


"""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    =				get_logger(__name__)


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


def 					lowercase      (    A_ = None	)->				Tuple:
  '''simple docstring'''







  a						:		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(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	)->				Optional[int]:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       A_	)->				str:
  '''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

  a						:		Dict											=      args.hub_token if hasattr(A_  ,       "hub_token"	) else None
  a						:		List[Any]											=      get_full_repo_name(A_  ,       token=A_	)

  a						:		str											=      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  ,       )

  a						:		str											=      os.path.join(args.output_dir  ,       "README.md"	)
  model_card.save(A_	)


def 					lowercase      (    A_  ,       A_ = None	)->				Optional[int]:
  '''simple docstring'''







  if resolved_file is None or commit_hash is not None:
    return commit_hash
  a						:		Optional[int]											=      str(Path(A_	).as_posix()	)
  a						:		Union[str, Any]											=      re.search(R"snapshots/([^/]+)/"  ,       A_	)
  if search is None:
    return None
  a						:		str											=      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    =				os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
__lowercase    =				os.path.join(hf_cache_home, """diffusers""")


def 					lowercase      (    A_ = None  ,       A_ = None	)->				List[Any]:
  '''simple docstring'''







  if new_cache_dir is None:
    a						:		Optional[Any]											=      DIFFUSERS_CACHE
  if old_cache_dir is None:
    a						:		str											=      old_diffusers_cache

  a						:		List[str]											=      Path(A_	).expanduser()
  a						:		Optional[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():
      a						:		List[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).


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

if cache_version < 1:
 __lowercase    =				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    =				"""\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	)->				List[Any]:
  '''simple docstring'''







  if variant is not None:
    a						:		Optional[Any]											=      weights_name.split("."	)
    a						:		Any											=      splits[:-1] + [variant] + splits[-1:]
    a						:		Dict											=      ".".join(A_	)

  return weights_name







def 					lowercase      (    A_  ,       *,
    A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_=None  ,       )->				Any:
  '''simple docstring'''







  a						:		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
      a						:		Tuple											=      os.path.join(A_  ,       A_	)
      return model_file
    elif subfolder is not None and os.path.isfile(
        os.path.join(A_  ,       A_  ,       A_	)	):
      a						:		List[str]											=      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:
        a						:		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
      a						:		Dict											=      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	"""gpt_neox"""






    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple=50432    ,			__UpperCAmelCase    :   str=6144    ,			__UpperCAmelCase    :   Any=44    ,			__UpperCAmelCase    :   Union[str, Any]=64    ,			__UpperCAmelCase    :   Dict=24576    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   List[Any]=0.25    ,			__UpperCAmelCase    :   Optional[Any]=10000    ,			__UpperCAmelCase    :   Tuple=0.0    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[Any]=2048    ,			__UpperCAmelCase    :   Optional[Any]=0.02    ,			__UpperCAmelCase    :   Optional[Any]=1e-5    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   Union[str, Any]=0    ,			__UpperCAmelCase    :   Union[str, Any]=2    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   List[Any]=True    ,			__UpperCAmelCase    :   int=None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      super().__init__(bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)
      a						:		List[Any]											=      vocab_size
      a						:		Optional[int]											=      max_position_embeddings
      a						:		List[Any]											=      hidden_size
      a						:		Union[str, Any]											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Optional[Any]											=      hidden_act
      a						:		Dict											=      rotary_pct
      a						:		Any											=      rotary_emb_base
      a						:		Dict											=      attention_dropout
      a						:		List[str]											=      hidden_dropout
      a						:		List[str]											=      classifier_dropout
      a						:		Any											=      initializer_range
      a						:		Union[str, Any]											=      layer_norm_eps
      a						:		int											=      use_cache
      a						:		int											=      tie_word_embeddings
      a						:		str											=      use_parallel_residual
      a						:		Union[str, Any]											=      rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            "The hidden size is not divisble by the number of attention heads! Make sure to update them!")






    def      __snake_case							( self    :   Any):
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,			__UpperCAmelCase) or len(self.rope_scaling) != 2:
        raise ValueError(
            "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
            f'''got {self.rope_scaling}''')
      a						:		str											=      self.rope_scaling.get("type"    ,			__UpperCAmelCase)
      a						:		List[str]											=      self.rope_scaling.get("factor"    ,			__UpperCAmelCase)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''')
      if rope_scaling_factor is None or not isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) or rope_scaling_factor <= 1.0:
        raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''')






