





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[torch.FloatTensor]			     = None
			lowerCamelCase_	:	torch.FloatTensor			     = None
			lowerCamelCase_	:	Optional[Tuple[torch.FloatTensor]]			     = None
			lowerCamelCase_	:	Optional[Tuple[torch.FloatTensor]]			     = None




class    __lowerCAmelCase			(     _a	):






			def __init__(self   ,				__magic_name__=1   ,				__magic_name__=0   ,				__magic_name__=2   ,				__magic_name__=512   ,				__magic_name__="cls"   ,				__magic_name__=False   ,				__magic_name__=True   ,				**__magic_name__   ,				)	->  str:


							'''simple docstring'''



							super().__init__(pad_token_id=__UpperCAmelCase   ,				bos_token_id=__UpperCAmelCase   ,				eos_token_id=__UpperCAmelCase   ,				**__UpperCAmelCase       )
							snake_case_		:       Optional[Any]           =    project_dim
							snake_case_		:       Optional[int]           =    pooler_fn
							snake_case_		:       Optional[int]           =    learn_encoder
							snake_case_		:       str           =    use_attention_mask




class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Dict			     = [R"pooler", R"logit_scale"]
			lowerCamelCase_	:	Tuple			     = [R"position_ids", R"predictions.decoder.bias"]
			lowerCamelCase_	:	Optional[int]			     = "roberta"
			lowerCamelCase_	:	Tuple			     = RobertaSeriesConfig






			def __init__(self   ,				__magic_name__       )	->  Tuple:


							'''simple docstring'''



							super().__init__(__UpperCAmelCase       )
							snake_case_		:       Union[str, Any]           =    XLMRobertaModel(__UpperCAmelCase       )
							snake_case_		:       Optional[Any]           =    nn.Linear(config.hidden_size   ,				config.project_dim       )
							snake_case_		:       Any           =    getattr(__UpperCAmelCase   ,				'''has_pre_transformation'''   ,				__UpperCAmelCase       )
							if self.has_pre_transformation:
											snake_case_		:       int           =    nn.Linear(config.hidden_size   ,				config.project_dim       )
											snake_case_		:       int           =    nn.LayerNorm(config.hidden_size   ,				eps=config.layer_norm_eps       )
							self.post_init()






			def      lowerCamelCase    (self   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				)	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    return_dict if return_dict is not None else self.config.use_return_dict

							snake_case_		:       List[Any]           =    self.base_model(
							    input_ids=__UpperCAmelCase   ,				attention_mask=__UpperCAmelCase   ,				token_type_ids=__UpperCAmelCase   ,				position_ids=__UpperCAmelCase   ,				head_mask=__UpperCAmelCase   ,				inputs_embeds=__UpperCAmelCase   ,				encoder_hidden_states=__UpperCAmelCase   ,				encoder_attention_mask=__UpperCAmelCase   ,				output_attentions=__UpperCAmelCase   ,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,				return_dict=__UpperCAmelCase   ,				)

							if self.has_pre_transformation:
											snake_case_		:       List[Any]           =    outputs["hidden_states"][-2]
											snake_case_		:       str           =    self.pre_LN(__UpperCAmelCase       )
											snake_case_		:       Optional[Any]           =    self.transformation_pre(__UpperCAmelCase       )

											return TransformationModelOutput(
											    projection_state=__UpperCAmelCase   ,				last_hidden_state=outputs.last_hidden_state   ,				hidden_states=outputs.hidden_states   ,				attentions=outputs.attentions   ,				)
							else:
											snake_case_		:       Union[str, Any]           =    self.transformation(outputs.last_hidden_state       )
											return TransformationModelOutput(
											    projection_state=__UpperCAmelCase   ,				last_hidden_state=outputs.last_hidden_state   ,				hidden_states=outputs.hidden_states   ,				attentions=outputs.attentions   ,				)





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 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							str      						= 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 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.dummy_uncond_unet
											a__	:							Optional[Any]      						= PNDMScheduler()

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

											a__	:							Tuple      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy"				).images

											a__	:							Optional[int]      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy" ,					return_dict=__UpperCAmelCase				)[0]

											a__	:							str      						= image[0, -3:, -3:, -1]
											a__	:							int      						= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 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 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= "google/ddpm-cifar10-32"

											a__	:							List[Any]      						= UNetaDModel.from_pretrained(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							int      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= torch.manual_seed(0				)
											a__	:							Union[str, Any]      						= pndm(generator=__UpperCAmelCase ,					output_type="numpy"				).images

											a__	:							List[Any]      						= image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							Dict      						= np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

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


