






'''simple docstring'''

import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCamelCase      					=get_logger(__name__)


UpperCamelCase      					=Path(__file__).parent / "model_card_template.md"
UpperCamelCase      					=uuida().hex
UpperCamelCase      					=os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase      					=os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase      					=HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"



def 				snake_case       (  a_				:			Union[Dict, str, None] = None						)							->					str:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				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(a_     , a_						):
            ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()						)
      elif isinstance(a_     , a_						):
            ua += "; " + user_agent
      return ua



def 				snake_case       (  a_				:			str     , a_				:			Optional[str] = None     , a_				:			Optional[str] = None						)							->					List[str]:





      """simple docstring"""

      if token is None:
            UpperCamelCase_					: Optional[Any]            =				HfFolder.get_token()
      if organization is None:
            UpperCamelCase_					: List[Any]            =				whoami(a_						)["""name"""]
            return f"{username}/{model_id}"
      else:
            return f"{organization}/{model_id}"



def 				snake_case       (  a_				:			Union[str, Any]     , a_				:			List[str]						)							->					Union[str, Any]:





      """simple docstring"""

      if not is_jinja_available():
            raise ValueError(
                """Modelcard rendering is based on Jinja templates."""
                """ Please make sure to have `jinja` installed before using `create_model_card`."""
                """ To install it, please run `pip install Jinja2`."""						)

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

      UpperCamelCase_					: Optional[int]            =				args.hub_token if hasattr(a_     , """hub_token"""						) else None
      UpperCamelCase_					: Tuple            =				get_full_repo_name(a_     , token=a_						)

      UpperCamelCase_					: Union[str, Any]            =				ModelCard.from_template(
          card_data=ModelCardData(  # 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=a_     , model_name=a_     , repo_name=a_     , dataset_name=args.dataset_name if hasattr(a_     , """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(a_     , """gradient_accumulation_steps"""						) else None
          )     , adam_betaa=args.adam_betaa if hasattr(a_     , """adam_beta1"""						) else None     , adam_betaa=args.adam_betaa if hasattr(a_     , """adam_beta2"""						) else None     , adam_weight_decay=args.adam_weight_decay if hasattr(a_     , """adam_weight_decay"""						) else None     , adam_epsilon=args.adam_epsilon if hasattr(a_     , """adam_epsilon"""						) else None     , lr_scheduler=args.lr_scheduler if hasattr(a_     , """lr_scheduler"""						) else None     , lr_warmup_steps=args.lr_warmup_steps if hasattr(a_     , """lr_warmup_steps"""						) else None     , ema_inv_gamma=args.ema_inv_gamma if hasattr(a_     , """ema_inv_gamma"""						) else None     , ema_power=args.ema_power if hasattr(a_     , """ema_power"""						) else None     , ema_max_decay=args.ema_max_decay if hasattr(a_     , """ema_max_decay"""						) else None     , mixed_precision=args.mixed_precision     , )

      UpperCamelCase_					: str            =				os.path.join(args.output_dir     , """README.md"""						)
      model_card.save(a_						)



def 				snake_case       (  a_				:			Optional[str]     , a_				:			Optional[str] = None						)							->					List[str]:





      """simple docstring"""

      if resolved_file is None or commit_hash is not None:
            return commit_hash
      UpperCamelCase_					: Union[str, Any]            =				str(Path(a_						).as_posix()						)
      UpperCamelCase_					: List[Any]            =				re.search(r"""snapshots/([^/]+)/"""     , a_						)
      if search is None:
            return None
      UpperCamelCase_					: List[Any]            =				search.groups()[0]
      return commit_hash if REGEX_COMMIT_HASH.match(a_						) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCamelCase      					=os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCamelCase      					=os.path.join(hf_cache_home, "diffusers")



def 				snake_case       (  a_				:			Optional[str] = None     , a_				:			Optional[str] = None						)							->					None:





      """simple docstring"""

      if new_cache_dir is None:
            UpperCamelCase_					: Tuple            =				DIFFUSERS_CACHE
      if old_cache_dir is None:
            UpperCamelCase_					: Dict            =				old_diffusers_cache

      UpperCamelCase_					: Optional[Any]            =				Path(a_						).expanduser()
      UpperCamelCase_					: Optional[int]            =				Path(a_						).expanduser()
      for old_blob_path in old_cache_dir.glob("""**/blobs/*"""						):
            if old_blob_path.is_file() and not old_blob_path.is_symlink():
                  UpperCamelCase_					: Dict            =				new_cache_dir / old_blob_path.relative_to(a_						)
                  new_blob_path.parent.mkdir(parents=a_     , exist_ok=a_						)
                  os.replace(a_     , a_						)
                  try:
                        os.symlink(a_     , a_						)
                  except OSError:
                        logger.warning(
                            """Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."""						)
      # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCamelCase      					=os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
      UpperCamelCase      					=0
else:
      with open(cache_version_file) as f:
            try:
                  UpperCamelCase      					=int(f.read())
            except ValueError:
                  UpperCamelCase      					=0

if cache_version < 1:
      UpperCamelCase      					=os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
      if old_cache_is_not_empty:
            logger.warning(
                "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
                "existing cached models. This is a one-time operation, you can interrupt it or run it "
                "later by calling `diffusers.utils.hub_utils.move_cache()`."
            )
            try:
                  move_cache()
            except Exception as e:
                  UpperCamelCase      					="\n".join(traceback.format_tb(e.__traceback__))
                  logger.error(
                      f"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
                      "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
                      "message and we will do our best to help."
                  )

if cache_version < 1:
      try:
            os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
            with open(cache_version_file, "w") as f:
                  f.write("1")
      except Exception:
            logger.warning(
                f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
                "the directory exists and can be written to."
            )



def 				snake_case       (  a_				:			str     , a_				:			Optional[str] = None						)							->					str:





      """simple docstring"""

      if variant is not None:
            UpperCamelCase_					: List[str]            =				weights_name.split("""."""						)
            UpperCamelCase_					: Optional[int]            =				splits[:-1] + [variant] + splits[-1:]
            UpperCamelCase_					: Tuple            =				""".""".join(a_						)

      return weights_name



def 				snake_case       (  a_				:			List[str]     , *,
    a_				:			int     , a_				:			int     , a_				:			Tuple     , a_				:			Optional[int]     , a_				:			str     , a_				:			Union[str, Any]     , a_				:			List[str]     , a_				:			List[Any]     , a_				:			Optional[Any]     , a_				:			Optional[int]     , a_				:			int=None     , )							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				str(a_						)
      if os.path.isfile(a_						):
            return pretrained_model_name_or_path
      elif os.path.isdir(a_						):
            if os.path.isfile(os.path.join(a_     , a_						)						):
                  # Load from a PyTorch checkpoint
                  UpperCamelCase_					: Union[str, Any]            =				os.path.join(a_     , a_						)
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(a_     , a_     , a_						)						):
                  UpperCamelCase_					: Tuple            =				os.path.join(a_     , a_     , a_						)
                  return model_file
            else:
                  raise EnvironmentError(
                      f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."						)
      else:
            # 1. First check if deprecated way of loading from branches is used
            if (
                revision in DEPRECATED_REVISION_ARGS
                and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
                and version.parse(version.parse(a_						).base_version						) >= version.parse("""0.20.0"""						)
            ):
                  try:
                        UpperCamelCase_					: Optional[int]            =				hf_hub_download(
                            a_     , filename=_add_variant(a_     , a_						)     , cache_dir=a_     , force_download=a_     , proxies=a_     , resume_download=a_     , local_files_only=a_     , use_auth_token=a_     , user_agent=a_     , subfolder=a_     , revision=revision or commit_hash     , )
                        warnings.warn(
                            f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."     , a_     , )
                        return model_file
                  except:  # noqa: E722
                        warnings.warn(
                            f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a_     , a_						)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a_     , a_						)}' so that the correct variant file can be added."     , a_     , )
            try:
                  # 2. Load model file as usual
                  UpperCamelCase_					: Any            =				hf_hub_download(
                      a_     , filename=a_     , cache_dir=a_     , force_download=a_     , proxies=a_     , resume_download=a_     , local_files_only=a_     , use_auth_token=a_     , user_agent=a_     , subfolder=a_     , revision=revision or commit_hash     , )
                  return model_file

            except RepositoryNotFoundError:
                  raise EnvironmentError(
                      f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                      """listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a """
                      """token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
                      """login`."""						)
            except RevisionNotFoundError:
                  raise EnvironmentError(
                      f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                      """this model name. Check the model page at """
                      f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."						)
            except EntryNotFoundError:
                  raise EnvironmentError(
                      f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."						)
            except HTTPError as err:
                  raise EnvironmentError(
                      f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"						)
            except ValueError:
                  raise EnvironmentError(
                      f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                      f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                      f" directory containing a file named {weights_name} or"
                      """ \nCheckout your internet connection or see how to run the library in"""
                      """ offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."""						)
            except EnvironmentError:
                  raise EnvironmentError(
                      f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                      """'https://huggingface.co/models', make sure you don't have a local directory with the same name. """
                      f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                      f"containing a file named {weights_name}"						)














'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .multicontrolnet import MultiControlNetModel
      from .pipeline_controlnet import StableDiffusionControlNetPipeline
      from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
      from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
      from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






