





import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  lowercase__			    =						XCLIPTextConfig()

  # derive patch size from model name
  lowercase__			    =						model_name.find('patch'	)
  lowercase__			    =						int(model_name[start_idx + len('patch'	) : start_idx + len('patch'	) + 2]	)
  lowercase__			    =						XCLIPVisionConfig(patch_size=_SCREAMING_SNAKE_CASE	,		num_frames=_SCREAMING_SNAKE_CASE	)

  if "large" in model_name:
    lowercase__			    =						768
    lowercase__			    =						3072
    lowercase__			    =						12

    lowercase__			    =						1024
    lowercase__			    =						4096
    lowercase__			    =						16
    lowercase__			    =						24
    lowercase__			    =						768
    lowercase__			    =						3072

  if model_name == "xclip-large-patch14-16-frames":
    lowercase__			    =						336

  lowercase__			    =						XCLIPConfig.from_text_vision_configs(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  if "large" in model_name:
    lowercase__			    =						768

  return config




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Union[str, Any]:
  # text encoder
  if name == "token_embedding.weight":
    lowercase__			    =						name.replace('token_embedding.weight'	,		'text_model.embeddings.token_embedding.weight'	)
  if name == "positional_embedding":
    lowercase__			    =						name.replace('positional_embedding'	,		'text_model.embeddings.position_embedding.weight'	)
  if "ln_1" in name:
    lowercase__			    =						name.replace('ln_1'	,		'layer_norm1'	)
  if "ln_2" in name:
    lowercase__			    =						name.replace('ln_2'	,		'layer_norm2'	)
  if "c_fc" in name:
    lowercase__			    =						name.replace('c_fc'	,		'fc1'	)
  if "c_proj" in name:
    lowercase__			    =						name.replace('c_proj'	,		'fc2'	)
  if name.startswith('transformer.resblocks'	):
    lowercase__			    =						name.replace('transformer.resblocks'	,		'text_model.encoder.layers'	)
  if "attn.out_proj" in name and "message" not in name:
    lowercase__			    =						name.replace('attn.out_proj'	,		'self_attn.out_proj'	)
  if "ln_final" in name:
    lowercase__			    =						name.replace('ln_final'	,		'text_model.final_layer_norm'	)
  # visual encoder
  if name == "visual.class_embedding":
    lowercase__			    =						name.replace('visual.class_embedding'	,		'vision_model.embeddings.class_embedding'	)
  if name == "visual.positional_embedding":
    lowercase__			    =						name.replace('visual.positional_embedding'	,		'vision_model.embeddings.position_embedding.weight'	)
  if name.startswith('visual.transformer.resblocks'	):
    lowercase__			    =						name.replace('visual.transformer.resblocks'	,		'vision_model.encoder.layers'	)
  if "visual.conv1" in name:
    lowercase__			    =						name.replace('visual.conv1'	,		'vision_model.embeddings.patch_embedding'	)
  if "visual.ln_pre" in name:
    lowercase__			    =						name.replace('visual.ln_pre'	,		'vision_model.pre_layernorm'	)
  if "visual.ln_post" in name:
    lowercase__			    =						name.replace('visual.ln_post'	,		'vision_model.post_layernorm'	)
  if "visual.proj" in name:
    lowercase__			    =						name.replace('visual.proj'	,		'visual_projection.weight'	)
  if "text_projection" in name:
    lowercase__			    =						name.replace('text_projection'	,		'text_projection.weight'	)
  # things on top
  if "prompts_visual_proj" in name:
    lowercase__			    =						name.replace('prompts_visual_proj'	,		'prompts_visual_projection'	)
  if "prompts_visual_ln" in name:
    lowercase__			    =						name.replace('prompts_visual_ln'	,		'prompts_visual_layernorm'	)
  # mit
  if name == "mit.positional_embedding":
    lowercase__			    =						name.replace('positional'	,		'position'	)
  if name.startswith('mit.resblocks'	):
    lowercase__			    =						name.replace('mit.resblocks'	,		'mit.encoder.layers'	)
  # prompts generator
  if name.startswith('prompts_generator.norm'	):
    lowercase__			    =						name.replace('prompts_generator.norm'	,		'prompts_generator.layernorm'	)

  return name




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  for key in orig_state_dict.copy().keys():
    lowercase__			    =						orig_state_dict.pop(_SCREAMING_SNAKE_CASE	)

    if "attn.in_proj" in key:
      lowercase__			    =						key.split('.'	)
      if key.startswith('visual'	):
        lowercase__			    =						key_split[3]
        lowercase__			    =						config.vision_config.hidden_size
        if "message_attn" in key:
          if "weight" in key:
            lowercase__			    =						val[
                :dim, :
            ]
            lowercase__			    =						val[
                dim : dim * 2, :
            ]
            lowercase__			    =						val[
                -dim:, :
            ]
          else:
            lowercase__			    =						val[
                :dim
            ]
            lowercase__			    =						val[
                dim : dim * 2
            ]
            lowercase__			    =						val[
                -dim:
            ]
        else:
          if "weight" in key:
            lowercase__			    =						val[
                :dim, :
            ]
            lowercase__			    =						val[
                dim : dim * 2, :
            ]
            lowercase__			    =						val[
                -dim:, :
            ]
          else:
            lowercase__			    =						val[:dim]
            lowercase__			    =						val[
                dim : dim * 2
            ]
            lowercase__			    =						val[-dim:]
      elif key.startswith('mit'	):
        lowercase__			    =						key_split[2]
        lowercase__			    =						config.vision_config.mit_hidden_size
        if "weight" in key:
          lowercase__			    =						val[:dim, :]
          lowercase__			    =						val[dim : dim * 2, :]
          lowercase__			    =						val[-dim:, :]
        else:
          lowercase__			    =						val[:dim]
          lowercase__			    =						val[dim : dim * 2]
          lowercase__			    =						val[-dim:]
      else:
        lowercase__			    =						key_split[2]
        lowercase__			    =						config.text_config.hidden_size
        if "weight" in key:
          lowercase__			    =						val[:dim, :]
          lowercase__			    =						val[
              dim : dim * 2, :
          ]
          lowercase__			    =						val[-dim:, :]
        else:
          lowercase__			    =						val[:dim]
          lowercase__			    =						val[
              dim : dim * 2
          ]
          lowercase__			    =						val[-dim:]
    else:
      lowercase__			    =						rename_key(_SCREAMING_SNAKE_CASE	)
      if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
        lowercase__			    =						val.T
      lowercase__			    =						val

  return orig_state_dict




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
  if num_frames == 8:
    lowercase__			    =						'eating_spaghetti_8_frames.npy'
  elif num_frames == 16:
    lowercase__			    =						'eating_spaghetti.npy'
  elif num_frames == 32:
    lowercase__			    =						'eating_spaghetti_32_frames.npy'
  lowercase__			    =						hf_hub_download(
      repo_id='hf-internal-testing/spaghetti-video'	,		filename=_SCREAMING_SNAKE_CASE	,		repo_type='dataset'	,		)
  lowercase__			    =						np.load(_SCREAMING_SNAKE_CASE	)
  return list(_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=False	)       ->     List[str]:
  lowercase__			    =						{
      # fully supervised kinetics-400 checkpoints
      'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
      'xclip-base-patch32-16-frames': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
      ),
      'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
      'xclip-base-patch16-16-frames': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
      ),
      'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
      'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
      # fully supervised kinetics-600 checkpoints
      'xclip-base-patch16-kinetics-600': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
      ),
      'xclip-base-patch16-kinetics-600-16-frames': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
      ),
      'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
      # few shot
      'xclip-base-patch16-hmdb-2-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
      ),
      'xclip-base-patch16-hmdb-4-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
      ),
      'xclip-base-patch16-hmdb-8-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
      ),
      'xclip-base-patch16-hmdb-16-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
      ),
      'xclip-base-patch16-ucf-2-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
      ),
      'xclip-base-patch16-ucf-4-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
      ),
      'xclip-base-patch16-ucf-8-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
      ),
      'xclip-base-patch16-ucf-16-shot': (
          'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
      ),
      # zero shot
      'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
  }

  lowercase__			    =						model_to_url[model_name]
  lowercase__			    =						8
  if "16-frames" in model_name:
    lowercase__			    =						16
  elif "shot" in model_name:
    lowercase__			    =						32

  lowercase__			    =						get_xclip_config(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						XCLIPModel(_SCREAMING_SNAKE_CASE	)
  model.eval()

  if "drive" in checkpoint_url:
    lowercase__			    =						'pytorch_model.bin'
    gdown.cached_download(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		quiet=_SCREAMING_SNAKE_CASE	)
    lowercase__			    =						torch.load(_SCREAMING_SNAKE_CASE	,		map_location='cpu'	)['model']
  else:
    lowercase__			    =						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	)['model']

  lowercase__			    =						convert_state_dict(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						XCLIPModel(_SCREAMING_SNAKE_CASE	)
  lowercase__    ,					lowercase__			    =						model.load_state_dict(_SCREAMING_SNAKE_CASE	,		strict=_SCREAMING_SNAKE_CASE	)
  assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
  model.eval()

  lowercase__			    =						336 if model_name == 'xclip-large-patch14-16-frames' else 224
  lowercase__			    =						VideoMAEImageProcessor(size=_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'	)
  lowercase__			    =						CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'	)
  lowercase__			    =						XCLIPProcessor(image_processor=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						prepare_video(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						processor(
      text=['playing sports', 'eating spaghetti', 'go shopping']	,		videos=_SCREAMING_SNAKE_CASE	,		return_tensors='pt'	,		padding=_SCREAMING_SNAKE_CASE	)

  print('Shape of pixel values:'	,		inputs.pixel_values.shape	)

  with torch.no_grad():
    lowercase__			    =						model(**_SCREAMING_SNAKE_CASE	)

  # Verify outputs
  lowercase__			    =						outputs.logits_per_video
  lowercase__			    =						logits_per_video.softmax(dim=1	)
  print('Probs:'	,		_SCREAMING_SNAKE_CASE	)
  # kinetics-400
  if model_name == "xclip-base-patch32":
    lowercase__			    =						torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]	)
  elif model_name == "xclip-base-patch32-16-frames":
    lowercase__			    =						torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]	)
  elif model_name == "xclip-base-patch16":
    lowercase__			    =						torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]	)
  elif model_name == "xclip-base-patch16-16-frames":
    lowercase__			    =						torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]	)
  elif model_name == "xclip-large-patch14":
    lowercase__			    =						torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]	)
  elif model_name == "xclip-large-patch14-16-frames":
    lowercase__			    =						torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]	)
  # kinetics-600
  elif model_name == "xclip-base-patch16-kinetics-600":
    lowercase__			    =						torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]	)
  elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
    lowercase__			    =						torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]	)
  elif model_name == "xclip-large-patch14-kinetics-600":
    lowercase__			    =						torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]	)
  # few shot
  elif model_name == "xclip-base-patch16-hmdb-2-shot":
    lowercase__			    =						torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]	)
  elif model_name == "xclip-base-patch16-hmdb-4-shot":
    lowercase__			    =						torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]	)
  elif model_name == "xclip-base-patch16-hmdb-8-shot":
    lowercase__			    =						torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]	)
  elif model_name == "xclip-base-patch16-hmdb-16-shot":
    lowercase__			    =						torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]	)
  elif model_name == "xclip-base-patch16-ucf-2-shot":
    lowercase__			    =						torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]	)
  elif model_name == "xclip-base-patch16-ucf-4-shot":
    lowercase__			    =						torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]	)
  elif model_name == "xclip-base-patch16-ucf-8-shot":
    lowercase__			    =						torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]	)
  elif model_name == "xclip-base-patch16-ucf-16-shot":
    lowercase__			    =						torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]	)
  # zero shot
  elif model_name == "xclip-base-patch16-zero-shot":
    lowercase__			    =						torch.tensor([[3.5_082E-04, 9.9_785E-01, 1.7_966E-03]]	)
  else:
    raise ValueError(F"""Model name {model_name} not supported"""	)
  assert torch.allclose(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		atol=1E-3	)
  print('Looks ok!'	)

  if pytorch_dump_folder_path is not None:
    print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(_SCREAMING_SNAKE_CASE	)

  if push_to_hub:
    print('Pushing model, processor and slow tokenizer files to the hub...'	)
    model.push_to_hub(_SCREAMING_SNAKE_CASE	,		organization='nielsr'	)
    processor.push_to_hub(_SCREAMING_SNAKE_CASE	,		organization='nielsr'	)
    slow_tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE	,		organization='nielsr'	)


if __name__ == "__main__":
      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""xclip-base-patch32""",
          type=str,
          help="""Name of the model.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      lowercase_					     =	parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	List[Any]				      = StableDiffusionSAGPipeline
   _UpperCamelCase					:	str				      = TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase					:	Dict				      = TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase					:	str				      = TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase					:	Optional[int]				      = TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase					:	Union[str, Any]				      = False


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						UNetaDConditionModel(
         block_out_channels=(32, 64)    ,  layers_per_block=2    ,  sample_size=32    ,  in_channels=4    ,  out_channels=4    ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,  cross_attention_dim=32    ,  )
     lowercase__			    =						DDIMScheduler(
         beta_start=0.00085    ,  beta_end=0.012    ,  beta_schedule='scaled_linear'    ,  clip_sample=a    ,  set_alpha_to_one=a    ,  )
     torch.manual_seed(0 )
     lowercase__			    =						AutoencoderKL(
         block_out_channels=[32, 64]    ,  in_channels=3    ,  out_channels=3    ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,  latent_channels=4    ,  )
     torch.manual_seed(0 )
     lowercase__			    =						CLIPTextConfig(
         bos_token_id=0    ,  eos_token_id=2    ,  hidden_size=32    ,  intermediate_size=37    ,  layer_norm_eps=1E-0_5    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  pad_token_id=1    ,  vocab_size=1_000    ,  )
     lowercase__			    =						CLIPTextModel(a )
     lowercase__			    =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

     lowercase__			    =						{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[int]    ,  a    :       Any=0 )->     Union[str, Any]:





     """simple docstring"""







     if str(a ).startswith('mps' ):
       lowercase__			    =						torch.manual_seed(a )
     else:
       lowercase__			    =						torch.Generator(device=a ).manual_seed(a )
     lowercase__			    =						{
         'prompt': '.',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 1.0,
         'sag_scale': 1.0,
         'output_type': 'numpy',
     }
     return inputs


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Any:





     """simple docstring"""







     super().test_inference_batch_single_identical(expected_max_diff=3E-3 )






@slow
@require_torch_gpu
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     int:





     """simple docstring"""







     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
     lowercase__			    =						sag_pipe.to(a )
     sag_pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'.'
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						sag_pipe(
         [prompt]    ,  generator=a    ,  guidance_scale=7.5    ,  sag_scale=1.0    ,  num_inference_steps=20    ,  output_type='np' )

     lowercase__			    =						output.images

     lowercase__			    =						image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
     lowercase__			    =						sag_pipe.to(a )
     sag_pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'.'
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						sag_pipe(
         [prompt]    ,  generator=a    ,  guidance_scale=7.5    ,  sag_scale=1.0    ,  num_inference_steps=20    ,  output_type='np' )

     lowercase__			    =						output.images

     lowercase__			    =						image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
     lowercase__			    =						sag_pipe.to(a )
     sag_pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'.'
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						sag_pipe(
         [prompt]    ,  width=768    ,  height=512    ,  generator=a    ,  guidance_scale=7.5    ,  sag_scale=1.0    ,  num_inference_steps=20    ,  output_type='np'    ,  )

     lowercase__			    =						output.images

     assert image.shape == (1, 512, 768, 3)


