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# Copyright 2022 The HuggingFace Team. All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import platform

from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version

from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available

from . import BaseDiffusersCLICommand

def info_command_factory(_):

return EnvironmentCommand()

class EnvironmentCommand(BaseDiffusersCLICommand):

@staticmethod

def register_subcommand(parser: ArgumentParser):

download_parser = parser.add_parser("env")

download_parser.set_defaults(func=info_command_factory)

def run(self):

hub_version = huggingface_hub.__version__

pt_version = "not installed"

pt_cuda_available = "NA"

if is_torch_available():

import torch

pt_version = torch.__version__

pt_cuda_available = torch.cuda.is_available()

transformers_version = "not installed"

if is_transformers_available():

import transformers

transformers_version = transformers.__version__

accelerate_version = "not installed"

if is_accelerate_available():

import accelerate

transformers_version = accelerate.__version__

xformers_version = "not installed"

if is_xformers_available():

import xformers

xformers_version = xformers.__version__

info = {

"`diffusers` version": version,

"Platform": platform.platform(),

"Python version": platform.python_version(),

"PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",

"Huggingface_hub version": hub_version,

"Transformers version": transformers_version,

"Accelerate version": accelerate_version,

"xFormers version": xformers_version,

"Using GPU in script?": "<fill in>",

"Using distributed or parallel set-up in script?": "<fill in>",

}

print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")

print(self.format_dict(info))

return info

@staticmethod

def format_dict(d):

return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"