




"""simple docstring"""




import warnings

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


lowerCAmelCase							:    Optional[Any]  							=							logging.get_logger(__name__)

lowerCAmelCase							:    Tuple  							=							{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		'''mvp'''
				__UpperCamelCase    						=		['''past_key_values''']
				__UpperCamelCase    						=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





				def __init__( self	,				_a=50_267	,				_a=1_024	,				_a=12	,				_a=4_096	,				_a=16	,				_a=12	,				_a=4_096	,				_a=16	,				_a=0.0	,				_a=0.0	,				_a="gelu"	,				_a=1_024	,				_a=0.1	,				_a=0.0	,				_a=0.0	,				_a=0.02	,				_a=0.0	,				_a=False	,				_a=True	,				_a=1	,				_a=0	,				_a=2	,				_a=True	,				_a=2	,				_a=2	,				_a=False	,				_a=100	,				_a=800	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase       =      vocab_size
							lowerCamelCase       =      max_position_embeddings
							lowerCamelCase       =      d_model
							lowerCamelCase       =      encoder_ffn_dim
							lowerCamelCase       =      encoder_layers
							lowerCamelCase       =      encoder_attention_heads
							lowerCamelCase       =      decoder_ffn_dim
							lowerCamelCase       =      decoder_layers
							lowerCamelCase       =      decoder_attention_heads
							lowerCamelCase       =      dropout
							lowerCamelCase       =      attention_dropout
							lowerCamelCase       =      activation_dropout
							lowerCamelCase       =      activation_function
							lowerCamelCase       =      init_std
							lowerCamelCase       =      encoder_layerdrop
							lowerCamelCase       =      decoder_layerdrop
							lowerCamelCase       =      classifier_dropout
							lowerCamelCase       =      use_cache
							lowerCamelCase       =      encoder_layers
							lowerCamelCase       =      scale_embedding  # scale factor will be sqrt(d_model) if True
							lowerCamelCase       =      use_prompt
							lowerCamelCase       =      prompt_length
							lowerCamelCase       =      prompt_mid_dim

							super().__init__(
							    pad_token_id=A__	,				bos_token_id=A__	,				eos_token_id=A__	,				is_encoder_decoder=A__	,				decoder_start_token_id=A__	,				forced_eos_token_id=A__	,				**A__	,				)

							if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""	,				A__				):
										lowerCamelCase       =      self.bos_token_id
										warnings.warn(
										    f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
										    """The config can simply be saved and uploaded again to be fixed."""				)









"""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 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      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				):





							"""simple docstring"""
							lowerCamelCase       =      self.dummy_uncond_unet
							lowerCamelCase       =      ScoreSdeVeScheduler()

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a	,				return_dict=_a				)[
							    0
							]

							lowerCamelCase       =      image[0, -3:, -3:, -1]
							lowerCamelCase       =      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCamelCase       =      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 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """google/ncsnpp-church-256"""
							lowerCamelCase       =      UNetaDModel.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVeScheduler.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=10	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      image[0, -3:, -3:, -1]

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

							lowerCamelCase       =      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



