






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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[torch.FloatTensor]			=	None
     UpperCamelCase_							:		torch.FloatTensor			=	None
     UpperCamelCase_							:		Optional[Tuple[torch.FloatTensor]]			=	None
     UpperCamelCase_							:		Optional[Tuple[torch.FloatTensor]]			=	None





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	List[str]					, UpperCAmelCase_       :	Any=1					, UpperCAmelCase_       :	Dict=0					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	Optional[int]=512					, UpperCAmelCase_       :	Dict="cls"					, UpperCAmelCase_       :	Optional[Any]=False					, UpperCAmelCase_       :	Union[str, Any]=True					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    project_dim
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    pooler_fn
       SCREAMING_SNAKE_CASE       :      List[Any]							=    learn_encoder
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    use_attention_mask





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	[r'''pooler''', r'''logit_scale''']
     UpperCamelCase_							:		Optional[Any]			=	[r'''position_ids''', r'''predictions.decoder.bias''']
     UpperCamelCase_							:		str			=	'''roberta'''
     UpperCamelCase_							:		Optional[Any]			=	RobertaSeriesConfig


     def __init__(				self       :	Tuple					, UpperCAmelCase_       :	List[str]      ):
       super().__init__(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    XLMRobertaModel(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    nn.Linear(config.hidden_size					, config.project_dim      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    getattr(UpperCAmelCase_					, "has_pre_transformation"					, UpperCAmelCase_      )
       if self.has_pre_transformation:
         SCREAMING_SNAKE_CASE       :      str							=    nn.Linear(config.hidden_size					, config.project_dim      )
         SCREAMING_SNAKE_CASE       :      List[Any]							=    nn.LayerNorm(config.hidden_size					, eps=config.layer_norm_eps      )
       self.post_init()






     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[torch.Tensor] = None					, UpperCAmelCase_       :	Optional[bool] = None					, UpperCAmelCase_       :	Optional[bool] = None					, UpperCAmelCase_       :	Optional[bool] = None					, ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    return_dict if return_dict is not None else self.config.use_return_dict

       SCREAMING_SNAKE_CASE       :      int							=    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:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    outputs["hidden_states"][-2]
         SCREAMING_SNAKE_CASE       :      int							=    self.pre_LN(UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Tuple							=    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:
         SCREAMING_SNAKE_CASE       :      Tuple							=    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 gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	List[Any]      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
       SCREAMING_SNAKE_CASE       :      int							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      str							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    output.images

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]      )

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


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      str							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      str							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    output.images

       SCREAMING_SNAKE_CASE       :      Dict							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-1






     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_dpmpp_2m"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    sd_pipe(
           [prompt]					, generator=UpperCAmelCase_					, guidance_scale=7.5					, num_inference_steps=15					, output_type="np"					, use_karras_sigmas=UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    output.images

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.array(
           [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]      )

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






