




"""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


lowerCAmelCase_						=			get_logger(__name__)


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






def  snake_case(    __magic_name__ = None   )			->							str:





     '''simple docstring'''


     lowercase :       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(__magic_name__			,		__magic_name__   ):
          ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()   )
     elif isinstance(__magic_name__			,		__magic_name__   ):
          ua += "; " + user_agent
     return ua






def  snake_case(    __magic_name__			,		__magic_name__ = None			,		__magic_name__ = None   )			->							Optional[Any]:





     '''simple docstring'''


     if token is None:
          lowercase :       int       =			HfFolder.get_token()
     if organization is None:
          lowercase :       List[str]       =			whoami(__magic_name__   )['''name''']
          return F"""{username}/{model_id}"""
     else:
          return F"""{organization}/{model_id}"""






def  snake_case(    __magic_name__			,		__magic_name__   )			->							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(__magic_name__			,		'''local_rank'''   ) and args.local_rank not in [-1, 0]:
          return

     lowercase :       Optional[Any]       =			args.hub_token if hasattr(__magic_name__			,		'''hub_token'''   ) else None
     lowercase :       int       =			get_full_repo_name(__magic_name__			,		token=__magic_name__   )

     lowercase :       Dict       =			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=__magic_name__			,		model_name=__magic_name__			,		repo_name=__magic_name__			,		dataset_name=args.dataset_name if hasattr(__magic_name__			,		'''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(__magic_name__			,		'''gradient_accumulation_steps'''   ) else None
         )			,		adam_betaa=args.adam_betaa if hasattr(__magic_name__			,		'''adam_beta1'''   ) else None			,		adam_betaa=args.adam_betaa if hasattr(__magic_name__			,		'''adam_beta2'''   ) else None			,		adam_weight_decay=args.adam_weight_decay if hasattr(__magic_name__			,		'''adam_weight_decay'''   ) else None			,		adam_epsilon=args.adam_epsilon if hasattr(__magic_name__			,		'''adam_epsilon'''   ) else None			,		lr_scheduler=args.lr_scheduler if hasattr(__magic_name__			,		'''lr_scheduler'''   ) else None			,		lr_warmup_steps=args.lr_warmup_steps if hasattr(__magic_name__			,		'''lr_warmup_steps'''   ) else None			,		ema_inv_gamma=args.ema_inv_gamma if hasattr(__magic_name__			,		'''ema_inv_gamma'''   ) else None			,		ema_power=args.ema_power if hasattr(__magic_name__			,		'''ema_power'''   ) else None			,		ema_max_decay=args.ema_max_decay if hasattr(__magic_name__			,		'''ema_max_decay'''   ) else None			,		mixed_precision=args.mixed_precision			,		)

     lowercase :       Any       =			os.path.join(args.output_dir			,		'''README.md'''   )
     model_card.save(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__ = None   )			->							int:





     '''simple docstring'''


     if resolved_file is None or commit_hash is not None:
          return commit_hash
     lowercase :       Dict       =			str(Path(__magic_name__   ).as_posix()   )
     lowercase :       Any       =			re.search(r'''snapshots/([^/]+)/'''			,		__magic_name__   )
     if search is None:
          return None
     lowercase :       List[Any]       =			search.groups()[0]
     return commit_hash if REGEX_COMMIT_HASH.match(__magic_name__   ) 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.
lowerCAmelCase_						=			os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
lowerCAmelCase_						=			os.path.join(hf_cache_home, 'diffusers')






def  snake_case(    __magic_name__ = None			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     if new_cache_dir is None:
          lowercase :       str       =			DIFFUSERS_CACHE
     if old_cache_dir is None:
          lowercase :       List[str]       =			old_diffusers_cache

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


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

if cache_version < 1:
 lowerCAmelCase_						=			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:
   lowerCAmelCase_						=			'\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(    __magic_name__			,		__magic_name__ = None   )			->							str:





     '''simple docstring'''


     if variant is not None:
          lowercase :       List[str]       =			weights_name.split('''.'''   )
          lowercase :       Any       =			splits[:-1] + [variant] + splits[-1:]
          lowercase :       Tuple       =			'''.'''.join(__magic_name__   )

     return weights_name






def  snake_case(    __magic_name__			,		*,
    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=None			,		)			->							Dict:





     '''simple docstring'''


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

import datasets


lowerCAmelCase_						=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCAmelCase_						=			'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCAmelCase_						=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION				)
class 						_A (  datasets.Metric				):






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION				,						citation=_CITATION				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(self._get_feature_types()				)				,						reference_urls=[
               '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
           ]				,						)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       if self.config_name == "multilist":
            return {
                "predictions": datasets.Sequence(datasets.Value('''float'''				)				),
                "references": datasets.Sequence(datasets.Value('''float'''				)				),
            }
       else:
            return {
                "predictions": datasets.Value('''float'''				),
                "references": datasets.Value('''float'''				),
            }






  def  __a      (  self		:     Any				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any=None				,						_A		:     Any="uniform_average"				,						_A		:     Optional[Any]=True				)		->   Dict:






       """simple docstring"""







       lowercase :       Any       =			mean_squared_error(
           _A				,						_A				,						sample_weight=_A				,						multioutput=_A				,						squared=_A				)

       return {"mse": mse}