



'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, 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					:				int ):







							'''simple docstring'''
							torch.manual_seed(0 )
							_UpperCamelCase					:		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 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   self.dummy_uncond_unet
							_UpperCamelCase					:		List[Any] 				=   ScoreSdeVeScheduler()

							_UpperCamelCase					:		Optional[int] 				=   ScoreSdeVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
							sde_ve.to(lowerCamelCase__ )
							sde_ve.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		str 				=   sde_ve(num_inference_steps=2 ,output_type='numpy' ,generator=lowerCamelCase__ ).images

							_UpperCamelCase					:		Dict 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		Union[str, Any] 				=   sde_ve(num_inference_steps=2 ,output_type='numpy' ,generator=lowerCamelCase__ ,return_dict=lowerCamelCase__ )[
							    0
							]

							_UpperCamelCase					:		Optional[Any] 				=   image[0, -3:, -3:, -1]
							_UpperCamelCase					:		List[Any] 				=   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_UpperCamelCase					:		List[str] 				=   np.array([0.0, 1.0, 0.0, 0.0, 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					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   'google/ncsnpp-church-256'
							_UpperCamelCase					:		Union[str, Any] 				=   UNetaDModel.from_pretrained(lowerCamelCase__ )

							_UpperCamelCase					:		List[str] 				=   ScoreSdeVeScheduler.from_pretrained(lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   ScoreSdeVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
							sde_ve.to(lowerCamelCase__ )
							sde_ve.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		int 				=   sde_ve(num_inference_steps=10 ,output_type='numpy' ,generator=lowerCamelCase__ ).images

							_UpperCamelCase					:		str 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							_UpperCamelCase					:		int 				=   np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )

							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


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







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



