


'''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        a__     (					UpperCamelCase_						:      Union[Dict, str, None] = None						):
   UpperCAmelCase__	:List[str]      =      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(UpperCamelCase_,   UpperCamelCase_						):
      ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items()						)
   elif isinstance(UpperCamelCase_,   UpperCamelCase_						):
      ua += "; " + user_agent
   return ua


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


def        a__     (					UpperCamelCase_						:      List[str],   UpperCamelCase_						:      Dict						):
   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(UpperCamelCase_,   '''local_rank'''						) and args.local_rank not in [-1, 0]:
      return

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

   UpperCAmelCase__	:List[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=UpperCamelCase_,   model_name=UpperCamelCase_,   repo_name=UpperCamelCase_,   dataset_name=args.dataset_name if hasattr(UpperCamelCase_,   '''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(UpperCamelCase_,   '''gradient_accumulation_steps'''						) else None
       ),   adam_betaa=args.adam_betaa if hasattr(UpperCamelCase_,   '''adam_beta1'''						) else None,   adam_betaa=args.adam_betaa if hasattr(UpperCamelCase_,   '''adam_beta2'''						) else None,   adam_weight_decay=args.adam_weight_decay if hasattr(UpperCamelCase_,   '''adam_weight_decay'''						) else None,   adam_epsilon=args.adam_epsilon if hasattr(UpperCamelCase_,   '''adam_epsilon'''						) else None,   lr_scheduler=args.lr_scheduler if hasattr(UpperCamelCase_,   '''lr_scheduler'''						) else None,   lr_warmup_steps=args.lr_warmup_steps if hasattr(UpperCamelCase_,   '''lr_warmup_steps'''						) else None,   ema_inv_gamma=args.ema_inv_gamma if hasattr(UpperCamelCase_,   '''ema_inv_gamma'''						) else None,   ema_power=args.ema_power if hasattr(UpperCamelCase_,   '''ema_power'''						) else None,   ema_max_decay=args.ema_max_decay if hasattr(UpperCamelCase_,   '''ema_max_decay'''						) else None,   mixed_precision=args.mixed_precision,   )

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


def        a__     (					UpperCamelCase_						:      Optional[str],   UpperCamelCase_						:      Optional[str] = None						):
   if resolved_file is None or commit_hash is not None:
      return commit_hash
   UpperCAmelCase__	:Dict      =      str(Path(UpperCamelCase_						).as_posix()						)
   UpperCAmelCase__	:str      =      re.search(r'''snapshots/([^/]+)/''',   UpperCamelCase_						)
   if search is None:
      return None
   UpperCAmelCase__	:Optional[int]      =      search.groups()[0]
   return commit_hash if REGEX_COMMIT_HASH.match(UpperCamelCase_						) 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        a__     (					UpperCamelCase_						:      Optional[str] = None,   UpperCamelCase_						:      Optional[str] = None						):
   if new_cache_dir is None:
      UpperCAmelCase__	:Union[str, Any]      =      DIFFUSERS_CACHE
   if old_cache_dir is None:
      UpperCAmelCase__	:str      =      old_diffusers_cache

   UpperCAmelCase__	:List[str]      =      Path(UpperCamelCase_						).expanduser()
   UpperCAmelCase__	:Tuple      =      Path(UpperCamelCase_						).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__	:int      =      new_cache_dir / old_blob_path.relative_to(UpperCamelCase_						)
         new_blob_path.parent.mkdir(parents=UpperCamelCase_,   exist_ok=UpperCamelCase_						)
         os.replace(UpperCamelCase_,   UpperCamelCase_						)
         try:
            os.symlink(UpperCamelCase_,   UpperCamelCase_						)
         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        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      Optional[str] = None						):
   if variant is not None:
      UpperCAmelCase__	:Union[str, Any]      =      weights_name.split('''.'''						)
      UpperCAmelCase__	:Tuple      =      splits[:-1] + [variant] + splits[-1:]
      UpperCAmelCase__	:int      =      '''.'''.join(UpperCamelCase_						)

   return weights_name


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




class 				UpperCAmelCase			(			metaclass=_snake_case						):
 UpperCAmelCase				=					["note_seq"]


 def __init__(      self							:	List[str]			,       *__lowerCamelCase							:	int			,       **__lowerCamelCase							:	Optional[int] ):
    requires_backends(self			,       ['''note_seq'''] )


 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	Optional[Any]			,       *__lowerCamelCase							:	Dict			,       **__lowerCamelCase							:	Optional[Any] ):
    requires_backends(cls			,       ['''note_seq'''] )





 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	str			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Optional[Any] ):
    requires_backends(cls			,       ['''note_seq'''] )



