'''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


_a   :	List[Any] 							=   get_logger(__name__)


_a   :	Tuple 							=   Path(__file__).parent / "model_card_template.md"
_a   :	Dict 							=   uuida().hex
_a   :	Tuple 							=   os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
_a   :	Any 							=   os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
_a   :	int 							=   HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"

def _lowercase	(      lowerCamelCase__ = None						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		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(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						):
              ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()						)
       elif isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						):
              ua += "; " + user_agent
       return ua

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__ = None							,    lowerCamelCase__ = None						)					->       Optional[Any]:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





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

       __UpperCAmelCase  :				Optional[int]       				=		args.hub_token if hasattr(SCREAMING_SNAKE_CASE__							,    "hub_token"						) else None
       __UpperCAmelCase  :				Union[str, Any]       				=		get_full_repo_name(SCREAMING_SNAKE_CASE__							,    token=SCREAMING_SNAKE_CASE__						)

       __UpperCAmelCase  :				Optional[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=SCREAMING_SNAKE_CASE__							,    model_name=SCREAMING_SNAKE_CASE__							,    repo_name=SCREAMING_SNAKE_CASE__							,    dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE__							,    "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(SCREAMING_SNAKE_CASE__							,    "gradient_accumulation_steps"						) else None
           )							,    adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__							,    "adam_beta1"						) else None							,    adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__							,    "adam_beta2"						) else None							,    adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE__							,    "adam_weight_decay"						) else None							,    adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE__							,    "adam_epsilon"						) else None							,    lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE__							,    "lr_scheduler"						) else None							,    lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE__							,    "lr_warmup_steps"						) else None							,    ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE__							,    "ema_inv_gamma"						) else None							,    ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE__							,    "ema_power"						) else None							,    ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE__							,    "ema_max_decay"						) else None							,    mixed_precision=args.mixed_precision							,    )

       __UpperCAmelCase  :				Tuple       				=		os.path.join(args.output_dir							,    "README.md"						)
       model_card.save(SCREAMING_SNAKE_CASE__						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__ = None						)					->       List[str]:





       """simple docstring"""


       if resolved_file is None or commit_hash is not None:
              return commit_hash
       __UpperCAmelCase  :				Optional[int]       				=		str(Path(SCREAMING_SNAKE_CASE__						).as_posix()						)
       __UpperCAmelCase  :				List[str]       				=		re.search(R"snapshots/([^/]+)/"							,    SCREAMING_SNAKE_CASE__						)
       if search is None:
              return None
       __UpperCAmelCase  :				Union[str, Any]       				=		search.groups()[0]
       return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE__						) 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.
_a   :	Union[str, Any] 							=   os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_a   :	str 							=   os.path.join(hf_cache_home, "diffusers")

def _lowercase	(      lowerCamelCase__ = None							,    lowerCamelCase__ = None						)					->       int:





       """simple docstring"""


       if new_cache_dir is None:
              __UpperCAmelCase  :				Tuple       				=		DIFFUSERS_CACHE
       if old_cache_dir is None:
              __UpperCAmelCase  :				List[Any]       				=		old_diffusers_cache

       __UpperCAmelCase  :				List[str]       				=		Path(SCREAMING_SNAKE_CASE__						).expanduser()
       __UpperCAmelCase  :				Union[str, Any]       				=		Path(SCREAMING_SNAKE_CASE__						).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  :				Union[str, Any]       				=		new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE__						)
                     new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__							,    exist_ok=SCREAMING_SNAKE_CASE__						)
                     os.replace(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
                     try:
                            os.symlink(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
                     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).


_a   :	Union[str, Any] 							=   os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
  _a   :	str 							=   0
else:
  with open(cache_version_file) as f:
    try:
      _a   :	Dict 							=   int(f.read())
    except ValueError:
      _a   :	Tuple 							=   0

if cache_version < 1:
  _a   :	Tuple 							=   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:
      _a   :	List[Any] 							=   "\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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__ = None						)					->       Union[str, Any]:





       """simple docstring"""


       if variant is not None:
              __UpperCAmelCase  :				int       				=		weights_name.split("."						)
              __UpperCAmelCase  :				Dict       				=		splits[:-1] + [variant] + splits[-1:]
              __UpperCAmelCase  :				int       				=		""".""".join(SCREAMING_SNAKE_CASE__						)

       return weights_name


def _lowercase	(      lowerCamelCase__							,    *,
    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=None							,    )					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		str(SCREAMING_SNAKE_CASE__						)
       if os.path.isfile(SCREAMING_SNAKE_CASE__						):
              return pretrained_model_name_or_path
       elif os.path.isdir(SCREAMING_SNAKE_CASE__						):
              if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						):
                     # Load from a PyTorch checkpoint
                     __UpperCAmelCase  :				str       				=		os.path.join(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
                     return model_file
              elif subfolder is not None and os.path.isfile(
                  os.path.join(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						):
                     __UpperCAmelCase  :				List[str]       				=		os.path.join(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
                     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(SCREAMING_SNAKE_CASE__						).base_version						) >= version.parse("0.20.0"						)
              ):
                     try:
                            __UpperCAmelCase  :				Tuple       				=		hf_hub_download(
                                SCREAMING_SNAKE_CASE__							,    filename=_add_variant(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)							,    cache_dir=SCREAMING_SNAKE_CASE__							,    force_download=SCREAMING_SNAKE_CASE__							,    proxies=SCREAMING_SNAKE_CASE__							,    resume_download=SCREAMING_SNAKE_CASE__							,    local_files_only=SCREAMING_SNAKE_CASE__							,    use_auth_token=SCREAMING_SNAKE_CASE__							,    user_agent=SCREAMING_SNAKE_CASE__							,    subfolder=SCREAMING_SNAKE_CASE__							,    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."""							,    SCREAMING_SNAKE_CASE__							,    )
                            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(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)} 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(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)}\' so that the correct variant file can be added."""							,    SCREAMING_SNAKE_CASE__							,    )
              try:
                     # 2. Load model file as usual
                     __UpperCAmelCase  :				List[str]       				=		hf_hub_download(
                         SCREAMING_SNAKE_CASE__							,    filename=SCREAMING_SNAKE_CASE__							,    cache_dir=SCREAMING_SNAKE_CASE__							,    force_download=SCREAMING_SNAKE_CASE__							,    proxies=SCREAMING_SNAKE_CASE__							,    resume_download=SCREAMING_SNAKE_CASE__							,    local_files_only=SCREAMING_SNAKE_CASE__							,    use_auth_token=SCREAMING_SNAKE_CASE__							,    user_agent=SCREAMING_SNAKE_CASE__							,    subfolder=SCREAMING_SNAKE_CASE__							,    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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a   :	str 							=   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	str 							=   ["ViTFeatureExtractor"]
   _a   :	Dict 							=   ["ViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ViTForImageClassification",
       "ViTForMaskedImageModeling",
       "ViTModel",
       "ViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[str] 							=   [
       "TFViTForImageClassification",
       "TFViTModel",
       "TFViTPreTrainedModel",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   [
       "FlaxViTForImageClassification",
       "FlaxViTModel",
       "FlaxViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _a   :	Dict 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






