





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     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					List[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     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self.dummy_uncond_unet
        A_       :					Optional[Any]        =      PNDMScheduler()

        A_       :					Optional[Any]        =      PNDMPipeline(unet=snake_case      ,   scheduler=snake_case	)
        pndm.to(snake_case	)
        pndm.set_progress_bar_config(disable=snake_case	)

        A_       :					Union[str, Any]        =      torch.manual_seed(0	)
        A_       :					Tuple        =      pndm(generator=snake_case      ,   num_inference_steps=20      ,   output_type="numpy"	).images

        A_       :					Union[str, Any]        =      torch.manual_seed(0	)
        A_       :					str        =      pndm(generator=snake_case      ,   num_inference_steps=20      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					List[Any]        =      image[0, -3:, -3:, -1]
        A_       :					int        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        A_       :					int        =      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     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      "google/ddpm-cifar10-32"

        A_       :					int        =      UNetaDModel.from_pretrained(snake_case	)
        A_       :					Dict        =      PNDMScheduler()

        A_       :					Optional[int]        =      PNDMPipeline(unet=snake_case      ,   scheduler=snake_case	)
        pndm.to(snake_case	)
        pndm.set_progress_bar_config(disable=snake_case	)
        A_       :					Optional[Any]        =      torch.manual_seed(0	)
        A_       :					str        =      pndm(generator=snake_case      ,   output_type="numpy"	).images

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

        assert image.shape == (1, 32, 32, 3)
        A_       :					Optional[int]        =      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







import logging

from transformers import PretrainedConfig


_lowerCAmelCase				:   str						       =						logging.getLogger(__name__)


_lowerCAmelCase				:   Dict						       =						{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''bertabs'''



     def __init__(				self							:Optional[int]      ,   snake_case							:Any=30_522      ,   snake_case							:List[str]=512      ,   snake_case							:str=6      ,   snake_case							:int=512      ,   snake_case							:Optional[Any]=8      ,   snake_case							:Tuple=512      ,   snake_case							:str=0.2      ,   snake_case							:Any=6      ,   snake_case							:Optional[Any]=768      ,   snake_case							:Optional[Any]=8      ,   snake_case							:List[Any]=2_048      ,   snake_case							:Dict=0.2      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)

        A_       :					List[str]        =      vocab_size
        A_       :					int        =      max_pos

        A_       :					Tuple        =      enc_layers
        A_       :					Tuple        =      enc_hidden_size
        A_       :					str        =      enc_heads
        A_       :					Optional[Any]        =      enc_ff_size
        A_       :					Optional[Any]        =      enc_dropout

        A_       :					List[str]        =      dec_layers
        A_       :					List[Any]        =      dec_hidden_size
        A_       :					Optional[int]        =      dec_heads
        A_       :					Any        =      dec_ff_size
        A_       :					Optional[int]        =      dec_dropout
