





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_UpperCAmelCase       : Tuple			     =       False

class      lowerCAmelCase							(      unittest.TestCase    ):
 pass


@nightly
@require_torch_gpu
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Any			)    ->			int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def   A_			(   self						:   Tuple			)    ->			Optional[int]:
     lowerCamelCase__					:   int      				=	VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa			)
     pipe.to(UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'			)

     lowerCamelCase__					:   Union[str, Any]      				=	torch.manual_seed(0			)
     lowerCamelCase__					:   Union[str, Any]      				=	pipe.dual_guided(
         prompt='first prompt'    ,  image=UpperCAmelCase    ,  text_to_image_strength=0.7_5    ,  generator=UpperCAmelCase    ,  guidance_scale=7.5    ,  num_inference_steps=2    ,  output_type='numpy'    ,  ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(UpperCAmelCase			)
         lowerCamelCase__					:   str      				=	VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase    ,  torch_dtype=torch.floataa			)
     pipe.to(UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	generator.manual_seed(0			)
     lowerCamelCase__					:   Optional[int]      				=	pipe.dual_guided(
         prompt='first prompt'    ,  image=UpperCAmelCase    ,  text_to_image_strength=0.7_5    ,  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   A_			(   self						:   Dict			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa			)
     pipe.to(UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   List[str]      				=	'cyberpunk 2077'
     lowerCamelCase__					:   int      				=	load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'			)
     lowerCamelCase__					:   int      				=	torch.manual_seed(0			)
     lowerCamelCase__					:   Optional[int]      				=	pipe.dual_guided(
         prompt=UpperCAmelCase    ,  image=UpperCAmelCase    ,  text_to_image_strength=0.7_5    ,  generator=UpperCAmelCase    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy'    ,  ).images

     lowerCamelCase__					:   Dict      				=	image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__					:   Optional[int]      				=	np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]			)

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

     lowerCamelCase__					:   Any      				=	'A painting of a squirrel eating a burger '
     lowerCamelCase__					:   str      				=	torch.manual_seed(0			)
     lowerCamelCase__					:   int      				=	pipe.text_to_image(
         prompt=UpperCAmelCase    ,  generator=UpperCAmelCase    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy'			).images

     lowerCamelCase__					:   List[Any]      				=	image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__					:   Union[str, Any]      				=	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]			)

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

     lowerCamelCase__					:   int      				=	pipe.image_variation(UpperCAmelCase    ,  generator=UpperCAmelCase    ,  output_type='numpy'			).images

     lowerCamelCase__					:   List[str]      				=	image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__					:   Optional[Any]      				=	np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]			)

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












import socket


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   int      				=	socket.socket(socket.AF_INET		,			socket.SOCK_STREAM				)
    lowerCamelCase__					:   Optional[int]      				=	socket.gethostname()
    lowerCamelCase__					:   Optional[int]      				=	1_2312

    sock.connect((host, port)				)
    sock.send(b'Hello server!'				)

    with open('Received_file'		,			'wb'				) as out_file:
        print('File opened'				)
        print('Receiving data...'				)
        while True:
            lowerCamelCase__					:   str      				=	sock.recv(1024				)
            if not data:
                break
            out_file.write(_UpperCAmelCase				)

    print('Successfully received the file'				)
    sock.close()
    print('Connection closed'				)


if __name__ == "__main__":
    main()





