

"""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


_A							=  get_logger(__name__)


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




def 					a__						(						lowerCAmelCase = None    )	-> str:
      UpperCAmelCase__			:							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(lowerCAmelCase			,	lowerCAmelCase    ):
            ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()    )
      elif isinstance(lowerCAmelCase			,	lowerCAmelCase    ):
            ua += "; " + user_agent
      return ua




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase = None			,	lowerCAmelCase = None    )	-> List[Any]:
      if token is None:
            UpperCAmelCase__			:							List[str]							=    HfFolder.get_token()
      if organization is None:
            UpperCAmelCase__			:							str							=    whoami(lowerCAmelCase    )["""name"""]
            return F"""{username}/{model_id}"""
      else:
            return F"""{organization}/{model_id}"""




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:
      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

      UpperCAmelCase__			:							Tuple							=    args.hub_token if hasattr(lowerCAmelCase			,	"""hub_token"""    ) else None
      UpperCAmelCase__			:							int							=    get_full_repo_name(lowerCAmelCase			,	token=lowerCAmelCase    )

      UpperCAmelCase__			:							Optional[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=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			,	)

      UpperCAmelCase__			:							Optional[int]							=    os.path.join(args.output_dir			,	"""README.md"""    )
      model_card.save(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase = None    )	-> Union[str, Any]:
      if resolved_file is None or commit_hash is not None:
            return commit_hash
      UpperCAmelCase__			:							List[str]							=    str(Path(lowerCAmelCase    ).as_posix()    )
      UpperCAmelCase__			:							Tuple							=    re.search(R"""snapshots/([^/]+)/"""			,	lowerCAmelCase    )
      if search is None:
            return None
      UpperCAmelCase__			:							Tuple							=    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.
_A							=  os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
_A							=  os.path.join(hf_cache_home, """diffusers""")




def 					a__						(						lowerCAmelCase = None			,	lowerCAmelCase = None    )	-> None:
      if new_cache_dir is None:
            UpperCAmelCase__			:							Union[str, Any]							=    DIFFUSERS_CACHE
      if old_cache_dir is None:
            UpperCAmelCase__			:							List[str]							=    old_diffusers_cache

      UpperCAmelCase__			:							int							=    Path(lowerCAmelCase    ).expanduser()
      UpperCAmelCase__			:							Tuple							=    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():
                  UpperCAmelCase__			:							Any							=    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).


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

if cache_version < 1:
    _A							=  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:
            _A							=  """\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__						(						lowerCAmelCase			,	lowerCAmelCase = None    )	-> str:
      if variant is not None:
            UpperCAmelCase__			:							List[Any]							=    weights_name.split("""."""    )
            UpperCAmelCase__			:							Dict							=    splits[:-1] + [variant] + splits[-1:]
            UpperCAmelCase__			:							int							=    """.""".join(lowerCAmelCase    )

      return weights_name





def 					a__						(						lowerCAmelCase			,	*,
    lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase=None			,	)	-> Optional[int]:
      UpperCAmelCase__			:							List[str]							=    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
                  UpperCAmelCase__			:							List[Any]							=    os.path.join(lowerCAmelCase			,	lowerCAmelCase    )
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )    ):
                  UpperCAmelCase__			:							Optional[int]							=    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:
                        UpperCAmelCase__			:							str							=    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
                  UpperCAmelCase__			:							int							=    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"""
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A							=  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CpmAntForCausalLM""",
        """CpmAntModel""",
        """CpmAntPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

    _A							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

