							)					->bool:
          return self.size == 0



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          return False if self.is_empty() else self.array[self.front]



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp





