





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class 					lowercase__ (			__lowerCamelCase       ):

			'''simple docstring'''


			a		:							Optional[int]     	=	"levit"




			def __init__(					self,	__magic_name__=224,	__magic_name__=3,	__magic_name__=3,	__magic_name__=2,	__magic_name__=1,	__magic_name__=16,	__magic_name__=[128, 256, 384],	__magic_name__=[4, 8, 12],	__magic_name__=[4, 4, 4],	__magic_name__=[16, 16, 16],	__magic_name__=0,	__magic_name__=[2, 2, 2],	__magic_name__=[2, 2, 2],	__magic_name__=0.02,	**__magic_name__,	)				->   Dict:






							"""simple docstring"""
							super().__init__(**__magic_name__							)
							UpperCamelCase__							:       str      =   image_size
							UpperCamelCase__							:       List[str]      =   num_channels
							UpperCamelCase__							:       Union[str, Any]      =   kernel_size
							UpperCamelCase__							:       str      =   stride
							UpperCamelCase__							:       str      =   padding
							UpperCamelCase__							:       List[str]      =   hidden_sizes
							UpperCamelCase__							:       Union[str, Any]      =   num_attention_heads
							UpperCamelCase__							:       str      =   depths
							UpperCamelCase__							:       List[str]      =   key_dim
							UpperCamelCase__							:       Optional[Any]      =   drop_path_rate
							UpperCamelCase__							:       List[str]      =   patch_size
							UpperCamelCase__							:       List[str]      =   attention_ratio
							UpperCamelCase__							:       Dict      =   mlp_ratio
							UpperCamelCase__							:       Optional[int]      =   initializer_range
							UpperCamelCase__							:       Dict      =   [
							    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]






class 					lowercase__ (			__lowerCamelCase       ):

			'''simple docstring'''


			a		:							List[Any]     	=	version.parse("1.11"       )




			@property
			def    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






							"""simple docstring"""
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]							)




			@property
			def    UpperCamelCase__						(					self							)				->   float:






							"""simple docstring"""
							return 1E-4








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


UpperCAmelCase_  							=				False






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   pass






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

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       int      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:       Union[str, Any]      =   VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   generator.manual_seed(0							)
       UpperCamelCase__							:       str      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	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    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion''',	torch_dtype=torch.floataa							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[int]      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       Optional[int]      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=50,	output_type='''numpy'''							).images

       UpperCamelCase__							:       int      =   image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       int      =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

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

