



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


SCREAMING_SNAKE_CASE_:Any             =						get_logger(__name__)


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




def 	__UpperCamelCase	(  _lowerCAmelCase = None		)       ->  str:



    """simple docstring"""






    A :       Union[str, 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(_lowerCAmelCase      ,     _lowerCAmelCase		):
        ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()		)
    elif isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		):
        ua += "; " + user_agent
    return ua




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None		)       ->  Union[str, Any]:



    """simple docstring"""






    if token is None:
        A :       Dict          =   HfFolder.get_token()
    if organization is None:
        A :       int          =   whoami(_lowerCAmelCase		)["""name"""]
        return f'''{username}/{model_id}'''
    else:
        return f'''{organization}/{model_id}'''




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Optional[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(_lowerCAmelCase      ,     """local_rank"""		) and args.local_rank not in [-1, 0]:
        return

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

    A :       int          =   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=_lowerCAmelCase      ,     model_name=_lowerCAmelCase      ,     repo_name=_lowerCAmelCase      ,     dataset_name=args.dataset_name if hasattr(_lowerCAmelCase      ,     """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(_lowerCAmelCase      ,     """gradient_accumulation_steps"""		) else None
        )      ,     adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase      ,     """adam_beta1"""		) else None      ,     adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase      ,     """adam_beta2"""		) else None      ,     adam_weight_decay=args.adam_weight_decay if hasattr(_lowerCAmelCase      ,     """adam_weight_decay"""		) else None      ,     adam_epsilon=args.adam_epsilon if hasattr(_lowerCAmelCase      ,     """adam_epsilon"""		) else None      ,     lr_scheduler=args.lr_scheduler if hasattr(_lowerCAmelCase      ,     """lr_scheduler"""		) else None      ,     lr_warmup_steps=args.lr_warmup_steps if hasattr(_lowerCAmelCase      ,     """lr_warmup_steps"""		) else None      ,     ema_inv_gamma=args.ema_inv_gamma if hasattr(_lowerCAmelCase      ,     """ema_inv_gamma"""		) else None      ,     ema_power=args.ema_power if hasattr(_lowerCAmelCase      ,     """ema_power"""		) else None      ,     ema_max_decay=args.ema_max_decay if hasattr(_lowerCAmelCase      ,     """ema_max_decay"""		) else None      ,     mixed_precision=args.mixed_precision      ,     )

    A :       List[Any]          =   os.path.join(args.output_dir      ,     """README.md"""		)
    model_card.save(_lowerCAmelCase		)




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase = None		)       ->  Union[str, Any]:



    """simple docstring"""






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




def 	__UpperCamelCase	(  _lowerCAmelCase = None      ,     _lowerCAmelCase = None		)       ->  None:



    """simple docstring"""






    if new_cache_dir is None:
        A :       Union[str, Any]          =   DIFFUSERS_CACHE
    if old_cache_dir is None:
        A :       Optional[Any]          =   old_diffusers_cache

    A :       Dict          =   Path(_lowerCAmelCase		).expanduser()
    A :       Union[str, Any]          =   Path(_lowerCAmelCase		).expanduser()
    for old_blob_path in old_cache_dir.glob("""**/blobs/*"""		):
        if old_blob_path.is_file() and not old_blob_path.is_symlink():
            A :       Union[str, Any]          =   new_cache_dir / old_blob_path.relative_to(_lowerCAmelCase		)
            new_blob_path.parent.mkdir(parents=_lowerCAmelCase      ,     exist_ok=_lowerCAmelCase		)
            os.replace(_lowerCAmelCase      ,     _lowerCAmelCase		)
            try:
                os.symlink(_lowerCAmelCase      ,     _lowerCAmelCase		)
            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).


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

if cache_version < 1:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						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:
               SCREAMING_SNAKE_CASE_:Tuple             =						"""\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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase = None		)       ->  str:



    """simple docstring"""






    if variant is not None:
        A :       Union[str, Any]          =   weights_name.split("""."""		)
        A :       List[Any]          =   splits[:-1] + [variant] + splits[-1:]
        A :       Any          =   """.""".join(_lowerCAmelCase		)

    return weights_name




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     *,
    _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=None      ,     )       ->  Optional[int]:



    """simple docstring"""






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







from ..utils import DummyObject, requires_backends






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)


