"""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


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    (	lowercase = None						):







  """simple docstring"""
  _UpperCAmelCase         =					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(lowercase      ,lowercase						):
    ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()						)
  elif isinstance(lowercase      ,lowercase						):
    ua += "; " + user_agent
  return ua






def 			__UpperCAmelCase    (	lowercase      ,lowercase = None      ,lowercase = None						):







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






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







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

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

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

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






def 			__UpperCAmelCase    (	lowercase      ,lowercase = None						):







  """simple docstring"""
  if resolved_file is None or commit_hash is not None:
    return commit_hash
  _UpperCAmelCase         =					str(Path(lowercase						).as_posix()						)
  _UpperCAmelCase         =					re.search(R"""snapshots/([^/]+)/"""      ,lowercase						)
  if search is None:
    return None
  _UpperCAmelCase         =					search.groups()[0]
  return commit_hash if REGEX_COMMIT_HASH.match(lowercase						) 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    (	lowercase = None      ,lowercase = None						):







  """simple docstring"""
  if new_cache_dir is None:
    _UpperCAmelCase         =					DIFFUSERS_CACHE
  if old_cache_dir is None:
    _UpperCAmelCase         =					old_diffusers_cache

  _UpperCAmelCase         =					Path(lowercase						).expanduser()
  _UpperCAmelCase         =					Path(lowercase						).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         =					new_cache_dir / old_blob_path.relative_to(lowercase						)
      new_blob_path.parent.mkdir(parents=lowercase      ,exist_ok=lowercase						)
      os.replace(lowercase      ,lowercase						)
      try:
        os.symlink(lowercase      ,lowercase						)
      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    (	lowercase      ,lowercase = None						):







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

  return weights_name






def 			__UpperCAmelCase    (	lowercase      ,*,
    lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase=None      ,):







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


import math






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					2
  _UpperCAmelCase         =					int(math.sqrt(lowercase						)						)  # Size of every segment
  _UpperCAmelCase         =					[True] * (end + 1)
  _UpperCAmelCase         =					[]

  while start <= end:
    if temp[start] is True:
      in_prime.append(lowercase						)
      for i in range(start * start      ,end + 1      ,lowercase						):
        _UpperCAmelCase         =					False
    start += 1
  prime += in_prime

  _UpperCAmelCase         =					end + 1
  _UpperCAmelCase         =					min(2 * end      ,lowercase						)

  while low <= n:
    _UpperCAmelCase         =					[True] * (high - low + 1)
    for each in in_prime:
      _UpperCAmelCase         =					math.floor(low / each						) * each
      if t < low:
        t += each

      for j in range(lowercase      ,high + 1      ,lowercase						):
        _UpperCAmelCase         =					False

    for j in range(len(lowercase						)						):
      if temp[j] is True:
        prime.append(j + low						)

    _UpperCAmelCase         =					high + 1
    _UpperCAmelCase         =					min(high + end      ,lowercase						)

  return prime

print(sieve(1_0**6))
