


'''simple docstring'''

import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase:		Optional[Any]    						=  logging.get_logger(__name__)



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any] )		->       List[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    ConvNextVaConfig()

   if "atto" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 6, 2]
      UpperCamelCase__			    =    [40, 80, 1_60, 3_20]
   if "femto" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 6, 2]
      UpperCamelCase__			    =    [48, 96, 1_92, 3_84]
   if "pico" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 6, 2]
      UpperCamelCase__			    =    [64, 1_28, 2_56, 5_12]
   if "nano" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 8, 2]
      UpperCamelCase__			    =    [80, 1_60, 3_20, 6_40]
   if "tiny" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 9, 3]
      UpperCamelCase__			    =    [96, 1_92, 3_84, 7_68]
   if "base" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 27, 3]
      UpperCamelCase__			    =    [1_28, 2_56, 5_12, 10_24]
   if "large" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 27, 3]
      UpperCamelCase__			    =    [1_92, 3_84, 7_68, 15_36]
   if "huge" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 27, 3]
      UpperCamelCase__			    =    [3_52, 7_04, 14_08, 28_16]

   UpperCamelCase__			    =    10_00
   UpperCamelCase__			    =    "imagenet-1k-id2label.json"
   UpperCamelCase__			    =    (1, 10_00)

   UpperCamelCase__			    =    "huggingface/label-files"
   UpperCamelCase__			    =    num_labels
   UpperCamelCase__			    =    json.load(open(hf_hub_download(_UpperCamelCase     ,		_UpperCamelCase     ,		repo_type="dataset" )     ,		"r" ) )
   UpperCamelCase__			    =    {int(_UpperCamelCase ): v for k, v in idalabel.items()}

   UpperCamelCase__			    =    idalabel
   UpperCamelCase__			    =    {v: k for k, v in idalabel.items()}
   UpperCamelCase__			    =    hidden_sizes
   UpperCamelCase__			    =    depths

   return config, expected_shape



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple )		->       List[str]:





   '''simple docstring'''







   if "downsample_layers.0.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.0.0"     ,		"embeddings.patch_embeddings" )
   if "downsample_layers.0.1" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.0.1"     ,		"embeddings.norm" )  # we rename to layernorm later on
   if "downsample_layers.1.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.1.0"     ,		"stages.1.downsampling_layer.0" )
   if "downsample_layers.1.1" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.1.1"     ,		"stages.1.downsampling_layer.1" )
   if "downsample_layers.2.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.2.0"     ,		"stages.2.downsampling_layer.0" )
   if "downsample_layers.2.1" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.2.1"     ,		"stages.2.downsampling_layer.1" )
   if "downsample_layers.3.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.3.0"     ,		"stages.3.downsampling_layer.0" )
   if "downsample_layers.3.1" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.3.1"     ,		"stages.3.downsampling_layer.1" )
   if "stages" in name and "downsampling_layer" not in name:
      # stages.0.0. for instance should be renamed to stages.0.layers.0.
      UpperCamelCase__			    =    name[: len("stages.0" )] + ".layers" + name[len("stages.0" ) :]
   if "gamma" in name:
      UpperCamelCase__			    =    name.replace("gamma"     ,		"weight" )
   if "beta" in name:
      UpperCamelCase__			    =    name.replace("beta"     ,		"bias" )
   if "stages" in name:
      UpperCamelCase__			    =    name.replace("stages"     ,		"encoder.stages" )
   if "norm" in name:
      UpperCamelCase__			    =    name.replace("norm"     ,		"layernorm" )
   if "head" in name:
      UpperCamelCase__			    =    name.replace("head"     ,		"classifier" )

   return name



def 							SCREAMING_SNAKE_CASE__(		)		->       Any:





   '''simple docstring'''







   UpperCamelCase__			    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCamelCase__			    =    Image.open(requests.get(_UpperCamelCase     ,		stream=_UpperCamelCase ).raw )
   return im



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any )		->       str:





   '''simple docstring'''







   if "224" in checkpoint_url:
      UpperCamelCase__			    =    2_24
      UpperCamelCase__			    =    2_24 / 2_56
   elif "384" in checkpoint_url:
      UpperCamelCase__			    =    3_84
      UpperCamelCase__			    =    None
   else:
      UpperCamelCase__			    =    5_12
      UpperCamelCase__			    =    None

   return ConvNextImageProcessor(
       size=_UpperCamelCase     ,		crop_pct=_UpperCamelCase     ,		image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]     ,		image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]     ,		resample=PILImageResampling.BICUBIC     ,		)



@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Optional[Any]     ,		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Dict )		->       Union[str, Any]:





   '''simple docstring'''







   print("Downloading original model from checkpoint..." )
   # define ConvNeXTV2 configuration based on URL
   UpperCamelCase__    ,		UpperCamelCase__			    =    get_convnextva_config(_UpperCamelCase )
   # load original state_dict from URL
   UpperCamelCase__			    =    torch.hub.load_state_dict_from_url(_UpperCamelCase )["model"]

   print("Converting model parameters..." )
   # rename keys
   for key in state_dict.copy().keys():
      UpperCamelCase__			    =    state_dict.pop(_UpperCamelCase )
      UpperCamelCase__			    =    val
   # add prefix to all keys expect classifier head
   for key in state_dict.copy().keys():
      UpperCamelCase__			    =    state_dict.pop(_UpperCamelCase )
      if not key.startswith("classifier" ):
         UpperCamelCase__			    =    "convnextv2." + key
      UpperCamelCase__			    =    val

   # load HuggingFace model
   UpperCamelCase__			    =    ConvNextVaForImageClassification(_UpperCamelCase )
   model.load_state_dict(_UpperCamelCase )
   model.eval()

   # Check outputs on an image, prepared by ConvNextImageProcessor
   UpperCamelCase__			    =    convert_preprocessor(_UpperCamelCase )
   UpperCamelCase__			    =    preprocessor(images=prepare_img()     ,		return_tensors="pt" )
   UpperCamelCase__			    =    model(**_UpperCamelCase ).logits

   # note: the logits below were obtained without center cropping
   if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6] )
   else:
      raise ValueError(F'Unknown URL: {checkpoint_url}' )

   assert torch.allclose(logits[0, :5]     ,		_UpperCamelCase     ,		atol=1e-3 )
   assert logits.shape == expected_shape
   print("Model outputs match the original results!" )

   if save_model:
      print("Saving model to local..." )
      # Create folder to save model
      if not os.path.isdir(_UpperCamelCase ):
         os.mkdir(_UpperCamelCase )

      model.save_pretrained(_UpperCamelCase )
      preprocessor.save_pretrained(_UpperCamelCase )

   UpperCamelCase__			    =    "convnextv2"
   if "atto" in checkpoint_url:
      model_name += "-atto"
   if "femto" in checkpoint_url:
      model_name += "-femto"
   if "pico" in checkpoint_url:
      model_name += "-pico"
   if "nano" in checkpoint_url:
      model_name += "-nano"
   elif "tiny" in checkpoint_url:
      model_name += "-tiny"
   elif "base" in checkpoint_url:
      model_name += "-base"
   elif "large" in checkpoint_url:
      model_name += "-large"
   elif "huge" in checkpoint_url:
      model_name += "-huge"
   if "22k" in checkpoint_url and "1k" not in checkpoint_url:
      model_name += "-22k"
   elif "22k" in checkpoint_url and "1k" in checkpoint_url:
      model_name += "-22k-1k"
   elif "1k" in checkpoint_url:
      model_name += "-1k"
   if "224" in checkpoint_url:
      model_name += "-224"
   elif "384" in checkpoint_url:
      model_name += "-384"
   elif "512" in checkpoint_url:
      model_name += "-512"

   if push_to_hub:
      print(F'Pushing {model_name} to the hub...' )
      model.push_to_hub(_UpperCamelCase )
      preprocessor.push_to_hub(_UpperCamelCase )


if __name__ == "__main__":
       __lowercase:		Union[str, Any]    						=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--checkpoint_url",
           default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
           type=str,
           help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="model",
           type=str,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument("--save_model", action="store_true", help="Save model to local")
       parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

       __lowercase:		Tuple    						=  parser.parse_args()
       convert_convnextva_checkpoint(
           args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
       )



'''simple docstring'''

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[Any]            =       KandinskyVaaPriorEmbaEmbPipeline
 _lowerCamelCase			:       Tuple            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       [
     'num_images_per_prompt',
     'strength',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase			:       List[str]            =       False

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    return self.time_input_dim

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    return self.time_input_dim * 4

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return 100

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
    return tokenizer

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPTextConfig(
        bos_token_id=0,       eos_token_id=2,       hidden_size=self.text_embedder_hidden_size,       projection_dim=self.text_embedder_hidden_size,       intermediate_size=37,       layer_norm_eps=1e-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       )
    return CLIPTextModelWithProjection(a_       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    torch.manual_seed(0       )

    UpperCamelCase__			    =    {
        "num_attention_heads": 2,
        "attention_head_dim": 12,
        "embedding_dim": self.text_embedder_hidden_size,
        "num_layers": 1,
    }

    UpperCamelCase__			    =    PriorTransformer(**a_       )
    # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
    UpperCamelCase__			    =    nn.Parameter(torch.ones(model.clip_std.shape       )       )
    return model

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPVisionConfig(
        hidden_size=self.text_embedder_hidden_size,       image_size=224,       projection_dim=self.text_embedder_hidden_size,       intermediate_size=37,       num_attention_heads=4,       num_channels=3,       num_hidden_layers=5,       patch_size=14,       )

    UpperCamelCase__			    =    CLIPVisionModelWithProjection(a_       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPImageProcessor(
        crop_size=224,       do_center_crop=a_,       do_normalize=a_,       do_resize=a_,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

    return image_processor

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.dummy_prior
    UpperCamelCase__			    =    self.dummy_image_encoder
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    self.dummy_tokenizer
    UpperCamelCase__			    =    self.dummy_image_processor

    UpperCamelCase__			    =    UnCLIPScheduler(
        variance_type="fixed_small_log",       prediction_type="sample",       num_train_timesteps=1000,       clip_sample=a_,       clip_sample_range=10.0,       )

    UpperCamelCase__			    =    {
        "prior": prior,
        "image_encoder": image_encoder,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "scheduler": scheduler,
        "image_processor": image_processor,
    }

    return components

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						List[str],       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    if str(a_       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(a_       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )

    UpperCamelCase__			    =    floats_tensor((1, 3, 64, 64),       rng=random.Random(a_       )       ).to(a_       )
    UpperCamelCase__			    =    image.cpu().permute(0,       2,       3,       1       )[0]
    UpperCamelCase__			    =    Image.fromarray(np.uinta(a_       )       ).convert("RGB"       ).resize((256, 256)       )

    UpperCamelCase__			    =    {
        "prompt": "horse",
        "image": init_image,
        "strength": 0.5,
        "generator": generator,
        "guidance_scale": 4.0,
        "num_inference_steps": 2,
        "output_type": "np",
    }
    return inputs

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"

    UpperCamelCase__			    =    self.get_dummy_components()

    UpperCamelCase__			    =    self.pipeline_class(**a_       )
    UpperCamelCase__			    =    pipe.to(a_       )

    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    pipe(**self.get_dummy_inputs(a_       )       )
    UpperCamelCase__			    =    output.image_embeds

    UpperCamelCase__			    =    pipe(
        **self.get_dummy_inputs(a_       ),       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -10:]
    UpperCamelCase__			    =    image_from_tuple[0, -10:]

    assert image.shape == (1, 32)

    UpperCamelCase__			    =    np.array(
        [
            0.1_071_284,
            1.3_330_271,
            0.61_260_223,
            -0.6_691_065,
            -0.3_846_852,
            -1.0_303_661,
            0.22_716_111,
            0.03_348_901,
            0.30_040_675,
            -0.24_805_029,
        ]       )

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

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    True
    UpperCamelCase__			    =    False

    self._test_inference_batch_single_identical(
        test_max_difference=a_,       relax_max_difference=a_,       test_mean_pixel_difference=a_,       )

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    False

    self._test_attention_slicing_forward_pass(
        test_max_difference=a_,       test_mean_pixel_difference=a_,       )