


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       lowercase_					(unittest.TestCase   ):





				@property
				def 						__UpperCamelCase    (				self)  -> str:
											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:
											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       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> str:
											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([0.15_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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase:						str   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='big_bird'





				def __init__(				self       ,  lowercase_=50358       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu_new"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=4096       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-12       ,  lowercase_=True       ,  lowercase_=0       ,  lowercase_=1       ,  lowercase_=2       ,  lowercase_=66       ,  lowercase_="block_sparse"       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_=64       ,  lowercase_=3       ,  lowercase_=None       ,  **lowercase_       ,  )  -> Any:
											super().__init__(
											    pad_token_id=lowercase_       ,  bos_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  sep_token_id=lowercase_       ,  **lowercase_       ,  )

											a__      =vocab_size
											a__      =max_position_embeddings
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =initializer_range
											a__      =type_vocab_size
											a__      =layer_norm_eps
											a__      =use_cache

											a__      =rescale_embeddings
											a__      =attention_type
											a__      =use_bias
											a__      =block_size
											a__      =num_random_blocks
											a__      =classifier_dropout







class       lowercase_					(lowercase__   ):





				@property
				def 						__UpperCamelCase    (				self)  -> Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		a__      ={0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		a__      ={0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ])

