



"""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


lowercase__    :  int							    =	get_logger(__name__)


lowercase__    :  List[Any]							    =	Path(__file__).parent / '''model_card_template.md'''
lowercase__    :  List[Any]							    =	uuida().hex
lowercase__    :  Optional[Any]							    =	os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowercase__    :  Tuple							    =	os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowercase__    :  Optional[int]							    =	HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''




def __lowercase			(							_a = None    ):
    snake_case_ :     Tuple				=	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 __lowercase			(							_a		,			_a = None		,			_a = None    ):
    if token is None:
        snake_case_ :     Optional[Any]				=	HfFolder.get_token()
    if organization is None:
        snake_case_ :     List[Any]				=	whoami(_a    )['''name''']
        return f"{username}/{model_id}"
    else:
        return f"{organization}/{model_id}"




def __lowercase			(							_a		,			_a    ):
    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

    snake_case_ :     Optional[int]				=	args.hub_token if hasattr(_a		,			'''hub_token'''    ) else None
    snake_case_ :     List[str]				=	get_full_repo_name(_a		,			token=_a    )

    snake_case_ :     Tuple				=	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		,			)

    snake_case_ :     List[str]				=	os.path.join(args.output_dir		,			'''README.md'''    )
    model_card.save(_a    )




def __lowercase			(							_a		,			_a = None    ):
    if resolved_file is None or commit_hash is not None:
        return commit_hash
    snake_case_ :     Any				=	str(Path(_a    ).as_posix()    )
    snake_case_ :     List[str]				=	re.search(r'''snapshots/([^/]+)/'''		,			_a    )
    if search is None:
        return None
    snake_case_ :     int				=	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.
lowercase__    :  Optional[int]							    =	os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowercase__    :  Optional[int]							    =	os.path.join(hf_cache_home, '''diffusers''')




def __lowercase			(							_a = None		,			_a = None    ):
    if new_cache_dir is None:
        snake_case_ :     List[Any]				=	DIFFUSERS_CACHE
    if old_cache_dir is None:
        snake_case_ :     Optional[Any]				=	old_diffusers_cache

    snake_case_ :     Optional[Any]				=	Path(_a    ).expanduser()
    snake_case_ :     Union[str, Any]				=	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():
            snake_case_ :     Union[str, Any]				=	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).


lowercase__    :  str							    =	os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
     lowercase__    :  Any							    =	0
else:
     with open(cache_version_file) as f:
          try:
               lowercase__    :  Tuple							    =	int(f.read())
          except ValueError:
               lowercase__    :  Any							    =	0

if cache_version < 1:
     lowercase__    :  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:
               lowercase__    :  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			(							_a		,			_a = None    ):
    if variant is not None:
        snake_case_ :     str				=	weights_name.split('''.'''    )
        snake_case_ :     Any				=	splits[:-1] + [variant] + splits[-1:]
        snake_case_ :     Optional[Any]				=	'''.'''.join(_a    )

    return weights_name






def __lowercase			(							_a		,			*,
    _a		,			_a		,			_a		,			_a		,			_a		,			_a		,			_a		,			_a		,			_a		,			_a		,			_a=None		,			):
    snake_case_ :     str				=	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
            snake_case_ :     List[Any]				=	os.path.join(_a		,			_a    )
            return model_file
        elif subfolder is not None and os.path.isfile(
            os.path.join(_a		,			_a		,			_a    )    ):
            snake_case_ :     Union[str, Any]				=	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:
                snake_case_ :     Any				=	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
            snake_case_ :     Optional[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"""

import numpy as np




def __lowercase			(							_a    ):
    return (2 / (1 + np.exp(-2 * vector    ))) - 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





