






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__lowerCamelCase						= logging.get_logger(__name__)  # pylint: disable=invalid-name

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  ['''safety_checker''', '''feature_extractor''']




						def __init__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase = True		,	)			->						int:
													super().__init__()
													if hasattr(scheduler.config		,	'''steps_offset'''       ) and scheduler.config.steps_offset != 1:
																				_a  :    Tuple			=						(
																				    F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
																				    F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
																				    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
																				    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
																				    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
																				    ' file'
																				)
																				deprecate('''steps_offset!=1'''		,	'''1.0.0'''		,	__SCREAMING_SNAKE_CASE		,	standard_warn=__SCREAMING_SNAKE_CASE       )
																				_a  :    List[Any]			=						dict(scheduler.config       )
																				_a  :    Dict			=						1
																				_a  :    Dict			=						FrozenDict(__SCREAMING_SNAKE_CASE       )

													self.register_modules(
													    vae=__SCREAMING_SNAKE_CASE		,	text_encoder=__SCREAMING_SNAKE_CASE		,	tokenizer=__SCREAMING_SNAKE_CASE		,	unet=__SCREAMING_SNAKE_CASE		,	scheduler=__SCREAMING_SNAKE_CASE		,	safety_checker=__SCREAMING_SNAKE_CASE		,	feature_extractor=__SCREAMING_SNAKE_CASE		,	)
													self.register_to_config(requires_safety_checker=__SCREAMING_SNAKE_CASE       )




						@property
						def      snake_case__(    self       )			->						List[Any]:
													return {k: getattr(self		,	__SCREAMING_SNAKE_CASE       ) for k in self.config.keys() if not k.startswith('''_'''       )}




						def      snake_case__(    self		,	lowercase = "auto"       )			->						List[str]:
													if slice_size == "auto":
																				# half the attention head size is usually a good trade-off between
																				# speed and memory
																				_a  :    List[str]			=						self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE       )




						def      snake_case__(    self       )			->						Any:
													self.enable_attention_slicing(__SCREAMING_SNAKE_CASE       )




						@torch.no_grad()
						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase = 0.8		,	lowercase = 50		,	lowercase = 7.5		,	lowercase = None		,	lowercase = 1		,	lowercase = 0.0		,	lowercase = None		,	lowercase = "pil"		,	lowercase = True		,	lowercase = None		,	lowercase = 1		,	)			->						Union[str, Any]:
													# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
													return StableDiffusionInpaintPipelineLegacy(**self.components       )(
													    prompt=__SCREAMING_SNAKE_CASE		,	image=__SCREAMING_SNAKE_CASE		,	mask_image=__SCREAMING_SNAKE_CASE		,	strength=__SCREAMING_SNAKE_CASE		,	num_inference_steps=__SCREAMING_SNAKE_CASE		,	guidance_scale=__SCREAMING_SNAKE_CASE		,	negative_prompt=__SCREAMING_SNAKE_CASE		,	num_images_per_prompt=__SCREAMING_SNAKE_CASE		,	eta=__SCREAMING_SNAKE_CASE		,	generator=__SCREAMING_SNAKE_CASE		,	output_type=__SCREAMING_SNAKE_CASE		,	return_dict=__SCREAMING_SNAKE_CASE		,	callback=__SCREAMING_SNAKE_CASE		,	)




						@torch.no_grad()
						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase = 0.8		,	lowercase = 50		,	lowercase = 7.5		,	lowercase = None		,	lowercase = 1		,	lowercase = 0.0		,	lowercase = None		,	lowercase = "pil"		,	lowercase = True		,	lowercase = None		,	lowercase = 1		,	**lowercase		,	)			->						Dict:
													# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
													return StableDiffusionImgaImgPipeline(**self.components       )(
													    prompt=__SCREAMING_SNAKE_CASE		,	image=__SCREAMING_SNAKE_CASE		,	strength=__SCREAMING_SNAKE_CASE		,	num_inference_steps=__SCREAMING_SNAKE_CASE		,	guidance_scale=__SCREAMING_SNAKE_CASE		,	negative_prompt=__SCREAMING_SNAKE_CASE		,	num_images_per_prompt=__SCREAMING_SNAKE_CASE		,	eta=__SCREAMING_SNAKE_CASE		,	generator=__SCREAMING_SNAKE_CASE		,	output_type=__SCREAMING_SNAKE_CASE		,	return_dict=__SCREAMING_SNAKE_CASE		,	callback=__SCREAMING_SNAKE_CASE		,	callback_steps=__SCREAMING_SNAKE_CASE		,	)







						@torch.no_grad()
						def      snake_case__(    self		,	lowercase		,	lowercase = 512		,	lowercase = 512		,	lowercase = 50		,	lowercase = 7.5		,	lowercase = None		,	lowercase = 1		,	lowercase = 0.0		,	lowercase = None		,	lowercase = None		,	lowercase = "pil"		,	lowercase = True		,	lowercase = None		,	lowercase = 1		,	)			->						List[str]:
													# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
													return StableDiffusionPipeline(**self.components       )(
													    prompt=__SCREAMING_SNAKE_CASE		,	height=__SCREAMING_SNAKE_CASE		,	width=__SCREAMING_SNAKE_CASE		,	num_inference_steps=__SCREAMING_SNAKE_CASE		,	guidance_scale=__SCREAMING_SNAKE_CASE		,	negative_prompt=__SCREAMING_SNAKE_CASE		,	num_images_per_prompt=__SCREAMING_SNAKE_CASE		,	eta=__SCREAMING_SNAKE_CASE		,	generator=__SCREAMING_SNAKE_CASE		,	latents=__SCREAMING_SNAKE_CASE		,	output_type=__SCREAMING_SNAKE_CASE		,	return_dict=__SCREAMING_SNAKE_CASE		,	callback=__SCREAMING_SNAKE_CASE		,	callback_steps=__SCREAMING_SNAKE_CASE		,	)

'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str = "mytext.txt"   ,     snake_case_				:	bool = False							)       ->	None:
 import socket

 SCREAMING_SNAKE_CASE  :  Optional[int]								=		1_2312  # Reserve a port for your service.
 SCREAMING_SNAKE_CASE  :  Any								=		socket.socket()  # Create a socket object
 SCREAMING_SNAKE_CASE  :  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:
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[Any]								=		sock.accept()  # Establish connection with client.
  print(f"""Got connection from {addr}"""							)
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		conn.recv(1024							)
  print(f"""Server received: {data = }"""							)

  with open(snake_case_   ,     'rb'							) as in_file:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		in_file.read(1024							)
   while data:
    conn.send(snake_case_							)
    print(f"""Sent {data!r}"""							)
    SCREAMING_SNAKE_CASE  :  List[Any]								=		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()


