'''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__  :						Union[str, Any]					=      get_logger(__name__)


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







def __UpperCamelCase( _A       :					Union[Dict, str, None] = None     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[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(_A	,      _A     ):
      ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items()     )
   elif isinstance(_A	,      _A     ):
      ua += "; " + user_agent
   return ua







def __UpperCamelCase( _A       :					str	,      _A       :					Optional[str] = None	,      _A       :					Optional[str] = None     ):
   '''simple docstring'''
   if token is None:
      UpperCAmelCase__				:					Union[str, Any] 					=	HfFolder.get_token()
   if organization is None:
      UpperCAmelCase__				:					int 					=	whoami(_A     )['''name''']
      return F'''{username}/{model_id}'''
   else:
      return F'''{organization}/{model_id}'''







def __UpperCamelCase( _A       :					Dict	,      _A       :					Optional[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(_A	,      '''local_rank'''     ) and args.local_rank not in [-1, 0]:
      return

   UpperCAmelCase__				:					Union[str, Any] 					=	args.hub_token if hasattr(_A	,      '''hub_token'''     ) else None
   UpperCAmelCase__				:					List[Any] 					=	get_full_repo_name(_A	,      token=_A     )

   UpperCAmelCase__				:					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	,      )

   UpperCAmelCase__				:					int 					=	os.path.join(args.output_dir	,      '''README.md'''     )
   model_card.save(_A     )







def __UpperCamelCase( _A       :					Optional[str]	,      _A       :					Optional[str] = None     ):
   '''simple docstring'''
   if resolved_file is None or commit_hash is not None:
      return commit_hash
   UpperCAmelCase__				:					List[Any] 					=	str(Path(_A     ).as_posix()     )
   UpperCAmelCase__				:					Union[str, Any] 					=	re.search(R'''snapshots/([^/]+)/'''	,      _A     )
   if search is None:
      return None
   UpperCAmelCase__				:					Tuple 					=	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__  :						Optional[Any]					=      os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
UpperCamelCase__  :						int					=      os.path.join(hf_cache_home, 'diffusers')







def __UpperCamelCase( _A       :					Optional[str] = None	,      _A       :					Optional[str] = None     ):
   '''simple docstring'''
   if new_cache_dir is None:
      UpperCAmelCase__				:					Dict 					=	DIFFUSERS_CACHE
   if old_cache_dir is None:
      UpperCAmelCase__				:					List[str] 					=	old_diffusers_cache

   UpperCAmelCase__				:					List[str] 					=	Path(_A     ).expanduser()
   UpperCAmelCase__				:					str 					=	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__				:					str 					=	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__  :						Optional[Any]					=      os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
  UpperCamelCase__  :						Tuple					=      0
else:
  with open(cache_version_file) as f:
    try:
      UpperCamelCase__  :						Optional[int]					=      int(f.read())
    except ValueError:
      UpperCamelCase__  :						Any					=      0

if cache_version < 1:
  UpperCamelCase__  :						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:
      UpperCamelCase__  :						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( _A       :					str	,      _A       :					Optional[str] = None     ):
   '''simple docstring'''
   if variant is not None:
      UpperCAmelCase__				:					Dict 					=	weights_name.split('''.'''     )
      UpperCAmelCase__				:					int 					=	splits[:-1] + [variant] + splits[-1:]
      UpperCAmelCase__				:					Dict 					=	'''.'''.join(_A     )

   return weights_name







def __UpperCamelCase( _A       :					Tuple	,      *,
    _A       :					List[Any]	,      _A       :					Dict	,      _A       :					int	,      _A       :					Union[str, Any]	,      _A       :					List[Any]	,      _A       :					int	,      _A       :					str	,      _A       :					int	,      _A       :					List[Any]	,      _A       :					Tuple	,      _A       :					Any=None	,      ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[Any] 					=	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__				:					Optional[int] 					=	os.path.join(_A	,      _A     )
         return model_file
      elif subfolder is not None and os.path.isfile(
          os.path.join(_A	,      _A	,      _A     )     ):
         UpperCAmelCase__				:					int 					=	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[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
         UpperCAmelCase__				:					int 					=	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 math import factorial, radians







def __UpperCamelCase( _A       :					float	,      _A       :					int = 18	,      _A       :					int = 10     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

   # Converting from degrees to radians
   UpperCAmelCase__				:					Dict 					=	radians(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	angle_in_radians
   UpperCAmelCase__				:					List[str] 					=	3
   UpperCAmelCase__				:					int 					=	-1

   for _ in range(_A     ):
      result += (b * (angle_in_radians**a)) / factorial(_A     )

      UpperCAmelCase__				:					List[str] 					=	-b  # One positive term and the next will be negative and so on...
      a += 2  # Increased by 2 for every term.

   return round(_A	,      _A     )


if __name__ == "__main__":
  __import__('doctest').testmod()




