



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  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		@property
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						torch.manual_seed(0				)
						lowerCAmelCase__  :       Tuple				=  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				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.dummy_uncond_unet
						lowerCAmelCase__  :       Union[str, Any]				=  PNDMScheduler()

						lowerCAmelCase__  :       Union[str, Any]				=  PNDMPipeline(unet=snake_case_	,scheduler=snake_case_				)
						pndm.to(snake_case_				)
						pndm.set_progress_bar_config(disable=snake_case_				)

						lowerCAmelCase__  :       Union[str, Any]				=  torch.manual_seed(0				)
						lowerCAmelCase__  :       List[Any]				=  pndm(generator=snake_case_	,num_inference_steps=20	,output_type='''numpy'''				).images

						lowerCAmelCase__  :       List[Any]				=  torch.manual_seed(0				)
						lowerCAmelCase__  :       Dict				=  pndm(generator=snake_case_	,num_inference_steps=20	,output_type='''numpy'''	,return_dict=snake_case_				)[0]

						lowerCAmelCase__  :       str				=  image[0, -3:, -3:, -1]
						lowerCAmelCase__  :       Union[str, Any]				=  image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						lowerCAmelCase__  :       Dict				=  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  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  '''google/ddpm-cifar10-32'''

						lowerCAmelCase__  :       List[str]				=  UNetaDModel.from_pretrained(snake_case_				)
						lowerCAmelCase__  :       str				=  PNDMScheduler()

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

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

						assert image.shape == (1, 32, 32, 3)
						lowerCAmelCase__  :       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






import warnings

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


__snake_case       :	List[Any]            =logging.get_logger(__name__)

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



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

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

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  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.'''				)

