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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    int         =				UNetaDModel(
						    block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=3		,     out_channels=3		,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,     )
						return model







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.dummy_uncond_unet
						A_		:    Union[str, Any]         =				PNDMScheduler()

						A_		:    Dict         =				PNDMPipeline(unet=lowercase		,     scheduler=lowercase )
						pndm.to(lowercase )
						pndm.set_progress_bar_config(disable=lowercase )

						A_		:    List[str]         =				torch.manual_seed(0 )
						A_		:    List[str]         =				pndm(generator=lowercase		,     num_inference_steps=2_0		,     output_type='numpy' ).images

						A_		:    Union[str, Any]         =				torch.manual_seed(0 )
						A_		:    List[str]         =				pndm(generator=lowercase		,     num_inference_steps=2_0		,     output_type='numpy'		,     return_dict=lowercase )[0]

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

						assert image.shape == (1, 3_2, 3_2, 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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				'google/ddpm-cifar10-32'

						A_		:    List[str]         =				UNetaDModel.from_pretrained(lowercase )
						A_		:    List[Any]         =				PNDMScheduler()

						A_		:    int         =				PNDMPipeline(unet=lowercase		,     scheduler=lowercase )
						pndm.to(lowercase )
						pndm.set_progress_bar_config(disable=lowercase )
						A_		:    List[str]         =				torch.manual_seed(0 )
						A_		:    Tuple         =				pndm(generator=lowercase		,     output_type='numpy' ).images

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

						assert image.shape == (1, 3_2, 3_2, 3)
						A_		:    Optional[int]         =				np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125] )

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''lilt'''
				def __init__(						self		,     lowercase=3_0_5_2_2		,     lowercase=7_6_8		,     lowercase=1_2		,     lowercase=1_2		,     lowercase=3_0_7_2		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=5_1_2		,     lowercase=2		,     lowercase=0.02		,     lowercase=1E-12		,     lowercase=0		,     lowercase="absolute"		,     lowercase=None		,     lowercase=4		,     lowercase=1_0_2_4		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase		,     **lowercase )

						A_		:    str         =				vocab_size
						A_		:    Tuple         =				hidden_size
						A_		:    List[Any]         =				num_hidden_layers
						A_		:    Any         =				num_attention_heads
						A_		:    Union[str, Any]         =				hidden_act
						A_		:    int         =				intermediate_size
						A_		:    int         =				hidden_dropout_prob
						A_		:    Union[str, Any]         =				attention_probs_dropout_prob
						A_		:    List[str]         =				max_position_embeddings
						A_		:    List[str]         =				type_vocab_size
						A_		:    Any         =				initializer_range
						A_		:    str         =				layer_norm_eps
						A_		:    Optional[int]         =				position_embedding_type
						A_		:    Union[str, Any]         =				classifier_dropout
						A_		:    Optional[Any]         =				channel_shrink_ratio
						A_		:    int         =				max_ad_position_embeddings






