"""simple docstring"""






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     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

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



class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

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



class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[Any]						    = [R"pooler", R"logit_scale"]
    _UpperCAmelCase	:str						    = [R"position_ids", R"predictions.decoder.bias"]
    _UpperCAmelCase	:Tuple						    = "roberta"
    _UpperCAmelCase	:List[str]						    = RobertaSeriesConfig






    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__(_UpperCAmelCase							)
       lowercase__:   str					=      XLMRobertaModel(_UpperCAmelCase							)
       lowercase__:   List[Any]					=      nn.Linear(config.hidden_size    ,			config.project_dim							)
       lowercase__:   Any					=      getattr(_UpperCAmelCase    ,			'''has_pre_transformation'''    ,			_UpperCAmelCase							)
       if self.has_pre_transformation:
          lowercase__:   str					=      nn.Linear(config.hidden_size    ,			config.project_dim							)
          lowercase__:   str					=      nn.LayerNorm(config.hidden_size    ,			eps=config.layer_norm_eps							)
       self.post_init()






    def _snake_case			(   self    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       lowercase__:   Dict					=      return_dict if return_dict is not None else self.config.use_return_dict

       lowercase__:   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:
          lowercase__:   List[str]					=      outputs['''hidden_states'''][-2]
          lowercase__:   Optional[int]					=      self.pre_LN(_UpperCAmelCase							)
          lowercase__:   Dict					=      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:
          lowercase__:   int					=      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    ,			)

"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

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