
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


lowercase_      							=       get_logger(__name__)


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


def 				a__ (				snake_case = None				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Union[str, 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(__snake_case    ,       __snake_case				):
    ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items()				)
  elif isinstance(__snake_case    ,       __snake_case				):
    ua += "; " + user_agent
  return ua


def 				a__ (				snake_case    ,       snake_case = None    ,       snake_case = None				):





  """simple docstring"""
  if token is None:
    __SCREAMING_SNAKE_CASE	:     List[str] =						HfFolder.get_token()
  if organization is None:
    __SCREAMING_SNAKE_CASE	:     Any =						whoami(__snake_case				)["name"]
    return F'''{username}/{model_id}'''
  else:
    return F'''{organization}/{model_id}'''


def 				a__ (				snake_case    ,       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(__snake_case    ,       '''local_rank'''				) and args.local_rank not in [-1, 0]:
    return

  __SCREAMING_SNAKE_CASE	:     List[Any] =						args.hub_token if hasattr(__snake_case    ,       '''hub_token'''				) else None
  __SCREAMING_SNAKE_CASE	:     List[str] =						get_full_repo_name(__snake_case    ,       token=__snake_case				)

  __SCREAMING_SNAKE_CASE	:     Tuple =						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=__snake_case    ,       model_name=__snake_case    ,       repo_name=__snake_case    ,       dataset_name=args.dataset_name if hasattr(__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(__snake_case    ,       '''gradient_accumulation_steps'''				) else None
      )    ,       adam_betaa=args.adam_betaa if hasattr(__snake_case    ,       '''adam_beta1'''				) else None    ,       adam_betaa=args.adam_betaa if hasattr(__snake_case    ,       '''adam_beta2'''				) else None    ,       adam_weight_decay=args.adam_weight_decay if hasattr(__snake_case    ,       '''adam_weight_decay'''				) else None    ,       adam_epsilon=args.adam_epsilon if hasattr(__snake_case    ,       '''adam_epsilon'''				) else None    ,       lr_scheduler=args.lr_scheduler if hasattr(__snake_case    ,       '''lr_scheduler'''				) else None    ,       lr_warmup_steps=args.lr_warmup_steps if hasattr(__snake_case    ,       '''lr_warmup_steps'''				) else None    ,       ema_inv_gamma=args.ema_inv_gamma if hasattr(__snake_case    ,       '''ema_inv_gamma'''				) else None    ,       ema_power=args.ema_power if hasattr(__snake_case    ,       '''ema_power'''				) else None    ,       ema_max_decay=args.ema_max_decay if hasattr(__snake_case    ,       '''ema_max_decay'''				) else None    ,       mixed_precision=args.mixed_precision    ,       )

  __SCREAMING_SNAKE_CASE	:     List[str] =						os.path.join(args.output_dir    ,       '''README.md'''				)
  model_card.save(__snake_case				)


def 				a__ (				snake_case    ,       snake_case = None				):





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


def 				a__ (				snake_case = None    ,       snake_case = None				):





  """simple docstring"""
  if new_cache_dir is None:
    __SCREAMING_SNAKE_CASE	:     Tuple =						DIFFUSERS_CACHE
  if old_cache_dir is None:
    __SCREAMING_SNAKE_CASE	:     Optional[int] =						old_diffusers_cache

  __SCREAMING_SNAKE_CASE	:     Dict =						Path(__snake_case				).expanduser()
  __SCREAMING_SNAKE_CASE	:     Dict =						Path(__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():
      __SCREAMING_SNAKE_CASE	:     Tuple =						new_cache_dir / old_blob_path.relative_to(__snake_case				)
      new_blob_path.parent.mkdir(parents=__snake_case    ,       exist_ok=__snake_case				)
      os.replace(__snake_case    ,       __snake_case				)
      try:
        os.symlink(__snake_case    ,       __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).


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

if cache_version < 1:
   lowercase_      							=       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:
         lowercase_      							=       '\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__ (				snake_case    ,       snake_case = None				):





  """simple docstring"""
  if variant is not None:
    __SCREAMING_SNAKE_CASE	:     Tuple =						weights_name.split('''.'''				)
    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						splits[:-1] + [variant] + splits[-1:]
    __SCREAMING_SNAKE_CASE	:     Dict =						".".join(__snake_case				)

  return weights_name


def 				a__ (				snake_case    ,       *,
    snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case=None    ,       ):





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





"""simple docstring"""






from __future__ import annotations


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   list:
			__lowerCAmelCase      :  Dict												=       []
			__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       input_list[low:mid], input_list[mid : high + 1]
			while left and right:
						result.append((left if left[0] <= right[0] else right).pop(0	)	)
			__lowerCAmelCase      :  int												=       result + left + right
			return input_list


def 			_lowercase				(  __snake_case	)						->   list:
			if len(__snake_case	) <= 1:
						return input_list
			__lowerCAmelCase      :  int												=       list(__snake_case	)

			# iteration for two-way merging
			__lowerCAmelCase      :  Optional[int]												=       2
			while p <= len(__snake_case	):
						# getting low, high and middle value for merge-sort of single list
						for i in range(0					,len(__snake_case	)					,__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       i
									__lowerCAmelCase      :  Tuple												=       i + p - 1
									__lowerCAmelCase      :  Optional[Any]												=       (low + high + 1) // 2
									__lowerCAmelCase      :  Any												=       merge(__snake_case					,__snake_case					,__snake_case					,__snake_case	)
						# final merge of last two parts
						if p * 2 >= len(__snake_case	):
									__lowerCAmelCase      :  Optional[Any]												=       i
									__lowerCAmelCase      :  Union[str, Any]												=       merge(__snake_case					,0					,__snake_case					,len(__snake_case	) - 1	)
									break
						p *= 2

			return input_list


if __name__ == "__main__":
	__snake_case							:   Union[str, Any]      =    input('Enter numbers separated by a comma:\n').strip()
	if user_input == "":
		__snake_case							:   Optional[int]      =    []
	else:
		__snake_case							:   int      =    [int(item.strip()) for item in user_input.split(',')]
	print(iter_merge_sort(unsorted))