"""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    :       Tuple					     =get_logger(__name__)


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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[Dict, str, None] = None		) ->    str:
      '''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(lowerCAmelCase__					,		lowerCAmelCase__		):
            ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items()		)
      elif isinstance(lowerCAmelCase__					,		lowerCAmelCase__		):
            ua += "; " + user_agent
      return ua


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Optional[str] = None					,		lowerCAmelCase__     :Optional[str] = None		) ->    int:
      '''simple docstring'''


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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :Optional[Any]		) ->    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(lowerCAmelCase__					,		"""local_rank"""		) and args.local_rank not in [-1, 0]:
            return

      lowercase														=	args.hub_token if hasattr(lowerCAmelCase__					,		"""hub_token"""		) else None
      lowercase														=	get_full_repo_name(lowerCAmelCase__					,		token=lowerCAmelCase__		)

      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=lowerCAmelCase__					,		model_name=lowerCAmelCase__					,		repo_name=lowerCAmelCase__					,		dataset_name=args.dataset_name if hasattr(lowerCAmelCase__					,		"""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(lowerCAmelCase__					,		"""gradient_accumulation_steps"""		) else None
          )					,		adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__					,		"""adam_beta1"""		) else None					,		adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__					,		"""adam_beta2"""		) else None					,		adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase__					,		"""adam_weight_decay"""		) else None					,		adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase__					,		"""adam_epsilon"""		) else None					,		lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase__					,		"""lr_scheduler"""		) else None					,		lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase__					,		"""lr_warmup_steps"""		) else None					,		ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase__					,		"""ema_inv_gamma"""		) else None					,		ema_power=args.ema_power if hasattr(lowerCAmelCase__					,		"""ema_power"""		) else None					,		ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase__					,		"""ema_max_decay"""		) else None					,		mixed_precision=args.mixed_precision					,		)

      lowercase														=	os.path.join(args.output_dir					,		"""README.md"""		)
      model_card.save(lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[str]					,		lowerCAmelCase__     :Optional[str] = None		) ->    List[Any]:
      '''simple docstring'''


      if resolved_file is None or commit_hash is not None:
            return commit_hash
      lowercase														=	str(Path(lowerCAmelCase__		).as_posix()		)
      lowercase														=	re.search(R"""snapshots/([^/]+)/"""					,		lowerCAmelCase__		)
      if search is None:
            return None
      lowercase														=	search.groups()[0]
      return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase__		) 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    :       int					     =os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
__lowerCAmelCase    :       List[str]					     =os.path.join(hf_cache_home, """diffusers""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[str] = None					,		lowerCAmelCase__     :Optional[str] = None		) ->    None:
      '''simple docstring'''


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

      lowercase														=	Path(lowerCAmelCase__		).expanduser()
      lowercase														=	Path(lowerCAmelCase__		).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(lowerCAmelCase__		)
                  new_blob_path.parent.mkdir(parents=lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
                  os.replace(lowerCAmelCase__					,		lowerCAmelCase__		)
                  try:
                        os.symlink(lowerCAmelCase__					,		lowerCAmelCase__		)
                  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    :       Union[str, Any]					     =os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
       __lowerCAmelCase    :       List[str]					     =0
else:
       with open(cache_version_file) as f:
              try:
                     __lowerCAmelCase    :       Any					     =int(f.read())
              except ValueError:
                     __lowerCAmelCase    :       str					     =0

if cache_version < 1:
       __lowerCAmelCase    :       List[str]					     =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    :       Union[str, 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 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Optional[str] = None		) ->    str:
      '''simple docstring'''


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

      return weights_name






def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		*,
    lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Any=None					,		) ->    Any:
      '''simple docstring'''


      lowercase														=	str(lowerCAmelCase__		)
      if os.path.isfile(lowerCAmelCase__		):
            return pretrained_model_name_or_path
      elif os.path.isdir(lowerCAmelCase__		):
            if os.path.isfile(os.path.join(lowerCAmelCase__					,		lowerCAmelCase__		)		):
                  # Load from a PyTorch checkpoint
                  lowercase														=	os.path.join(lowerCAmelCase__					,		lowerCAmelCase__		)
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__		)		):
                  lowercase														=	os.path.join(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__		)
                  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(lowerCAmelCase__		).base_version		) >= version.parse("""0.20.0"""		)
            ):
                  try:
                        lowercase														=	hf_hub_download(
                            lowerCAmelCase__					,		filename=_add_variant(lowerCAmelCase__					,		lowerCAmelCase__		)					,		cache_dir=lowerCAmelCase__					,		force_download=lowerCAmelCase__					,		proxies=lowerCAmelCase__					,		resume_download=lowerCAmelCase__					,		local_files_only=lowerCAmelCase__					,		use_auth_token=lowerCAmelCase__					,		user_agent=lowerCAmelCase__					,		subfolder=lowerCAmelCase__					,		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.'					,		lowerCAmelCase__					,		)
                        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(lowerCAmelCase__					,		lowerCAmelCase__		)} 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(lowerCAmelCase__					,		lowerCAmelCase__		)}\' so that the correct variant file can be added.'					,		lowerCAmelCase__					,		)
            try:
                  # 2. Load model file as usual
                  lowercase														=	hf_hub_download(
                      lowerCAmelCase__					,		filename=lowerCAmelCase__					,		cache_dir=lowerCAmelCase__					,		force_download=lowerCAmelCase__					,		proxies=lowerCAmelCase__					,		resume_download=lowerCAmelCase__					,		local_files_only=lowerCAmelCase__					,		use_auth_token=lowerCAmelCase__					,		user_agent=lowerCAmelCase__					,		subfolder=lowerCAmelCase__					,		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 queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer





class  _A						:

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            raise NotImplementedError()







      def 				A__       (						self						):







            """simple docstring"""



            raise NotImplementedError()






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	tokenizer
            lowercase														=	skip_prompt
            lowercase														=	decode_kwargs

            # variables used in the streaming process
            lowercase														=	[]
            lowercase														=	0
            lowercase														=	True

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if len(value.shape						) > 1 and value.shape[0] > 1:
                  raise ValueError("""TextStreamer only supports batch size 1"""						)
            elif len(value.shape						) > 1:
                  lowercase														=	value[0]

            if self.skip_prompt and self.next_tokens_are_prompt:
                  lowercase														=	False
                  return

            # Add the new token to the cache and decodes the entire thing.
            self.token_cache.extend(value.tolist()						)
            lowercase														=	self.tokenizer.decode(self.token_cache		,       **self.decode_kwargs						)

            # After the symbol for a new line, we flush the cache.
            if text.endswith("""\n"""						):
                  lowercase														=	text[self.print_len :]
                  lowercase														=	[]
                  lowercase														=	0
            # If the last token is a CJK character, we print the characters.
            elif len(__lowerCAmelCase						) > 0 and self._is_chinese_char(ord(text[-1]						)						):
                  lowercase														=	text[self.print_len :]
                  self.print_len += len(__lowerCAmelCase						)
            # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
            # which may change with the subsequent token -- there are probably smarter ways to do this!)
            else:
                  lowercase														=	text[self.print_len : text.rfind(""" """						) + 1]
                  self.print_len += len(__lowerCAmelCase						)

            self.on_finalized_text(__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            if len(self.token_cache						) > 0:
                  lowercase														=	self.tokenizer.decode(self.token_cache		,       **self.decode_kwargs						)
                  lowercase														=	text[self.print_len :]
                  lowercase														=	[]
                  lowercase														=	0
            else:
                  lowercase														=	""""""

            lowercase														=	True
            self.on_finalized_text(__lowerCAmelCase		,       stream_end=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False						):







            """simple docstring"""



            print(__lowerCAmelCase		,       flush=__lowerCAmelCase		,       end="""""" if not stream_end else None						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if (
                (cp >= 0X4_e00 and cp <= 0X9_fff)
                or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
                or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
                or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
                or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
                or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
                or (cp >= 0Xf_900 and cp <= 0Xf_aff)
                or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
            ):  #
                  return True

            return False






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False		,       __lowerCAmelCase = None		,       **__lowerCAmelCase						):







            """simple docstring"""



            super().__init__(__lowerCAmelCase		,       __lowerCAmelCase		,       **__lowerCAmelCase						)
            lowercase														=	Queue()
            lowercase														=	None
            lowercase														=	timeout

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False						):







            """simple docstring"""



            self.text_queue.put(__lowerCAmelCase		,       timeout=self.timeout						)
            if stream_end:
                  self.text_queue.put(self.stop_signal		,       timeout=self.timeout						)

      def __iter__(						self						):







            """simple docstring"""



            return self







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.text_queue.get(timeout=self.timeout						)
            if value == self.stop_signal:
                  raise StopIteration()
            else:
                  return value



