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# coding=utf-8

# Copyright 2022 The HuggingFace Inc. team.

#

# 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 os

import sys

from pathlib import Path

from typing import Dict, Optional, Union

from uuid import uuid4

from huggingface_hub import HfFolder, whoami

from . import __version__

from .utils import ENV_VARS_TRUE_VALUES, HUGGINGFACE_CO_RESOLVE_ENDPOINT, logging

from .utils.import_utils import (

_flax_version,

_jax_version,

_onnxruntime_version,

_torch_version,

is_flax_available,

is_modelcards_available,

is_onnx_available,

is_torch_available,

)

if is_modelcards_available():

from modelcards import CardData, ModelCard

logger = logging.get_logger(__name__)

MODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / "utils" / "model_card_template.md"

SESSION_ID = uuid4().hex

HF_HUB_OFFLINE = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES

DISABLE_TELEMETRY = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES

HUGGINGFACE_CO_TELEMETRY = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"

def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:

"""

Formats a user-agent string with basic info about a request.

"""

ua = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"

if DISABLE_TELEMETRY or HF_HUB_OFFLINE:

return ua + "; telemetry/off"

if is_torch_available():

ua += f"; torch/{_torch_version}"

if is_flax_available():

ua += f"; jax/{_jax_version}"

ua += f"; flax/{_flax_version}"

if is_onnx_available():

ua += f"; onnxruntime/{_onnxruntime_version}"

# CI will set this value to True

if os.environ.get("DIFFUSERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES:

ua += "; is_ci/true"

if isinstance(user_agent, dict):

ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items())

elif isinstance(user_agent, str):

ua += "; " + user_agent

return ua

def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):

if token is None:

token = HfFolder.get_token()

if organization is None:

username = whoami(token)["name"]

return f"{username}/{model_id}"

else:

return f"{organization}/{model_id}"

def create_model_card(args, model_name):

if not is_modelcards_available:

raise ValueError(

"Please make sure to have `modelcards` installed when using the `create_model_card` function. You can"

" install the package with `pip install modelcards`."

)

if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:

return

hub_token = args.hub_token if hasattr(args, "hub_token") else None

repo_name = get_full_repo_name(model_name, token=hub_token)

model_card = ModelCard.from_template(

card_data=CardData( # Card metadata object that will be converted to YAML block

language="en",

license="apache-2.0",

library_name="diffusers",

tags=[],

datasets=args.dataset_name,

metrics=[],

),

template_path=MODEL_CARD_TEMPLATE_PATH,

model_name=model_name,

repo_name=repo_name,

dataset_name=args.dataset_name if hasattr(args, "dataset_name") else None,

learning_rate=args.learning_rate,

train_batch_size=args.train_batch_size,

eval_batch_size=args.eval_batch_size,

gradient_accumulation_steps=args.gradient_accumulation_steps

if hasattr(args, "gradient_accumulation_steps")

else None,

adam_beta1=args.adam_beta1 if hasattr(args, "adam_beta1") else None,

adam_beta2=args.adam_beta2 if hasattr(args, "adam_beta2") else None,

adam_weight_decay=args.adam_weight_decay if hasattr(args, "adam_weight_decay") else None,

adam_epsilon=args.adam_epsilon if hasattr(args, "adam_epsilon") else None,

lr_scheduler=args.lr_scheduler if hasattr(args, "lr_scheduler") else None,

lr_warmup_steps=args.lr_warmup_steps if hasattr(args, "lr_warmup_steps") else None,

ema_inv_gamma=args.ema_inv_gamma if hasattr(args, "ema_inv_gamma") else None,

ema_power=args.ema_power if hasattr(args, "ema_power") else None,

ema_max_decay=args.ema_max_decay if hasattr(args, "ema_max_decay") else None,

mixed_precision=args.mixed_precision,

)

card_path = os.path.join(args.output_dir, "README.md")

model_card.save(card_path)