"""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_:					Optional[Any]     =					get_logger(__name__)


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







def 					__a   (		A = None      ):






     '''simple docstring'''



     lowercase__				 =							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 					__a   (		A    ,				A = None    ,				A = None      ):






     '''simple docstring'''



     if token is None:
          lowercase__				 =							HfFolder.get_token()
     if organization is None:
          lowercase__				 =							whoami(A      )["name"]
          return f'''{username}/{model_id}'''
     else:
          return f'''{organization}/{model_id}'''







def 					__a   (		A    ,				A      ):






     '''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

     lowercase__				 =							args.hub_token if hasattr(A    ,				"hub_token"      ) else None
     lowercase__				 =							get_full_repo_name(A    ,				token=A      )

     lowercase__				 =							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    ,				)

     lowercase__				 =							os.path.join(args.output_dir    ,				"README.md"      )
     model_card.save(A      )







def 					__a   (		A    ,				A = None      ):






     '''simple docstring'''



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







def 					__a   (		A = None    ,				A = None      ):






     '''simple docstring'''



     if new_cache_dir is None:
          lowercase__				 =							DIFFUSERS_CACHE
     if old_cache_dir is None:
          lowercase__				 =							old_diffusers_cache

     lowercase__				 =							Path(A      ).expanduser()
     lowercase__				 =							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():
               lowercase__				 =							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).


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

if cache_version < 1:
 lowerCAmelCase_:					Optional[int]     =					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_:					Optional[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 					__a   (		A    ,				A = None      ):






     '''simple docstring'''



     if variant is not None:
          lowercase__				 =							weights_name.split("."      )
          lowercase__				 =							splits[:-1] + [variant] + splits[-1:]
          lowercase__				 =							".".join(A      )

     return weights_name







def 					__a   (		A    ,				*,
    A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A=None    ,				):






     '''simple docstring'''



     lowercase__				 =							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
               lowercase__				 =							os.path.join(A    ,				A      )
               return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(A    ,				A    ,				A      )      ):
               lowercase__				 =							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:
                    lowercase__				 =							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
               lowercase__				 =							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 os
import sys

lowerCAmelCase_:					Any     =					os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase_:					Union[str, Any]     =					[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]







@add_start_docstrings(AutoConfig.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoConfig.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoTokenizer.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoTokenizer.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModel.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModel.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForCausalLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForMaskedLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForSequenceClassification.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForQuestionAnswering.from_pretrained(*A    ,				**A      )




