


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


_snake_case             =     get_logger(__name__)


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






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				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(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):
    ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()	)
  elif isinstance(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):
    ua += "; " + user_agent
  return ua






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if token is None:
    lowerCamelCase :					List[str]    =				HfFolder.get_token()
  if organization is None:
    lowerCamelCase :					Union[str, Any]    =				whoami(SCREAMING_SNAKE_CASE_	)["name"]
    return f"""{username}/{model_id}"""
  else:
    return f"""{organization}/{model_id}"""






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''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(SCREAMING_SNAKE_CASE_      ,						"local_rank"	) and args.local_rank not in [-1, 0]:
    return

  lowerCamelCase :					Optional[int]    =				args.hub_token if hasattr(SCREAMING_SNAKE_CASE_      ,						"hub_token"	) else None
  lowerCamelCase :					Dict    =				get_full_repo_name(SCREAMING_SNAKE_CASE_      ,						token=SCREAMING_SNAKE_CASE_	)

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

  lowerCamelCase :					Dict    =				os.path.join(args.output_dir      ,						"README.md"	)
  model_card.save(SCREAMING_SNAKE_CASE_	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if resolved_file is None or commit_hash is not None:
    return commit_hash
  lowerCamelCase :					str    =				str(Path(SCREAMING_SNAKE_CASE_	).as_posix()	)
  lowerCamelCase :					Optional[int]    =				re.search(r"snapshots/([^/]+)/"      ,						SCREAMING_SNAKE_CASE_	)
  if search is None:
    return None
  lowerCamelCase :					str    =				search.groups()[0]
  return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE_	) 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.
_snake_case             =     os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
_snake_case             =     os.path.join(hf_cache_home, '''diffusers''')






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = None      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if new_cache_dir is None:
    lowerCamelCase :					List[Any]    =				DIFFUSERS_CACHE
  if old_cache_dir is None:
    lowerCamelCase :					Any    =				old_diffusers_cache

  lowerCamelCase :					Union[str, Any]    =				Path(SCREAMING_SNAKE_CASE_	).expanduser()
  lowerCamelCase :					str    =				Path(SCREAMING_SNAKE_CASE_	).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 :					Dict    =				new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE_	)
      new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE_      ,						exist_ok=SCREAMING_SNAKE_CASE_	)
      os.replace(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
      try:
        os.symlink(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
      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).


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

if cache_version < 1:
   _snake_case             =     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:
         _snake_case             =     '''\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_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if variant is not None:
    lowerCamelCase :					Optional[int]    =				weights_name.split("."	)
    lowerCamelCase :					Optional[int]    =				splits[:-1] + [variant] + splits[-1:]
    lowerCamelCase :					Optional[Any]    =				".".join(SCREAMING_SNAKE_CASE_	)

  return weights_name







def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						*,
    SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=None      ,						):


  '''simple docstring'''

  lowerCamelCase :					str    =				str(SCREAMING_SNAKE_CASE_	)
  if os.path.isfile(SCREAMING_SNAKE_CASE_	):
    return pretrained_model_name_or_path
  elif os.path.isdir(SCREAMING_SNAKE_CASE_	):
    if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	):
      # Load from a PyTorch checkpoint
      lowerCamelCase :					int    =				os.path.join(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
      return model_file
    elif subfolder is not None and os.path.isfile(
        os.path.join(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	):
      lowerCamelCase :					str    =				os.path.join(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
      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(SCREAMING_SNAKE_CASE_	).base_version	) >= version.parse("0.20.0"	)
    ):
      try:
        lowerCamelCase :					List[str]    =				hf_hub_download(
            SCREAMING_SNAKE_CASE_      ,						filename=_add_variant(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						cache_dir=SCREAMING_SNAKE_CASE_      ,						force_download=SCREAMING_SNAKE_CASE_      ,						proxies=SCREAMING_SNAKE_CASE_      ,						resume_download=SCREAMING_SNAKE_CASE_      ,						local_files_only=SCREAMING_SNAKE_CASE_      ,						use_auth_token=SCREAMING_SNAKE_CASE_      ,						user_agent=SCREAMING_SNAKE_CASE_      ,						subfolder=SCREAMING_SNAKE_CASE_      ,						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."""      ,						SCREAMING_SNAKE_CASE_      ,						)
        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(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)} 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(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)}' so that the correct variant file can be added."""      ,						SCREAMING_SNAKE_CASE_      ,						)
    try:
      # 2. Load model file as usual
      lowerCamelCase :					Any    =				hf_hub_download(
          SCREAMING_SNAKE_CASE_      ,						filename=SCREAMING_SNAKE_CASE_      ,						cache_dir=SCREAMING_SNAKE_CASE_      ,						force_download=SCREAMING_SNAKE_CASE_      ,						proxies=SCREAMING_SNAKE_CASE_      ,						resume_download=SCREAMING_SNAKE_CASE_      ,						local_files_only=SCREAMING_SNAKE_CASE_      ,						use_auth_token=SCREAMING_SNAKE_CASE_      ,						user_agent=SCREAMING_SNAKE_CASE_      ,						subfolder=SCREAMING_SNAKE_CASE_      ,						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}"""	)







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for i in range(len(SCREAMING_SNAKE_CASE_	) - 1      ,						0      ,						-1	):
    lowerCamelCase :					Tuple    =				False

    for j in range(SCREAMING_SNAKE_CASE_      ,						0      ,						-1	):
      if unsorted[j] < unsorted[j - 1]:
        lowerCamelCase    ,	lowerCamelCase :					int    =				unsorted[j - 1], unsorted[j]
        lowerCamelCase :					Optional[int]    =				True

    for j in range(SCREAMING_SNAKE_CASE_	):
      if unsorted[j] > unsorted[j + 1]:
        lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				unsorted[j + 1], unsorted[j]
        lowerCamelCase :					Optional[Any]    =				True

    if not swapped:
      break
  return unsorted


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case             =     input('''Enter numbers separated by a comma:\n''').strip()
   _snake_case             =     [int(item) for item in user_input.split(''',''')]
   print(f'''{cocktail_shaker_sort(unsorted) = }''')



