






'''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


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       __UpperCAmelCase						(  lowerCamelCase_ = None)							->     str:
		UpperCamelCase__				:					Optional[int]					=					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(lowerCamelCase_     ,      lowerCamelCase_):
				ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items())
		elif isinstance(lowerCamelCase_     ,      lowerCamelCase_):
				ua += "; " + user_agent
		return ua







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = None)							->     Union[str, Any]:

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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Any:

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

		UpperCamelCase__				:					Tuple					=					args.hub_token if hasattr(lowerCamelCase_     ,      'hub_token') else None
		UpperCamelCase__				:					Union[str, Any]					=					get_full_repo_name(lowerCamelCase_     ,      token=lowerCamelCase_)

		UpperCamelCase__				:					List[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=lowerCamelCase_     ,      model_name=lowerCamelCase_     ,      repo_name=lowerCamelCase_     ,      dataset_name=args.dataset_name if hasattr(lowerCamelCase_     ,      '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(lowerCamelCase_     ,      'gradient_accumulation_steps') else None
		    )     ,      adam_betaa=args.adam_betaa if hasattr(lowerCamelCase_     ,      'adam_beta1') else None     ,      adam_betaa=args.adam_betaa if hasattr(lowerCamelCase_     ,      'adam_beta2') else None     ,      adam_weight_decay=args.adam_weight_decay if hasattr(lowerCamelCase_     ,      'adam_weight_decay') else None     ,      adam_epsilon=args.adam_epsilon if hasattr(lowerCamelCase_     ,      'adam_epsilon') else None     ,      lr_scheduler=args.lr_scheduler if hasattr(lowerCamelCase_     ,      'lr_scheduler') else None     ,      lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCamelCase_     ,      'lr_warmup_steps') else None     ,      ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCamelCase_     ,      'ema_inv_gamma') else None     ,      ema_power=args.ema_power if hasattr(lowerCamelCase_     ,      'ema_power') else None     ,      ema_max_decay=args.ema_max_decay if hasattr(lowerCamelCase_     ,      'ema_max_decay') else None     ,      mixed_precision=args.mixed_precision     ,      )

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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = None)							->     str:

		if resolved_file is None or commit_hash is not None:
				return commit_hash
		UpperCamelCase__				:					Union[str, Any]					=					str(Path(lowerCamelCase_).as_posix())
		UpperCamelCase__				:					List[Any]					=					re.search(R'snapshots/([^/]+)/'     ,      lowerCamelCase_)
		if search is None:
				return None
		UpperCamelCase__				:					str					=					search.groups()[0]
		return commit_hash if REGEX_COMMIT_HASH.match(lowerCamelCase_) 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       __UpperCAmelCase						(  lowerCamelCase_ = None     ,      lowerCamelCase_ = None)							->     None:

		if new_cache_dir is None:
				UpperCamelCase__				:					Optional[Any]					=					DIFFUSERS_CACHE
		if old_cache_dir is None:
				UpperCamelCase__				:					str					=					old_diffusers_cache

		UpperCamelCase__				:					Optional[int]					=					Path(lowerCamelCase_).expanduser()
		UpperCamelCase__				:					Any					=					Path(lowerCamelCase_).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__				:					str					=					new_cache_dir / old_blob_path.relative_to(lowerCamelCase_)
						new_blob_path.parent.mkdir(parents=lowerCamelCase_     ,      exist_ok=lowerCamelCase_)
						os.replace(lowerCamelCase_     ,      lowerCamelCase_)
						try:
								os.symlink(lowerCamelCase_     ,      lowerCamelCase_)
						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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = None)							->     str:

		if variant is not None:
				UpperCamelCase__				:					Any					=					weights_name.split('.')
				UpperCamelCase__				:					Optional[Any]					=					splits[:-1] + [variant] + splits[-1:]
				UpperCamelCase__				:					Optional[Any]					=					'.'.join(lowerCamelCase_)

		return weights_name







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

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase__          =						300  # TEMPERATURE (unit = K)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      )							->     float:

		if donor_conc <= 0:
				raise ValueError('Donor concentration should be positive')
		elif acceptor_conc <= 0:
				raise ValueError('Acceptor concentration should be positive')
		elif intrinsic_conc <= 0:
				raise ValueError('Intrinsic concentration should be positive')
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    'Donor concentration should be greater than intrinsic concentration')
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    'Acceptor concentration should be greater than intrinsic concentration')
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




