
# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class 			UpperCamelCase_			(						UpperCamelCase__	,     UpperCamelCase__	,     UpperCamelCase__	,     unittest.TestCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							StableDiffusionControlNetInpaintPipeline
							UpperCamelCase_					   =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							UpperCamelCase_					   =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCamelCase_					   =							frozenset({"""control_image"""}   )  # skip `image` and `mask` for now, only test for control_image
							UpperCamelCase_					   =							TEXT_TO_IMAGE_IMAGE_PARAMS
							def        lowerCAmelCase__ (				self) ->	List[str]:
										torch.manual_seed(0)
										UpperCamelCase__    :		Tuple     =				UNetaDConditionModel(
										    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						sample_size=32     ,						in_channels=9     ,						out_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,						cross_attention_dim=32     ,						)
										torch.manual_seed(0)
										UpperCamelCase__    :		Any     =				ControlNetModel(
										    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						in_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						cross_attention_dim=32     ,						conditioning_embedding_out_channels=(16, 32)     ,						)
										torch.manual_seed(0)
										UpperCamelCase__    :		Dict     =				DDIMScheduler(
										    beta_start=0.0_0085     ,						beta_end=0.012     ,						beta_schedule='scaled_linear'     ,						clip_sample=lowerCAmelCase__     ,						set_alpha_to_one=lowerCAmelCase__     ,						)
										torch.manual_seed(0)
										UpperCamelCase__    :		List[Any]     =				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)
										UpperCamelCase__    :		Union[str, Any]     =				CLIPTextConfig(
										    bos_token_id=0     ,						eos_token_id=2     ,						hidden_size=32     ,						intermediate_size=37     ,						layer_norm_eps=1E-05     ,						num_attention_heads=4     ,						num_hidden_layers=5     ,						pad_token_id=1     ,						vocab_size=10_00     ,						)
										UpperCamelCase__    :		str     =				CLIPTextModel(lowerCAmelCase__)
										UpperCamelCase__    :		List[Any]     =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

										UpperCamelCase__    :		List[str]     =				{
										    "unet": unet,
										    "controlnet": controlnet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase=0) ->	str:




										if str(lowerCAmelCase__).startswith('mps'):
													UpperCamelCase__    :		int     =				torch.manual_seed(lowerCAmelCase__)
										else:
													UpperCamelCase__    :		List[str]     =				torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)

										UpperCamelCase__    :		Optional[int]     =				2
										UpperCamelCase__    :		str     =				randn_tensor(
										    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     ,						generator=lowerCAmelCase__     ,						device=torch.device(lowerCAmelCase__)     ,						)
										UpperCamelCase__    :		List[Any]     =				floats_tensor((1, 3, 32, 32)     ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
										UpperCamelCase__    :		List[Any]     =				init_image.cpu().permute(0     ,						2     ,						3     ,						1)[0]

										UpperCamelCase__    :		Tuple     =				Image.fromarray(np.uinta(lowerCAmelCase__)).convert('RGB').resize((64, 64))
										UpperCamelCase__    :		Union[str, Any]     =				Image.fromarray(np.uinta(init_image + 4)).convert('RGB').resize((64, 64))

										UpperCamelCase__    :		Optional[Any]     =				{
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										    "image": image,
										    "mask_image": mask_image,
										    "control_image": control_image,
										}

										return inputs
							def        lowerCAmelCase__ (				self) ->	List[str]:
										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3)
							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()     ,						reason='XFormers attention is only available with CUDA and `xformers` installed'     ,						)
							def        lowerCAmelCase__ (				self) ->	str:
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)






							def        lowerCAmelCase__ (				self) ->	int:
										self._test_inference_batch_single_identical(expected_max_diff=2E-3)



class 			UpperCamelCase_			(						UpperCamelCase__   ):




							'''simple docstring'''

							UpperCamelCase_					   =							StableDiffusionControlNetInpaintPipeline
							UpperCamelCase_					   =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							UpperCamelCase_					   =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCamelCase_					   =							frozenset([]   )
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										torch.manual_seed(0)
										UpperCamelCase__    :		Tuple     =				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     ,						)
										torch.manual_seed(0)
										UpperCamelCase__    :		str     =				ControlNetModel(
										    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						in_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						cross_attention_dim=32     ,						conditioning_embedding_out_channels=(16, 32)     ,						)
										torch.manual_seed(0)
										UpperCamelCase__    :		Optional[int]     =				DDIMScheduler(
										    beta_start=0.0_0085     ,						beta_end=0.012     ,						beta_schedule='scaled_linear'     ,						clip_sample=lowerCAmelCase__     ,						set_alpha_to_one=lowerCAmelCase__     ,						)
										torch.manual_seed(0)
										UpperCamelCase__    :		Dict     =				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)
										UpperCamelCase__    :		Union[str, Any]     =				CLIPTextConfig(
										    bos_token_id=0     ,						eos_token_id=2     ,						hidden_size=32     ,						intermediate_size=37     ,						layer_norm_eps=1E-05     ,						num_attention_heads=4     ,						num_hidden_layers=5     ,						pad_token_id=1     ,						vocab_size=10_00     ,						)
										UpperCamelCase__    :		Optional[int]     =				CLIPTextModel(lowerCAmelCase__)
										UpperCamelCase__    :		Optional[int]     =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

										UpperCamelCase__    :		str     =				{
										    "unet": unet,
										    "controlnet": controlnet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components



class 			UpperCamelCase_			(						UpperCamelCase__	,     UpperCamelCase__	,     unittest.TestCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							StableDiffusionControlNetInpaintPipeline
							UpperCamelCase_					   =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							UpperCamelCase_					   =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										torch.manual_seed(0)
										UpperCamelCase__    :		str     =				UNetaDConditionModel(
										    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						sample_size=32     ,						in_channels=9     ,						out_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,						cross_attention_dim=32     ,						)
										torch.manual_seed(0)

										def init_weights(UpperCamelCase):
													if isinstance(lowerCAmelCase__     ,						torch.nn.Convad):
																torch.nn.init.normal(m.weight)
																m.bias.data.fill_(1.0)

										UpperCamelCase__    :		List[str]     =				ControlNetModel(
										    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						in_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						cross_attention_dim=32     ,						conditioning_embedding_out_channels=(16, 32)     ,						)
										controlneta.controlnet_down_blocks.apply(lowerCAmelCase__)

										torch.manual_seed(0)
										UpperCamelCase__    :		Union[str, Any]     =				ControlNetModel(
										    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						in_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						cross_attention_dim=32     ,						conditioning_embedding_out_channels=(16, 32)     ,						)
										controlneta.controlnet_down_blocks.apply(lowerCAmelCase__)

										torch.manual_seed(0)
										UpperCamelCase__    :		Union[str, Any]     =				DDIMScheduler(
										    beta_start=0.0_0085     ,						beta_end=0.012     ,						beta_schedule='scaled_linear'     ,						clip_sample=lowerCAmelCase__     ,						set_alpha_to_one=lowerCAmelCase__     ,						)
										torch.manual_seed(0)
										UpperCamelCase__    :		Optional[Any]     =				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)
										UpperCamelCase__    :		Optional[Any]     =				CLIPTextConfig(
										    bos_token_id=0     ,						eos_token_id=2     ,						hidden_size=32     ,						intermediate_size=37     ,						layer_norm_eps=1E-05     ,						num_attention_heads=4     ,						num_hidden_layers=5     ,						pad_token_id=1     ,						vocab_size=10_00     ,						)
										UpperCamelCase__    :		List[str]     =				CLIPTextModel(lowerCAmelCase__)
										UpperCamelCase__    :		List[Any]     =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

										UpperCamelCase__    :		Union[str, Any]     =				MultiControlNetModel([controlneta, controlneta])

										UpperCamelCase__    :		int     =				{
										    "unet": unet,
										    "controlnet": controlnet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase=0) ->	str:




										if str(lowerCAmelCase__).startswith('mps'):
													UpperCamelCase__    :		List[str]     =				torch.manual_seed(lowerCAmelCase__)
										else:
													UpperCamelCase__    :		Optional[Any]     =				torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)

										UpperCamelCase__    :		List[str]     =				2

										UpperCamelCase__    :		Optional[int]     =				[
										    randn_tensor(
										        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     ,						generator=lowerCAmelCase__     ,						device=torch.device(lowerCAmelCase__)     ,						),
										    randn_tensor(
										        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     ,						generator=lowerCAmelCase__     ,						device=torch.device(lowerCAmelCase__)     ,						),
										]
										UpperCamelCase__    :		List[str]     =				floats_tensor((1, 3, 32, 32)     ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
										UpperCamelCase__    :		Dict     =				init_image.cpu().permute(0     ,						2     ,						3     ,						1)[0]

										UpperCamelCase__    :		int     =				Image.fromarray(np.uinta(lowerCAmelCase__)).convert('RGB').resize((64, 64))
										UpperCamelCase__    :		str     =				Image.fromarray(np.uinta(init_image + 4)).convert('RGB').resize((64, 64))

										UpperCamelCase__    :		Optional[Any]     =				{
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										    "image": image,
										    "mask_image": mask_image,
										    "control_image": control_image,
										}

										return inputs
							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		Tuple     =				self.get_dummy_components()
										UpperCamelCase__    :		Union[str, Any]     =				self.pipeline_class(**lowerCAmelCase__)
										pipe.to(lowerCAmelCase__)

										UpperCamelCase__    :		int     =				10.0
										UpperCamelCase__    :		int     =				4

										UpperCamelCase__    :		Dict     =				self.get_dummy_inputs(lowerCAmelCase__)
										UpperCamelCase__    :		int     =				steps
										UpperCamelCase__    :		List[Any]     =				scale
										UpperCamelCase__    :		Tuple     =				pipe(**lowerCAmelCase__)[0]

										UpperCamelCase__    :		Dict     =				self.get_dummy_inputs(lowerCAmelCase__)
										UpperCamelCase__    :		Dict     =				steps
										UpperCamelCase__    :		int     =				scale
										UpperCamelCase__    :		int     =				pipe(**lowerCAmelCase__     ,						control_guidance_start=0.1     ,						control_guidance_end=0.2)[0]

										UpperCamelCase__    :		Tuple     =				self.get_dummy_inputs(lowerCAmelCase__)
										UpperCamelCase__    :		Optional[Any]     =				steps
										UpperCamelCase__    :		List[Any]     =				scale
										UpperCamelCase__    :		int     =				pipe(**lowerCAmelCase__     ,						control_guidance_start=[0.1, 0.3]     ,						control_guidance_end=[0.2, 0.7])[0]

										UpperCamelCase__    :		Any     =				self.get_dummy_inputs(lowerCAmelCase__)
										UpperCamelCase__    :		int     =				steps
										UpperCamelCase__    :		Dict     =				scale
										UpperCamelCase__    :		Union[str, Any]     =				pipe(**lowerCAmelCase__     ,						control_guidance_start=0.4     ,						control_guidance_end=[0.5, 0.8])[0]

										# make sure that all outputs are different
										assert np.sum(np.abs(output_a - output_a)) > 1E-3
										assert np.sum(np.abs(output_a - output_a)) > 1E-3
										assert np.sum(np.abs(output_a - output_a)) > 1E-3
							def        lowerCAmelCase__ (				self) ->	Any:
										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3)
							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()     ,						reason='XFormers attention is only available with CUDA and `xformers` installed'     ,						)
							def        lowerCAmelCase__ (				self) ->	Dict:
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)
							def        lowerCAmelCase__ (				self) ->	int:
										self._test_inference_batch_single_identical(expected_max_diff=2E-3)






							def        lowerCAmelCase__ (				self) ->	str:
										UpperCamelCase__    :		Union[str, Any]     =				self.get_dummy_components()
										UpperCamelCase__    :		Optional[Any]     =				self.pipeline_class(**lowerCAmelCase__)
										pipe.to(lowerCAmelCase__)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__)
										with tempfile.TemporaryDirectory() as tmpdir:
													try:
																# save_pretrained is not implemented for Multi-ControlNet
																pipe.save_pretrained(lowerCAmelCase__)
													except NotImplementedError:
																pass



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




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	Dict:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()
							def        lowerCAmelCase__ (				self) ->	Any:
										UpperCamelCase__    :		Optional[int]     =				ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny')

										UpperCamelCase__    :		Dict     =				StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    'runwayml/stable-diffusion-inpainting'     ,						safety_checker=lowerCAmelCase__     ,						controlnet=lowerCAmelCase__)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=lowerCAmelCase__)

										UpperCamelCase__    :		int     =				torch.Generator(device='cpu').manual_seed(0)
										UpperCamelCase__    :		List[Any]     =				load_image(
										    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png').resize((5_12, 5_12))

										UpperCamelCase__    :		Union[str, Any]     =				load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_inpaint/input_bench_mask.png').resize((5_12, 5_12))

										UpperCamelCase__    :		Optional[Any]     =				"pitch black hole"

										UpperCamelCase__    :		Tuple     =				load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png').resize((5_12, 5_12))

										UpperCamelCase__    :		List[str]     =				pipe(
										    lowerCAmelCase__     ,						image=lowerCAmelCase__     ,						mask_image=lowerCAmelCase__     ,						control_image=lowerCAmelCase__     ,						generator=lowerCAmelCase__     ,						output_type='np'     ,						num_inference_steps=3     ,						)

										UpperCamelCase__    :		Tuple     =				output.images[0]

										assert image.shape == (5_12, 5_12, 3)

										UpperCamelCase__    :		List[Any]     =				load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy')

										assert np.abs(expected_image - image).max() < 9E-2






							def        lowerCAmelCase__ (				self) ->	Any:
										UpperCamelCase__    :		Any     =				ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint')

										UpperCamelCase__    :		Tuple     =				StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    'runwayml/stable-diffusion-v1-5'     ,						safety_checker=lowerCAmelCase__     ,						controlnet=lowerCAmelCase__)
										UpperCamelCase__    :		Optional[int]     =				DDIMScheduler.from_config(pipe.scheduler.config)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=lowerCAmelCase__)

										UpperCamelCase__    :		int     =				torch.Generator(device='cpu').manual_seed(33)

										UpperCamelCase__    :		Any     =				load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png')
										UpperCamelCase__    :		Union[str, Any]     =				init_image.resize((5_12, 5_12))

										UpperCamelCase__    :		List[str]     =				load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png')
										UpperCamelCase__    :		Optional[Any]     =				mask_image.resize((5_12, 5_12))

										UpperCamelCase__    :		str     =				"a handsome man with ray-ban sunglasses"

										def make_inpaint_condition(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		List[Any]     =				np.array(image.convert('RGB')).astype(np.floataa) / 2_55.0
													UpperCamelCase__    :		str     =				np.array(image_mask.convert('L')).astype(np.floataa) / 2_55.0

													assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
													UpperCamelCase__    :		Any     =				-1.0  # set as masked pixel
													UpperCamelCase__    :		int     =				np.expand_dims(lowerCAmelCase__     ,						0).transpose(0     ,						3     ,						1     ,						2)
													UpperCamelCase__    :		Tuple     =				torch.from_numpy(lowerCAmelCase__)
													return image

										UpperCamelCase__    :		List[str]     =				make_inpaint_condition(lowerCAmelCase__     ,						lowerCAmelCase__)

										UpperCamelCase__    :		int     =				pipe(
										    lowerCAmelCase__     ,						image=lowerCAmelCase__     ,						mask_image=lowerCAmelCase__     ,						control_image=lowerCAmelCase__     ,						guidance_scale=9.0     ,						eta=1.0     ,						generator=lowerCAmelCase__     ,						num_inference_steps=20     ,						output_type='np'     ,						)
										UpperCamelCase__    :		Dict     =				output.images[0]

										assert image.shape == (5_12, 5_12, 3)

										UpperCamelCase__    :		Tuple     =				load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy')

										assert np.abs(expected_image - image).max() < 9E-2





def       _lowercase					(      __SCREAMING_SNAKE_CASE = "mytext.txt"				,    __SCREAMING_SNAKE_CASE = False )	->     None:
			import socket

			UpperCamelCase__    :		Tuple     =				1_2312  # Reserve a port for your service.
			UpperCamelCase__    :		Optional[int]     =				socket.socket()  # Create a socket object
			UpperCamelCase__    :		Optional[int]     =				socket.gethostname()  # Get local machine name
			sock.bind((host, port) )  # Bind to the port
			sock.listen(5 )  # Now wait for client connection.

			print('Server listening....' )

			while True:
						UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				sock.accept()  # Establish connection with client.
						print(F"""Got connection from {addr}""" )
						UpperCamelCase__    :		Union[str, Any]     =				conn.recv(1024 )
						print(F"""Server received: {data = }""" )

						with open(__SCREAMING_SNAKE_CASE				,    'rb' ) as in_file:
									UpperCamelCase__    :		Tuple     =				in_file.read(1024 )
									while data:
												conn.send(__SCREAMING_SNAKE_CASE )
												print(F"""Sent {data!r}""" )
												UpperCamelCase__    :		str     =				in_file.read(1024 )

						print('Done sending' )
						conn.close()
						if testing:  # Allow the test to complete
									break

			sock.shutdown(1 )
			sock.close()


if __name__ == "__main__":
							send_file()



