
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


UpperCamelCase_   	=	get_logger(__name__)


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any] = None  ):






 """simple docstring"""


 __lowerCAmelCase         =							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 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     int = None			,							UpperCamelCase:     List[Any] = None  ):






 """simple docstring"""


 if token is None:
  __lowerCAmelCase         =							HfFolder.get_token()
 if organization is None:
  __lowerCAmelCase         =							whoami(A_  )["name"]
  return F"{username}/{model_id}"
 else:
  return F"{organization}/{model_id}"






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     int  ):






 """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

 __lowerCAmelCase         =							args.hub_token if hasattr(A_			,							"hub_token"  ) else None
 __lowerCAmelCase         =							get_full_repo_name(A_			,							token=A_  )

 __lowerCAmelCase         =							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			,							)

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






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[str] = None  ):






 """simple docstring"""


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int] = None			,							UpperCamelCase:     Dict = None  ):






 """simple docstring"""


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

 __lowerCAmelCase         =							Path(A_  ).expanduser()
 __lowerCAmelCase         =							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():
   __lowerCAmelCase         =							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).


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

if cache_version < 1:
     UpperCamelCase_   	=	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:
               UpperCamelCase_   	=	'\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   (	UpperCamelCase:     str			,							UpperCamelCase:     Tuple = None  ):






 """simple docstring"""


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

 return weights_name







def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							*,
    UpperCamelCase:     Tuple			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Any			,							UpperCamelCase:     str			,							UpperCamelCase:     Any			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Union[str, Any]=None			,							):






 """simple docstring"""


 __lowerCAmelCase         =							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
   __lowerCAmelCase         =							os.path.join(A_			,							A_  )
   return model_file
  elif subfolder is not None and os.path.isfile(
      os.path.join(A_			,							A_			,							A_  )  ):
   __lowerCAmelCase         =							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:
    __lowerCAmelCase         =							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
   __lowerCAmelCase         =							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}"  )








# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)






