


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      __snake_case					       =   {
          """sld_warmup_steps""": 15,
          """sld_guidance_scale""": 20,
          """sld_threshold""": 0.0,
          """sld_momentum_scale""": 0.0,
          """sld_mom_beta""": 0.0,
      }
      __snake_case					       =   {
          """sld_warmup_steps""": 10,
          """sld_guidance_scale""": 1000,
          """sld_threshold""": 0.01,
          """sld_momentum_scale""": 0.3,
          """sld_mom_beta""": 0.4,
      }
      __snake_case					       =   {
          """sld_warmup_steps""": 7,
          """sld_guidance_scale""": 2000,
          """sld_threshold""": 0.0_25,
          """sld_momentum_scale""": 0.5,
          """sld_mom_beta""": 0.7,
      }
      __snake_case					       =   {
          """sld_warmup_steps""": 0,
          """sld_guidance_scale""": 5000,
          """sld_threshold""": 1.0,
          """sld_momentum_scale""": 0.5,
          """sld_mom_beta""": 0.7,
      }



@dataclass
class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      __snake_case					       =   42
      __snake_case					       =   42
      __snake_case					       =   42
      __snake_case					       =   42


if is_transformers_available() and is_torch_available():
       from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
       from .safety_checker import SafeStableDiffusionSafetyChecker





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import CamembertModel


@require_torch
@require_sentencepiece
@require_tokenizers
class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

