import functools

import importlib

import inspect

import io

import logging

import multiprocessing

import os

import random

import re

import struct

import sys

import tempfile

import time

import unittest

import urllib.parse

from contextlib import contextmanager

from io import BytesIO, StringIO

from pathlib import Path

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

import numpy as np

import PIL.Image

import PIL.ImageOps

import requests

from numpy.linalg import norm

from packaging import version

from .import_utils import (

BACKENDS_MAPPING,

is_compel_available,

is_flax_available,

is_note_seq_available,

is_onnx_available,

is_opencv_available,

is_peft_available,

is_timm_available,

is_torch_available,

is_torch_version,

is_torchsde_available,

is_transformers_available,

)

from .logging import get_logger

global_rng = random.Random()

logger = get_logger(__name__)

_required_peft_version = is_peft_available() and version.parse(

version.parse(importlib.metadata.version("peft")).base_version

) > version.parse("0.5")

_required_transformers_version = is_transformers_available() and version.parse(

version.parse(importlib.metadata.version("transformers")).base_version

) > version.parse("4.33")

USE_PEFT_BACKEND = _required_peft_version and _required_transformers_version

if is_torch_available():

import torch

# Set a backend environment variable for any extra module import required for a custom accelerator

if "DIFFUSERS_TEST_BACKEND" in os.environ:

backend = os.environ["DIFFUSERS_TEST_BACKEND"]

try:

_ = importlib.import_module(backend)

except ModuleNotFoundError as e:

raise ModuleNotFoundError(

f"Failed to import `DIFFUSERS_TEST_BACKEND` '{backend}'! This should be the name of an installed module \

to enable a specified backend.):\n{e}"

) from e

if "DIFFUSERS_TEST_DEVICE" in os.environ:

torch_device = os.environ["DIFFUSERS_TEST_DEVICE"]

try:

# try creating device to see if provided device is valid

_ = torch.device(torch_device)

except RuntimeError as e:

raise RuntimeError(

f"Unknown testing device specified by environment variable `DIFFUSERS_TEST_DEVICE`: {torch_device}"

) from e

logger.info(f"torch_device overrode to {torch_device}")

else:

torch_device = "cuda" if torch.cuda.is_available() else "cpu"

is_torch_higher_equal_than_1_12 = version.parse(

version.parse(torch.__version__).base_version

) >= version.parse("1.12")

if is_torch_higher_equal_than_1_12:

# Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details

mps_backend_registered = hasattr(torch.backends, "mps")

torch_device = "mps" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device

def torch_all_close(a, b, *args, **kwargs):

if not is_torch_available():

raise ValueError("PyTorch needs to be installed to use this function.")

if not torch.allclose(a, b, *args, **kwargs):

assert False, f"Max diff is absolute {(a - b).abs().max()}. Diff tensor is {(a - b).abs()}."

return True

def numpy_cosine_similarity_distance(a, b):

similarity = np.dot(a, b) / (norm(a) * norm(b))

distance = 1.0 - similarity.mean()

return distance

def print_tensor_test(

tensor,

limit_to_slices=None,

max_torch_print=None,

filename="test_corrections.txt",

expected_tensor_name="expected_slice",

):

if max_torch_print:

torch.set_printoptions(threshold=10_000)

test_name = os.environ.get("PYTEST_CURRENT_TEST")

if not torch.is_tensor(tensor):

tensor = torch.from_numpy(tensor)

if limit_to_slices:

tensor = tensor[0, -3:, -3:, -1]

tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace("\n", "")

# format is usually:

# expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161])

output_str = tensor_str.replace("tensor", f"{expected_tensor_name} = np.array")

test_file, test_class, test_fn = test_name.split("::")

test_fn = test_fn.split()[0]

with open(filename, "a") as f:

print("::".join([test_file, test_class, test_fn, output_str]), file=f)

def get_tests_dir(append_path=None):

"""

Args:

append_path: optional path to append to the tests dir path

Return:

The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is

joined after the `tests` dir the former is provided.

"""

# this function caller's __file__

caller__file__ = inspect.stack()[1][1]

tests_dir = os.path.abspath(os.path.dirname(caller__file__))

while not tests_dir.endswith("tests"):

tests_dir = os.path.dirname(tests_dir)

if append_path:

return Path(tests_dir, append_path).as_posix()

else:

return tests_dir

# Taken from the following PR:

# https://github.com/huggingface/accelerate/pull/1964

def str_to_bool(value) -> int:

"""

Converts a string representation of truth to `True` (1) or `False` (0). True values are `y`, `yes`, `t`, `true`,

`on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;

"""

value = value.lower()

if value in ("y", "yes", "t", "true", "on", "1"):

return 1

elif value in ("n", "no", "f", "false", "off", "0"):

return 0

else:

raise ValueError(f"invalid truth value {value}")

def parse_flag_from_env(key, default=False):

try:

value = os.environ[key]

except KeyError:

# KEY isn't set, default to `default`.

_value = default

else:

# KEY is set, convert it to True or False.

try:

_value = str_to_bool(value)

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

_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)

_run_nightly_tests = parse_flag_from_env("RUN_NIGHTLY", default=False)

def floats_tensor(shape, scale=1.0, rng=None, name=None):

"""Creates a random float32 tensor"""

if rng is None:

rng = global_rng

total_dims = 1

for dim in shape:

total_dims *= dim

values = []

for _ in range(total_dims):

values.append(rng.random() * scale)

return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()

def slow(test_case):

"""

Decorator marking a test as slow.

Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.

"""

return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)

def nightly(test_case):

"""

Decorator marking a test that runs nightly in the diffusers CI.

Slow tests are skipped by default. Set the RUN_NIGHTLY environment variable to a truthy value to run them.

"""

return unittest.skipUnless(_run_nightly_tests, "test is nightly")(test_case)

def require_torch(test_case):

"""

Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed.

"""

return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case)

def require_torch_2(test_case):

"""

Decorator marking a test that requires PyTorch 2. These tests are skipped when it isn't installed.

"""

return unittest.skipUnless(is_torch_available() and is_torch_version(">=", "2.0.0"), "test requires PyTorch 2")(

test_case

)

def require_torch_gpu(test_case):

"""Decorator marking a test that requires CUDA and PyTorch."""

return unittest.skipUnless(is_torch_available() and torch_device == "cuda", "test requires PyTorch+CUDA")(

test_case

)

# These decorators are for accelerator-specific behaviours that are not GPU-specific

def require_torch_accelerator(test_case):

"""Decorator marking a test that requires an accelerator backend and PyTorch."""

return unittest.skipUnless(is_torch_available() and torch_device != "cpu", "test requires accelerator+PyTorch")(

test_case

)

def require_torch_multi_gpu(test_case):

"""

Decorator marking a test that requires a multi-GPU setup (in PyTorch). These tests are skipped on a machine without

multiple GPUs. To run *only* the multi_gpu tests, assuming all test names contain multi_gpu: $ pytest -sv ./tests

-k "multi_gpu"

"""

if not is_torch_available():

return unittest.skip("test requires PyTorch")(test_case)

import torch

return unittest.skipUnless(torch.cuda.device_count() > 1, "test requires multiple GPUs")(test_case)

def require_torch_accelerator_with_fp16(test_case):

"""Decorator marking a test that requires an accelerator with support for the FP16 data type."""

return unittest.skipUnless(_is_torch_fp16_available(torch_device), "test requires accelerator with fp16 support")(

test_case

)

def require_torch_accelerator_with_fp64(test_case):

"""Decorator marking a test that requires an accelerator with support for the FP64 data type."""

return unittest.skipUnless(_is_torch_fp64_available(torch_device), "test requires accelerator with fp64 support")(

test_case

)

def require_torch_accelerator_with_training(test_case):

"""Decorator marking a test that requires an accelerator with support for training."""

return unittest.skipUnless(

is_torch_available() and backend_supports_training(torch_device),

"test requires accelerator with training support",

)(test_case)

def skip_mps(test_case):

"""Decorator marking a test to skip if torch_device is 'mps'"""

return unittest.skipUnless(torch_device != "mps", "test requires non 'mps' device")(test_case)

def require_flax(test_case):

"""

Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed

"""

return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case)

def require_compel(test_case):

"""

Decorator marking a test that requires compel: https://github.com/damian0815/compel. These tests are skipped when

the library is not installed.

"""

return unittest.skipUnless(is_compel_available(), "test requires compel")(test_case)

def require_onnxruntime(test_case):

"""

Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.

"""

return unittest.skipUnless(is_onnx_available(), "test requires onnxruntime")(test_case)

def require_note_seq(test_case):

"""

Decorator marking a test that requires note_seq. These tests are skipped when note_seq isn't installed.

"""

return unittest.skipUnless(is_note_seq_available(), "test requires note_seq")(test_case)

def require_torchsde(test_case):

"""

Decorator marking a test that requires torchsde. These tests are skipped when torchsde isn't installed.

"""

return unittest.skipUnless(is_torchsde_available(), "test requires torchsde")(test_case)

def require_peft_backend(test_case):

"""

Decorator marking a test that requires PEFT backend, this would require some specific versions of PEFT and

transformers.

"""

return unittest.skipUnless(USE_PEFT_BACKEND, "test requires PEFT backend")(test_case)

def require_timm(test_case):

"""

Decorator marking a test that requires timm. These tests are skipped when timm isn't installed.

"""

return unittest.skipUnless(is_timm_available(), "test requires timm")(test_case)

def require_peft_version_greater(peft_version):

"""

Decorator marking a test that requires PEFT backend with a specific version, this would require some specific

versions of PEFT and transformers.

"""

def decorator(test_case):

correct_peft_version = is_peft_available() and version.parse(

version.parse(importlib.metadata.version("peft")).base_version

) > version.parse(peft_version)

return unittest.skipUnless(

correct_peft_version, f"test requires PEFT backend with the version greater than {peft_version}"

)(test_case)

return decorator

def require_accelerate_version_greater(accelerate_version):

def decorator(test_case):

correct_accelerate_version = is_peft_available() and version.parse(

version.parse(importlib.metadata.version("accelerate")).base_version

) > version.parse(accelerate_version)

return unittest.skipUnless(

correct_accelerate_version, f"Test requires accelerate with the version greater than {accelerate_version}."

)(test_case)

return decorator

def deprecate_after_peft_backend(test_case):

"""

Decorator marking a test that will be skipped after PEFT backend

"""

return unittest.skipUnless(not USE_PEFT_BACKEND, "test skipped in favor of PEFT backend")(test_case)

def get_python_version():

sys_info = sys.version_info

major, minor = sys_info.major, sys_info.minor

return major, minor

def require_python39_or_higher(test_case):

def python39_available():

major, minor = get_python_version()

return major == 3 and minor >= 9

return unittest.skipUnless(python39_available(), "test requires Python 3.9 or higher")(test_case)

def load_numpy(arry: Union[str, np.ndarray], local_path: Optional[str] = None) -> np.ndarray:

if isinstance(arry, str):

if local_path is not None:

# local_path can be passed to correct images of tests

return Path(local_path, arry.split("/")[-5], arry.split("/")[-2], arry.split("/")[-1]).as_posix()

elif arry.startswith("http://") or arry.startswith("https://"):

response = requests.get(arry)

response.raise_for_status()

arry = np.load(BytesIO(response.content))

elif os.path.isfile(arry):

arry = np.load(arry)

else:

raise ValueError(

f"Incorrect path or url, URLs must start with `http://` or `https://`, and {arry} is not a valid path"

)

elif isinstance(arry, np.ndarray):

pass

else:

raise ValueError(

"Incorrect format used for numpy ndarray. Should be an url linking to an image, a local path, or a"

" ndarray."

)

return arry

def load_pt(url: str):

response = requests.get(url)

response.raise_for_status()

arry = torch.load(BytesIO(response.content))

return arry

def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:

"""

Loads `image` to a PIL Image.

Args:

image (`str` or `PIL.Image.Image`):

The image to convert to the PIL Image format.

Returns:

`PIL.Image.Image`:

A PIL Image.

"""

if isinstance(image, str):

if image.startswith("http://") or image.startswith("https://"):

image = PIL.Image.open(requests.get(image, stream=True).raw)

elif os.path.isfile(image):

image = PIL.Image.open(image)

else:

raise ValueError(

f"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path"

)

elif isinstance(image, PIL.Image.Image):

image = image

else:

raise ValueError(

"Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image."

)

image = PIL.ImageOps.exif_transpose(image)

image = image.convert("RGB")

return image

def preprocess_image(image: PIL.Image, batch_size: int):

w, h = image.size

w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8

image = image.resize((w, h), resample=PIL.Image.LANCZOS)

image = np.array(image).astype(np.float32) / 255.0

image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)

image = torch.from_numpy(image)

return 2.0 * image - 1.0

def export_to_gif(image: List[PIL.Image.Image], output_gif_path: str = None) -> str:

if output_gif_path is None:

output_gif_path = tempfile.NamedTemporaryFile(suffix=".gif").name

image[0].save(

output_gif_path,

save_all=True,

append_images=image[1:],

optimize=False,

duration=100,

loop=0,

)

return output_gif_path

@contextmanager

def buffered_writer(raw_f):

f = io.BufferedWriter(raw_f)

yield f

f.flush()

def export_to_ply(mesh, output_ply_path: str = None):

"""

Write a PLY file for a mesh.

"""

if output_ply_path is None:

output_ply_path = tempfile.NamedTemporaryFile(suffix=".ply").name

coords = mesh.verts.detach().cpu().numpy()

faces = mesh.faces.cpu().numpy()

rgb = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1)

with buffered_writer(open(output_ply_path, "wb")) as f:

f.write(b"ply\n")

f.write(b"format binary_little_endian 1.0\n")

f.write(bytes(f"element vertex {len(coords)}\n", "ascii"))

f.write(b"property float x\n")

f.write(b"property float y\n")

f.write(b"property float z\n")

if rgb is not None:

f.write(b"property uchar red\n")

f.write(b"property uchar green\n")

f.write(b"property uchar blue\n")

if faces is not None:

f.write(bytes(f"element face {len(faces)}\n", "ascii"))

f.write(b"property list uchar int vertex_index\n")

f.write(b"end_header\n")

if rgb is not None:

rgb = (rgb * 255.499).round().astype(int)

vertices = [

(*coord, *rgb)

for coord, rgb in zip(

coords.tolist(),

rgb.tolist(),

)

]

format = struct.Struct("<3f3B")

for item in vertices:

f.write(format.pack(*item))

else:

format = struct.Struct("<3f")

for vertex in coords.tolist():

f.write(format.pack(*vertex))

if faces is not None:

format = struct.Struct("<B3I")

for tri in faces.tolist():

f.write(format.pack(len(tri), *tri))

return output_ply_path

def export_to_obj(mesh, output_obj_path: str = None):

if output_obj_path is None:

output_obj_path = tempfile.NamedTemporaryFile(suffix=".obj").name

verts = mesh.verts.detach().cpu().numpy()

faces = mesh.faces.cpu().numpy()

vertex_colors = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1)

vertices = [

"{} {} {} {} {} {}".format(*coord, *color) for coord, color in zip(verts.tolist(), vertex_colors.tolist())

]

faces = ["f {} {} {}".format(str(tri[0] + 1), str(tri[1] + 1), str(tri[2] + 1)) for tri in faces.tolist()]

combined_data = ["v " + vertex for vertex in vertices] + faces

with open(output_obj_path, "w") as f:

f.writelines("\n".join(combined_data))

def export_to_video(video_frames: List[np.ndarray], output_video_path: str = None) -> str:

if is_opencv_available():

import cv2

else:

raise ImportError(BACKENDS_MAPPING["opencv"][1].format("export_to_video"))

if output_video_path is None:

output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name

fourcc = cv2.VideoWriter_fourcc(*"mp4v")

h, w, c = video_frames[0].shape

video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=8, frameSize=(w, h))

for i in range(len(video_frames)):

img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)

video_writer.write(img)

return output_video_path

def load_hf_numpy(path) -> np.ndarray:

base_url = "https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main"

if not path.startswith("http://") and not path.startswith("https://"):

path = os.path.join(base_url, urllib.parse.quote(path))

return load_numpy(path)

# --- pytest conf functions --- #

# to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once

pytest_opt_registered = {}

def pytest_addoption_shared(parser):

"""

This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there.

It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest`

option.

"""

option = "--make-reports"

if option not in pytest_opt_registered:

parser.addoption(

option,

action="store",

default=False,

help="generate report files. The value of this option is used as a prefix to report names",

)

pytest_opt_registered[option] = 1

def pytest_terminal_summary_main(tr, id):

"""

Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current

directory. The report files are prefixed with the test suite name.

This function emulates --duration and -rA pytest arguments.

This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined

there.

Args:

- tr: `terminalreporter` passed from `conftest.py`

- id: unique id like `tests` or `examples` that will be incorporated into the final reports filenames - this is

needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other.

NB: this functions taps into a private _pytest API and while unlikely, it could break should

pytest do internal changes - also it calls default internal methods of terminalreporter which

can be hijacked by various `pytest-` plugins and interfere.

"""

from _pytest.config import create_terminal_writer

if not len(id):

id = "tests"

config = tr.config

orig_writer = config.get_terminal_writer()

orig_tbstyle = config.option.tbstyle

orig_reportchars = tr.reportchars

dir = "reports"

Path(dir).mkdir(parents=True, exist_ok=True)

report_files = {

k: f"{dir}/{id}_{k}.txt"

for k in [

"durations",

"errors",

"failures_long",

"failures_short",

"failures_line",

"passes",

"stats",

"summary_short",

"warnings",

]

}

# custom durations report

# note: there is no need to call pytest --durations=XX to get this separate report

# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66

dlist = []

for replist in tr.stats.values():

for rep in replist:

if hasattr(rep, "duration"):

dlist.append(rep)

if dlist:

dlist.sort(key=lambda x: x.duration, reverse=True)

with open(report_files["durations"], "w") as f:

durations_min = 0.05 # sec

f.write("slowest durations\n")

for i, rep in enumerate(dlist):

if rep.duration < durations_min:

f.write(f"{len(dlist)-i} durations < {durations_min} secs were omitted")

break

f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n")

def summary_failures_short(tr):

# expecting that the reports were --tb=long (default) so we chop them off here to the last frame

reports = tr.getreports("failed")

if not reports:

return

tr.write_sep("=", "FAILURES SHORT STACK")

for rep in reports:

msg = tr._getfailureheadline(rep)

tr.write_sep("_", msg, red=True, bold=True)

# chop off the optional leading extra frames, leaving only the last one

longrepr = re.sub(r".*_ _ _ (_ ){10,}_ _ ", "", rep.longreprtext, 0, re.M | re.S)

tr._tw.line(longrepr)

# note: not printing out any rep.sections to keep the report short

# use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each

# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814

# note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g.

# pytest-instafail does that)

# report failures with line/short/long styles

config.option.tbstyle = "auto" # full tb

with open(report_files["failures_long"], "w") as f:

tr._tw = create_terminal_writer(config, f)

tr.summary_failures()

# config.option.tbstyle = "short" # short tb

with open(report_files["failures_short"], "w") as f:

tr._tw = create_terminal_writer(config, f)

summary_failures_short(tr)

config.option.tbstyle = "line" # one line per error

with open(report_files["failures_line"], "w") as f:

tr._tw = create_terminal_writer(config, f)

tr.summary_failures()

with open(report_files["errors"], "w") as f:

tr._tw = create_terminal_writer(config, f)

tr.summary_errors()

with open(report_files["warnings"], "w") as f:

tr._tw = create_terminal_writer(config, f)

tr.summary_warnings() # normal warnings

tr.summary_warnings() # final warnings

tr.reportchars = "wPpsxXEf" # emulate -rA (used in summary_passes() and short_test_summary())

with open(report_files["passes"], "w") as f:

tr._tw = create_terminal_writer(config, f)

tr.summary_passes()

with open(report_files["summary_short"], "w") as f:

tr._tw = create_terminal_writer(config, f)

tr.short_test_summary()

with open(report_files["stats"], "w") as f:

tr._tw = create_terminal_writer(config, f)

tr.summary_stats()

# restore:

tr._tw = orig_writer

tr.reportchars = orig_reportchars

config.option.tbstyle = orig_tbstyle

# Copied from https://github.com/huggingface/transformers/blob/000e52aec8850d3fe2f360adc6fd256e5b47fe4c/src/transformers/testing_utils.py#L1905

def is_flaky(max_attempts: int = 5, wait_before_retry: Optional[float] = None, description: Optional[str] = None):

"""

To decorate flaky tests. They will be retried on failures.

Args:

max_attempts (`int`, *optional*, defaults to 5):

The maximum number of attempts to retry the flaky test.

wait_before_retry (`float`, *optional*):

If provided, will wait that number of seconds before retrying the test.

description (`str`, *optional*):

A string to describe the situation (what / where / why is flaky, link to GH issue/PR comments, errors,

etc.)

"""

def decorator(test_func_ref):

@functools.wraps(test_func_ref)

def wrapper(*args, **kwargs):

retry_count = 1

while retry_count < max_attempts:

try:

return test_func_ref(*args, **kwargs)

except Exception as err:

print(f"Test failed with {err} at try {retry_count}/{max_attempts}.", file=sys.stderr)

if wait_before_retry is not None:

time.sleep(wait_before_retry)

retry_count += 1

return test_func_ref(*args, **kwargs)

return wrapper

return decorator

# Taken from: https://github.com/huggingface/transformers/blob/3658488ff77ff8d45101293e749263acf437f4d5/src/transformers/testing_utils.py#L1787

def run_test_in_subprocess(test_case, target_func, inputs=None, timeout=None):

"""

To run a test in a subprocess. In particular, this can avoid (GPU) memory issue.

Args:

test_case (`unittest.TestCase`):

The test that will run `target_func`.

target_func (`Callable`):

The function implementing the actual testing logic.

inputs (`dict`, *optional*, defaults to `None`):

The inputs that will be passed to `target_func` through an (input) queue.

timeout (`int`, *optional*, defaults to `None`):

The timeout (in seconds) that will be passed to the input and output queues. If not specified, the env.

variable `PYTEST_TIMEOUT` will be checked. If still `None`, its value will be set to `600`.

"""

if timeout is None:

timeout = int(os.environ.get("PYTEST_TIMEOUT", 600))

start_methohd = "spawn"

ctx = multiprocessing.get_context(start_methohd)

input_queue = ctx.Queue(1)

output_queue = ctx.JoinableQueue(1)

# We can't send `unittest.TestCase` to the child, otherwise we get issues regarding pickle.

input_queue.put(inputs, timeout=timeout)

process = ctx.Process(target=target_func, args=(input_queue, output_queue, timeout))

process.start()

# Kill the child process if we can't get outputs from it in time: otherwise, the hanging subprocess prevents

# the test to exit properly.

try:

results = output_queue.get(timeout=timeout)

output_queue.task_done()

except Exception as e:

process.terminate()

test_case.fail(e)

process.join(timeout=timeout)

if results["error"] is not None:

test_case.fail(f'{results["error"]}')

class CaptureLogger:

"""

Args:

Context manager to capture `logging` streams

logger: 'logging` logger object

Returns:

The captured output is available via `self.out`

Example:

```python

>>> from diffusers import logging

>>> from diffusers.testing_utils import CaptureLogger

>>> msg = "Testing 1, 2, 3"

>>> logging.set_verbosity_info()

>>> logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.py")

>>> with CaptureLogger(logger) as cl:

... logger.info(msg)

>>> assert cl.out, msg + "\n"

```

"""

def __init__(self, logger):

self.logger = logger

self.io = StringIO()

self.sh = logging.StreamHandler(self.io)

self.out = ""

def __enter__(self):

self.logger.addHandler(self.sh)

return self

def __exit__(self, *exc):

self.logger.removeHandler(self.sh)

self.out = self.io.getvalue()

def __repr__(self):

return f"captured: {self.out}\n"

def enable_full_determinism():

"""

Helper function for reproducible behavior during distributed training. See

- https://pytorch.org/docs/stable/notes/randomness.html for pytorch

"""

# Enable PyTorch deterministic mode. This potentially requires either the environment

# variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,

# depending on the CUDA version, so we set them both here

os.environ["CUDA_LAUNCH_BLOCKING"] = "1"

os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"

torch.use_deterministic_algorithms(True)

# Enable CUDNN deterministic mode

torch.backends.cudnn.deterministic = True

torch.backends.cudnn.benchmark = False

torch.backends.cuda.matmul.allow_tf32 = False

def disable_full_determinism():

os.environ["CUDA_LAUNCH_BLOCKING"] = "0"

os.environ["CUBLAS_WORKSPACE_CONFIG"] = ""

torch.use_deterministic_algorithms(False)

# Utils for custom and alternative accelerator devices

def _is_torch_fp16_available(device):

if not is_torch_available():

return False

import torch

device = torch.device(device)

try:

x = torch.zeros((2, 2), dtype=torch.float16).to(device)

_ = torch.mul(x, x)

return True

except Exception as e:

if device.type == "cuda":

raise ValueError(

f"You have passed a device of type 'cuda' which should work with 'fp16', but 'cuda' does not seem to be correctly installed on your machine: {e}"

)

return False

def _is_torch_fp64_available(device):

if not is_torch_available():

return False

import torch

device = torch.device(device)

try:

x = torch.zeros((2, 2), dtype=torch.float64).to(device)

_ = torch.mul(x, x)

return True

except Exception as e:

if device.type == "cuda":

raise ValueError(

f"You have passed a device of type 'cuda' which should work with 'fp64', but 'cuda' does not seem to be correctly installed on your machine: {e}"

)

return False

# Guard these lookups for when Torch is not used - alternative accelerator support is for PyTorch

if is_torch_available():

# Behaviour flags

BACKEND_SUPPORTS_TRAINING = {"cuda": True, "cpu": True, "mps": False, "default": True}

# Function definitions

BACKEND_EMPTY_CACHE = {"cuda": torch.cuda.empty_cache, "cpu": None, "mps": None, "default": None}

BACKEND_DEVICE_COUNT = {"cuda": torch.cuda.device_count, "cpu": lambda: 0, "mps": lambda: 0, "default": 0}

BACKEND_MANUAL_SEED = {"cuda": torch.cuda.manual_seed, "cpu": torch.manual_seed, "default": torch.manual_seed}

# This dispatches a defined function according to the accelerator from the function definitions.

def _device_agnostic_dispatch(device: str, dispatch_table: Dict[str, Callable], *args, **kwargs):

if device not in dispatch_table:

return dispatch_table["default"](*args, **kwargs)

fn = dispatch_table[device]

# Some device agnostic functions return values. Need to guard against 'None' instead at

# user level

if fn is None:

return None

return fn(*args, **kwargs)

# These are callables which automatically dispatch the function specific to the accelerator

def backend_manual_seed(device: str, seed: int):

return _device_agnostic_dispatch(device, BACKEND_MANUAL_SEED, seed)

def backend_empty_cache(device: str):

return _device_agnostic_dispatch(device, BACKEND_EMPTY_CACHE)

def backend_device_count(device: str):

return _device_agnostic_dispatch(device, BACKEND_DEVICE_COUNT)

# These are callables which return boolean behaviour flags and can be used to specify some

# device agnostic alternative where the feature is unsupported.

def backend_supports_training(device: str):

if not is_torch_available():

return False

if device not in BACKEND_SUPPORTS_TRAINING:

device = "default"

return BACKEND_SUPPORTS_TRAINING[device]

# Guard for when Torch is not available

if is_torch_available():

# Update device function dict mapping

def update_mapping_from_spec(device_fn_dict: Dict[str, Callable], attribute_name: str):

try:

# Try to import the function directly

spec_fn = getattr(device_spec_module, attribute_name)

device_fn_dict[torch_device] = spec_fn

except AttributeError as e:

# If the function doesn't exist, and there is no default, throw an error

if "default" not in device_fn_dict:

raise AttributeError(