


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 	__lowercase		(  _UpperCamelCase = None   )			->Union[str, Any]:







      """simple docstring"""

      lowercase	:      List[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 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase = None,			_UpperCamelCase = None   )			->List[str]:







      """simple docstring"""

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




def 	__lowercase		(  _UpperCamelCase,			_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(snake_case_,			'''local_rank'''   ) and args.local_rank not in [-1, 0]:
            return

      lowercase	:      Any          =						args.hub_token if hasattr(snake_case_,			'''hub_token'''   ) else None
      lowercase	:      int          =						get_full_repo_name(snake_case_,			token=snake_case_   )

      lowercase	:      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=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,			)

      lowercase	:      int          =						os.path.join(args.output_dir,			'''README.md'''   )
      model_card.save(snake_case_   )




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase = None   )			->Dict:







      """simple docstring"""

      if resolved_file is None or commit_hash is not None:
            return commit_hash
      lowercase	:      int          =						str(Path(snake_case_   ).as_posix()   )
      lowercase	:      List[Any]          =						re.search(R'''snapshots/([^/]+)/''',			snake_case_   )
      if search is None:
            return None
      lowercase	:      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.
__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 	__lowercase		(  _UpperCamelCase = None,			_UpperCamelCase = None   )			->Union[str, Any]:







      """simple docstring"""

      if new_cache_dir is None:
            lowercase	:      Union[str, Any]          =						DIFFUSERS_CACHE
      if old_cache_dir is None:
            lowercase	:      str          =						old_diffusers_cache

      lowercase	:      Any          =						Path(snake_case_   ).expanduser()
      lowercase	:      Tuple          =						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():
                  lowercase	:      Union[str, Any]          =						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).


__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 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase = None   )			->List[Any]:







      """simple docstring"""

      if variant is not None:
            lowercase	:      List[str]          =						weights_name.split('''.'''   )
            lowercase	:      Dict          =						splits[:-1] + [variant] + splits[-1:]
            lowercase	:      Any          =						""".""".join(snake_case_   )

      return weights_name




def 	__lowercase		(  _UpperCamelCase,			*,
    _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase=None,			)			->Any:







      """simple docstring"""

      lowercase	:      Tuple          =						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
                  lowercase	:      Tuple          =						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_   )   ):
                  lowercase	:      List[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:
                        lowercase	:      List[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
                  lowercase	:      Dict          =						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}"""   )







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      if n == 0:
            return 0
      _A							:     Tuple			=						float("""-inf"""     )
      for i in range(1,n + 1     ):
            _A							:     str			=						max(
                snake_case_,prices[i - 1] + naive_cut_rod_recursive(n - i,snake_case_     )     )

      return max_revue







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      _A							:     Dict			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      return _top_down_cut_rod_recursive(snake_case_,snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            _A							:     List[str]			=						float("""-inf"""     )
            for i in range(1,n + 1     ):
                  _A							:     Optional[Any]			=						max(
                      snake_case_,prices[i - 1] + _top_down_cut_rod_recursive(n - i,snake_case_,snake_case_     ),)

            _A							:     Tuple			=						max_revenue

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      _A							:     List[Any]			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      _A							:     Any			=						0

      for i in range(1,n + 1     ):
            _A							:     Optional[Any]			=						max_rev[i]
            for j in range(1,i + 1     ):
                  _A							:     int			=						max(snake_case_,prices[j - 1] + max_rev[i - j]     )

            _A							:     int			=						max_revenue_i

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if n < 0:
            _A							:     Optional[Any]			=						f'''n must be greater than or equal to 0. Got n = {n}'''
            raise ValueError(snake_case_     )

      if n > len(snake_case_     ):
            _A							:     Any			=						(
                """Each integral piece of rod must have a corresponding price. """
                f'''Got n = {n} but length of prices = {len(snake_case_     )}'''
            )
            raise ValueError(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						[6, 10, 12, 15, 20, 23]
      _A							:     List[Any]			=						len(snake_case_     )

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      _A							:     Any			=						36

      _A							:     List[Any]			=						top_down_cut_rod(snake_case_,snake_case_     )
      _A							:     List[Any]			=						bottom_up_cut_rod(snake_case_,snake_case_     )
      _A							:     Dict			=						naive_cut_rod_recursive(snake_case_,snake_case_     )

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()

