





'''simple docstring'''







import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


lowerCAmelCase_   :       Optional[Any]       =     logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							__SCREAMING_SNAKE_CASE    (SCREAMING_SNAKE_CASE__ ):






				"""simple docstring"""







				def __init__(    self	:   Union[str, Any]   ,							__a	:   WhisperForConditionalGeneration   ,							__a	:   WhisperProcessor   ,							__a	:   AutoencoderKL   ,							__a	:   CLIPTextModel   ,							__a	:   CLIPTokenizer   ,							__a	:   UNetaDConditionModel   ,							__a	:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]   ,							__a	:   StableDiffusionSafetyChecker   ,							__a	:   CLIPImageProcessor   ,							):
								super().__init__()

								if safety_checker is None:
												logger.warning(
												    f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
												    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
												    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
												    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
												    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
												    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."   )

								self.register_modules(
								    speech_model=_lowercase   ,							speech_processor=_lowercase   ,							vae=_lowercase   ,							text_encoder=_lowercase   ,							tokenizer=_lowercase   ,							unet=_lowercase   ,							scheduler=_lowercase   ,							feature_extractor=_lowercase   ,							)




				def     UpperCamelCase__  (    self	:   Optional[int]   ,							__a	:   Optional[Union[str, int]] = "auto"   ):
								if slice_size == "auto":
												_a				      =					self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_lowercase   )




				def     UpperCamelCase__  (    self	:   Optional[int]   ):
								self.enable_attention_slicing(_lowercase   )




				@torch.no_grad()
				def __call__(    self	:   Union[str, Any]   ,							__a	:   List[str]   ,							__a	:   Union[str, Any]=1_60_00   ,							__a	:   int = 5_12   ,							__a	:   int = 5_12   ,							__a	:   int = 50   ,							__a	:   float = 7.5   ,							__a	:   Optional[Union[str, List[str]]] = None   ,							__a	:   Optional[int] = 1   ,							__a	:   float = 0.0   ,							__a	:   Optional[torch.Generator] = None   ,							__a	:   Optional[torch.FloatTensor] = None   ,							__a	:   Optional[str] = "pil"   ,							__a	:   bool = True   ,							__a	:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,							__a	:   int = 1   ,							**__a	:   Dict   ,							):
								_a				      =					self.speech_processor.feature_extractor(
								    _lowercase   ,							return_tensors="pt"   ,							sampling_rate=_lowercase   ).input_features.to(self.device   )
								_a				      =					self.speech_model.generate(_lowercase   ,							max_length=48_00_00   )

								_a				      =					self.speech_processor.tokenizer.batch_decode(_lowercase   ,							skip_special_tokens=_lowercase   ,							normalize=_lowercase   )[
								    0
								]

								if isinstance(_lowercase   ,							_lowercase   ):
												_a				      =					1
								elif isinstance(_lowercase   ,							_lowercase   ):
												_a				      =					len(_lowercase   )
								else:
												raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_lowercase   )}'   )

								if height % 8 != 0 or width % 8 != 0:
												raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'   )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_lowercase   ,							_lowercase   ) or callback_steps <= 0)
								):
												raise ValueError(
												    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
												    f' {type(_lowercase   )}.'   )

								# get prompt text embeddings
								_a				      =					self.tokenizer(
								    _lowercase   ,							padding="max_length"   ,							max_length=self.tokenizer.model_max_length   ,							return_tensors="pt"   ,							)
								_a				      =					text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												_a				      =					self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
												logger.warning(
												    "The following part of your input was truncated because CLIP can only handle sequences up to"
												    f' {self.tokenizer.model_max_length} tokens: {removed_text}'   )
												_a				      =					text_input_ids[:, : self.tokenizer.model_max_length]
								_a				      =					self.text_encoder(text_input_ids.to(self.device   )   )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								_a				      =					text_embeddings.shape
								_a				      =					text_embeddings.repeat(1   ,							_lowercase   ,							1   )
								_a				      =					text_embeddings.view(bs_embed * num_images_per_prompt   ,							_lowercase   ,							-1   )

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								_a				      =					guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												_a				      =					42
												if negative_prompt is None:
																_a				      =					[""] * batch_size
												elif type(_lowercase   ) is not type(_lowercase   ):
																raise TypeError(
																    f'`negative_prompt` should be the same type to `prompt`, but got {type(_lowercase   )} !='
																    f' {type(_lowercase   )}.'   )
												elif isinstance(_lowercase   ,							_lowercase   ):
																_a				      =					[negative_prompt]
												elif batch_size != len(_lowercase   ):
																raise ValueError(
																    f'`negative_prompt`: {negative_prompt} has batch size {len(_lowercase   )}, but `prompt`:'
																    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																    " the batch size of `prompt`."   )
												else:
																_a				      =					negative_prompt

												_a				      =					text_input_ids.shape[-1]
												_a				      =					self.tokenizer(
												    _lowercase   ,							padding="max_length"   ,							max_length=_lowercase   ,							truncation=_lowercase   ,							return_tensors="pt"   ,							)
												_a				      =					self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												_a				      =					uncond_embeddings.shape[1]
												_a				      =					uncond_embeddings.repeat(1   ,							_lowercase   ,							1   )
												_a				      =					uncond_embeddings.view(batch_size * num_images_per_prompt   ,							_lowercase   ,							-1   )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												_a				      =					torch.cat([uncond_embeddings, text_embeddings]   )

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								_a				      =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								_a				      =					text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not exist on mps
																_a				      =					torch.randn(_lowercase   ,							generator=_lowercase   ,							device="cpu"   ,							dtype=_lowercase   ).to(
																    self.device   )
												else:
																_a				      =					torch.randn(_lowercase   ,							generator=_lowercase   ,							device=self.device   ,							dtype=_lowercase   )
								else:
												if latents.shape != latents_shape:
																raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
												_a				      =					latents.to(self.device   )

								# set timesteps
								self.scheduler.set_timesteps(_lowercase   )

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								_a				      =					self.scheduler.timesteps.to(self.device   )

								# scale the initial noise by the standard deviation required by the scheduler
								_a				      =					latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								_a				      =					"eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
								_a				      =					{}
								if accepts_eta:
												_a				      =					eta

								for i, t in enumerate(self.progress_bar(_lowercase   )   ):
												# expand the latents if we are doing classifier free guidance
												_a				      =					torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
												_a				      =					self.scheduler.scale_model_input(_lowercase   ,							_lowercase   )

												# predict the noise residual
												_a				      =					self.unet(_lowercase   ,							_lowercase   ,							encoder_hidden_states=_lowercase   ).sample

												# perform guidance
												if do_classifier_free_guidance:
																_a				      =					noise_pred.chunk(2   )
																_a				      =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# compute the previous noisy sample x_t -> x_t-1
												_a				      =					self.scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase   ).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																callback(_lowercase   ,							_lowercase   ,							_lowercase   )

								_a				      =					1 / 0.18215 * latents
								_a				      =					self.vae.decode(_lowercase   ).sample

								_a				      =					(image / 2 + 0.5).clamp(0   ,							1   )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								_a				      =					image.cpu().permute(0   ,							2   ,							3   ,							1   ).float().numpy()

								if output_type == "pil":
												_a				      =					self.numpy_to_pil(_lowercase   )

								if not return_dict:
												return image

								return StableDiffusionPipelineOutput(images=_lowercase   ,							nsfw_content_detected=_lowercase   )





"""simple docstring"""







import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('3.8'):
			import importlib_metadata
else:
			import importlib.metadata as importlib_metadata

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							List[Any]=False ):
						"""simple docstring"""






						try:
												snake_case   :			Optional[int]						    = os.environ[key]
						except KeyError:
												# KEY isn't set, default to `default`.
												snake_case   :			List[Any]						    = default
						else:
												# KEY is set, convert it to True or False.
												try:
																		snake_case   :			Dict						    = strtobool(lowerCamelCase_ )
												except ValueError:
																		# More values are supported, but let's keep the message simple.
																		raise ValueError(f'''If set, {key} must be yes or no.''' )
						return _value


A		=						parse_flag_from_env('RUN_SLOW', default=False)
A		=						parse_flag_from_env('RUN_REMOTE', default=False)
A		=						parse_flag_from_env('RUN_LOCAL', default=True)
A		=						parse_flag_from_env('RUN_PACKAGED', default=True)

# Compression
A		=						pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')
A		=						pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')
A		=						pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')

# Audio
A		=						pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),
    reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ',
)

# Beam
A		=						pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),
    reason='test requires apache-beam and a compatible dill version',
)

# Dill-cloudpickle compatibility
A		=						pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('0.3.2'),
    reason='test requires dill>0.3.2 for cloudpickle compatibility',
)

# Windows
A		=						pytest.mark.skipif(
    sys.platform == 'win32',
    reason='test should not be run on Windows',
)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						try:
												import faiss  # noqa
						except ImportError:
												snake_case   :			Dict						    = unittest.skip("test requires faiss" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						try:
												import regex  # noqa
						except ImportError:
												snake_case   :			List[Any]						    = unittest.skip("test requires regex" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						try:
												import elasticsearch  # noqa
						except ImportError:
												snake_case   :			Tuple						    = unittest.skip("test requires elasticsearch" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						try:
												import sqlalchemy  # noqa
						except ImportError:
												snake_case   :			Dict						    = unittest.skip("test requires sqlalchemy" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						if not config.TORCH_AVAILABLE:
												snake_case   :			Dict						    = unittest.skip("test requires PyTorch" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						if not config.TF_AVAILABLE:
												snake_case   :			Tuple						    = unittest.skip("test requires TensorFlow" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						if not config.JAX_AVAILABLE:
												snake_case   :			str						    = unittest.skip("test requires JAX" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						if not config.PIL_AVAILABLE:
												snake_case   :			Union[str, Any]						    = unittest.skip("test requires Pillow" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						try:
												import transformers  # noqa F401
						except ImportError:
												return unittest.skip("test requires transformers" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
						"""simple docstring"""






						try:
												import tiktoken  # noqa F401
						except ImportError:
												return unittest.skip("test requires tiktoken" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						try:
												import spacy  # noqa F401
						except ImportError:
												return unittest.skip("test requires spacy" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						def _require_spacy_model(lowerCamelCase_:							Dict ):
												try:
																		import spacy  # noqa F401

																		spacy.load(lowerCamelCase_ )
												except ImportError:
																		return unittest.skip("test requires spacy" )(lowerCamelCase_ )
												except OSError:
																		return unittest.skip("test requires spacy model '{}'".format(lowerCamelCase_ ) )(lowerCamelCase_ )
												else:
																		return test_case

						return _require_spacy_model

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
						"""simple docstring"""






						try:
												import pyspark  # noqa F401
						except ImportError:
												return unittest.skip("test requires pyspark" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						try:
												import joblibspark  # noqa F401
						except ImportError:
												return unittest.skip("test requires joblibspark" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						if not _run_slow_tests or _run_slow_tests == 0:
												snake_case   :			Dict						    = unittest.skip("test is slow" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						if not _run_local_tests or _run_local_tests == 0:
												snake_case   :			int						    = unittest.skip("test is local" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
						"""simple docstring"""






						if not _run_packaged_tests or _run_packaged_tests == 0:
												snake_case   :			List[str]						    = unittest.skip("test is packaged" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






						if not _run_remote_tests or _run_remote_tests == 0:
												snake_case   :			Union[str, Any]						    = unittest.skip("test requires remote" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			*lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						def decorate(cls:							int ):
												for name, fn in cls.__dict__.items():
																		if callable(lowerCamelCase_ ) and name.startswith("test" ):
																								for decorator in decorators:
																														snake_case   :			Optional[Any]						    = decorator(lowerCamelCase_ )
																								setattr(cls     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												return cls

						return decorate

class       _a  ( SCREAMING_SNAKE_CASE__):
			pass



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     0
			__magic_name__     					=     1
			__magic_name__     					=     2



@contextmanager
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int=OfflineSimulationMode.CONNECTION_FAILS     ,		lowerCamelCase_:							Dict=1e-16 ):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = requests.Session().request

						def timeout_request(lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							List[Any]     ,		lowerCamelCase_:							str     ,		**lowerCamelCase_:							List[Any] ):
												# Change the url to an invalid url so that the connection hangs
												snake_case   :			Optional[int]						    = "https://10.255.255.1"
												if kwargs.get("timeout" ) is None:
																		raise RequestWouldHangIndefinitelyError(
																		    f'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' )
												snake_case   :			str						    = timeout
												try:
																		return online_request(lowerCamelCase_     ,		lowerCamelCase_     ,		**lowerCamelCase_ )
												except Exception as e:
																		# The following changes in the error are just here to make the offline timeout error prettier
																		snake_case   :			Dict						    = url
																		snake_case   :			Union[str, Any]						    = e.args[0]
																		snake_case   :			Any						    = (max_retry_error.args[0].replace("10.255.255.1"     ,		f'''OfflineMock[{url}]''' ),)
																		snake_case   :			List[Any]						    = (max_retry_error,)
																		raise

						def raise_connection_error(lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							str     ,		**lowerCamelCase_:							int ):
												raise requests.ConnectionError("Offline mode is enabled."     ,		request=lowerCamelCase_ )

						if mode is OfflineSimulationMode.CONNECTION_FAILS:
												with patch("requests.Session.send"     ,		lowerCamelCase_ ):
																		yield
						elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
												# inspired from https://stackoverflow.com/a/904609
												with patch("requests.Session.request"     ,		lowerCamelCase_ ):
																		yield
						elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
												with patch("datasets.config.HF_DATASETS_OFFLINE"     ,		lowerCamelCase_ ):
																		yield
						else:
												raise ValueError("Please use a value from the OfflineSimulationMode enum." )

@contextmanager
def  __SCREAMING_SNAKE_CASE		(			*lowerCamelCase_:							List[str]     ,		**lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Any						    = str(Path().resolve() )
						with tempfile.TemporaryDirectory(*lowerCamelCase_     ,		**lowerCamelCase_ ) as tmp_dir:
												try:
																		os.chdir(lowerCamelCase_ )
																		yield
												finally:
																		os.chdir(lowerCamelCase_ )

@contextmanager
def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						import gc

						gc.collect()
						snake_case   :			Tuple						    = pa.total_allocated_bytes()
						yield
						assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."

@contextmanager
def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						import gc

						gc.collect()
						snake_case   :			Optional[int]						    = pa.total_allocated_bytes()
						yield
						assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						return deepcopy(lowerCamelCase_ ).integers(0     ,		1_0_0     ,		1_0 ).tolist() == deepcopy(lowerCamelCase_ ).integers(0     ,		1_0_0     ,		1_0 ).tolist()

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						import decorator
						from requests.exceptions import HTTPError

						def _wrapper(lowerCamelCase_:							Dict     ,		*lowerCamelCase_:							Tuple     ,		**lowerCamelCase_:							Any ):
												try:
																		return func(*lowerCamelCase_     ,		**lowerCamelCase_ )
												except HTTPError as err:
																		if str(lowerCamelCase_ ).startswith("500" ) or str(lowerCamelCase_ ).startswith("502" ):
																								pytest.xfail(str(lowerCamelCase_ ) )
																		raise err

						return decorator.decorator(_wrapper     ,		lowerCamelCase_ )

class       _a  :



			def __init__(			self							:				str   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				str   ,	_lowercase							:				Optional[Any]		)							->					Optional[int]:
									snake_case   :			List[Any]						    = returncode
									snake_case   :			Tuple						    = stdout
									snake_case   :			Dict						    = stderr



async def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						while True:
												snake_case   :			List[Any]						    = await stream.readline()
												if line:
																		callback(lowerCamelCase_ )
												else:
																		break

async def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any     ,		lowerCamelCase_:							Dict=None     ,		lowerCamelCase_:							Union[str, Any]=None     ,		lowerCamelCase_:							int=None     ,		lowerCamelCase_:							str=False     ,		lowerCamelCase_:							Optional[Any]=False ):
						"""simple docstring"""






						if echo:
												print("\nRunning: "     ,		" ".join(lowerCamelCase_ ) )

						snake_case   :			Any						    = await asyncio.create_subprocess_exec(
						    cmd[0]     ,		*cmd[1:]     ,		stdin=lowerCamelCase_     ,		stdout=asyncio.subprocess.PIPE     ,		stderr=asyncio.subprocess.PIPE     ,		env=lowerCamelCase_     ,		)

						# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
						# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
						#
						# If it starts hanging, will need to switch to the following code. The problem is that no data
						# will be seen until it's done and if it hangs for example there will be no debug info.
						# out, err = await p.communicate()
						# return _RunOutput(p.returncode, out, err)

						snake_case   :			Optional[int]						    = []
						snake_case   :			Tuple						    = []

						def tee(lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Union[str, Any]="" ):
												snake_case   :			Optional[Any]						    = line.decode("utf-8" ).rstrip()
												sink.append(lowerCamelCase_ )
												if not quiet:
																		print(lowerCamelCase_     ,		lowerCamelCase_     ,		file=lowerCamelCase_ )

    # XXX: the timeout doesn't seem to make any difference here
						await asyncio.wait(
						    [
						        _read_stream(p.stdout     ,		lambda lowerCamelCase_ : tee(lowerCamelCase_     ,		lowerCamelCase_     ,		sys.stdout     ,		label="stdout:" ) ),
						        _read_stream(p.stderr     ,		lambda lowerCamelCase_ : tee(lowerCamelCase_     ,		lowerCamelCase_     ,		sys.stderr     ,		label="stderr:" ) ),
						    ]     ,		timeout=lowerCamelCase_     ,		)
						return _RunOutput(await p.wait()     ,		lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any]=None     ,		lowerCamelCase_:							Any=None     ,		lowerCamelCase_:							List[str]=1_8_0     ,		lowerCamelCase_:							Dict=False     ,		lowerCamelCase_:							Optional[Any]=True ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = asyncio.get_event_loop()
						snake_case   :			str						    = loop.run_until_complete(
						    _stream_subprocess(lowerCamelCase_     ,		env=lowerCamelCase_     ,		stdin=lowerCamelCase_     ,		timeout=lowerCamelCase_     ,		quiet=lowerCamelCase_     ,		echo=lowerCamelCase_ ) )

						snake_case   :			Union[str, Any]						    = " ".join(lowerCamelCase_ )
						if result.returncode > 0:
												snake_case   :			List[Any]						    = "\n".join(result.stderr )
												raise RuntimeError(
												    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
												    f'''The combined stderr from workers follows:\n{stderr}''' )

						# check that the subprocess actually did run and produced some output, should the test rely on
						# the remote side to do the testing
						if not result.stdout and not result.stderr:
												raise RuntimeError(f'''\'{cmd_str}\' produced no output.''' )

						return result

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			int						    = os.environ.get("PYTEST_XDIST_WORKER"     ,		"gw0" )
						snake_case   :			Dict						    = re.sub(r"^gw"     ,		""     ,		lowerCamelCase_     ,		0     ,		re.M )
						return int(lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			int						    = 2_9_5_0_0
						snake_case   :			str						    = pytest_xdist_worker_id()
						return port + uniq_delta



