






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


lowerCAmelCase_           = get_logger(__name__)


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







def      snake_case		(							UpperCAmelCase	:       Union[Dict, str, None] = None   ):
				A	 =      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(UpperCAmelCase,						UpperCAmelCase   ):
								ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items()   )
				elif isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								ua += "; " + user_agent
				return ua







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       Optional[str] = None,						UpperCAmelCase	:       Optional[str] = None   ):
				if token is None:
								A	 =      HfFolder.get_token()
				if organization is None:
								A	 =      whoami(UpperCAmelCase   )['name']
								return f'{username}/{model_id}'
				else:
								return f'{organization}/{model_id}'







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

				A	 =      args.hub_token if hasattr(UpperCAmelCase,						'hub_token'   ) else None
				A	 =      get_full_repo_name(UpperCAmelCase,						token=UpperCAmelCase   )

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

				A	 =      os.path.join(args.output_dir,						'README.md'   )
				model_card.save(UpperCAmelCase   )







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







def      snake_case		(							UpperCAmelCase	:       Optional[str] = None,						UpperCAmelCase	:       Optional[str] = None   ):
				if new_cache_dir is None:
								A	 =      DIFFUSERS_CACHE
				if old_cache_dir is None:
								A	 =      old_diffusers_cache

				A	 =      Path(UpperCAmelCase   ).expanduser()
				A	 =      Path(UpperCAmelCase   ).expanduser()
				for old_blob_path in old_cache_dir.glob('**/blobs/*'   ):
								if old_blob_path.is_file() and not old_blob_path.is_symlink():
												A	 =      new_cache_dir / old_blob_path.relative_to(UpperCAmelCase   )
												new_blob_path.parent.mkdir(parents=UpperCAmelCase,						exist_ok=UpperCAmelCase   )
												os.replace(UpperCAmelCase,						UpperCAmelCase   )
												try:
																os.symlink(UpperCAmelCase,						UpperCAmelCase   )
												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).


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

if cache_version < 1:
					lowerCAmelCase_           = 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:
															lowerCAmelCase_           = '\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      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       Optional[str] = None   ):
				if variant is not None:
								A	 =      weights_name.split('.'   )
								A	 =      splits[:-1] + [variant] + splits[-1:]
								A	 =      '.'.join(UpperCAmelCase   )

				return weights_name







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













from math import factorial
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      real
						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      [1] * rank
						else:
										A	 =      rank
		def __repr__(  self		:       Any		)    ->   Optional[Any]:


						'''simple docstring'''
						return (
						    f'{self.real}+'
						    f'{"+".join(str(_SCREAMING_SNAKE_CASE		)+"E"+str(n+1		)for n,dual in enumerate(self.duals		)		)}'
						)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.duals.copy()
						while cur[-1] == 0:
										cur.pop(-1		)
						return Dual(self.real						,_SCREAMING_SNAKE_CASE		)
		def __add__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										return Dual(self.real + other						,self.duals		)
						A	 =      self.duals.copy()
						A	 =      other.duals.copy()
						if len(_SCREAMING_SNAKE_CASE		) > len(_SCREAMING_SNAKE_CASE		):
										o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						elif len(_SCREAMING_SNAKE_CASE		) < len(_SCREAMING_SNAKE_CASE		):
										s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						A	 =      []
						for i in range(len(_SCREAMING_SNAKE_CASE		)		):
										new_duals.append(s_dual[i] + o_dual[i]		)
						return Dual(self.real + other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __add__
		def __sub__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						return self + other * -1
		def __mul__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i * other		)
										return Dual(self.real * other						,_SCREAMING_SNAKE_CASE		)
						A	 =      [0] * (len(self.duals		) + len(other.duals		) + 1)
						for i, item in enumerate(self.duals		):
										for j, jtem in enumerate(other.duals		):
														new_duals[i + j + 1] += item * jtem
						for k in range(len(self.duals		)		):
										new_duals[k] += self.duals[k] * other.real
						for index in range(len(other.duals		)		):
										new_duals[index] += other.duals[index] * self.real
						return Dual(self.real * other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __mul__
		def __truediv__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   List[str]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i / other		)
										return Dual(self.real / other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError
		def __floordiv__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   Dict:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i // other		)
										return Dual(self.real // other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError







		def __pow__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						if n < 0 or isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										raise ValueError('power must be a positive integer'		)
						if n == 0:
										return 1
						if n == 1:
										return self
						A	 =      self
						for _ in range(n - 1		):
										x *= self
						return x







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       int   ):
				if not callable(UpperCAmelCase   ):
								raise ValueError('differentiate() requires a function as input for func'   )
				if not isinstance(UpperCAmelCase,						(float, int)   ):
								raise ValueError('differentiate() requires a float as input for position'   )
				if not isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								raise ValueError('differentiate() requires an int as input for order'   )
				A	 =      Dual(UpperCAmelCase,						1   )
				A	 =      func(UpperCAmelCase   )
				if order == 0:
								return result.real
				return result.duals[order - 1] * factorial(UpperCAmelCase   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







					def      snake_case		(							UpperCAmelCase	:       int   ):
									return y**2 * y**4



					print(differentiate(f, 9, 2))





