


'''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


__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       _UpperCamelCase (						UpperCamelCase__ = None   ):
			UpperCAmelCase__					:   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(UpperCamelCase__	, UpperCamelCase__   ):
						ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()   )
			elif isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						ua += "; " + user_agent
			return ua







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = None	, UpperCamelCase__ = None   ):
			if token is None:
						UpperCAmelCase__					:   List[str]       				=       HfFolder.get_token()
			if organization is None:
						UpperCAmelCase__					:   Dict       				=       whoami(UpperCamelCase__   )["""name"""]
						return f'''{username}/{model_id}'''
			else:
						return f'''{organization}/{model_id}'''







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			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

			UpperCAmelCase__					:   int       				=       args.hub_token if hasattr(UpperCamelCase__	, """hub_token"""   ) else None
			UpperCAmelCase__					:   Optional[int]       				=       get_full_repo_name(UpperCamelCase__	, token=UpperCamelCase__   )

			UpperCAmelCase__					:   Dict       				=       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	, )

			UpperCAmelCase__					:   str       				=       os.path.join(args.output_dir	, """README.md"""   )
			model_card.save(UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = None   ):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			UpperCAmelCase__					:   Union[str, Any]       				=       str(Path(UpperCamelCase__   ).as_posix()   )
			UpperCAmelCase__					:   List[Any]       				=       re.search(R"""snapshots/([^/]+)/"""	, UpperCamelCase__   )
			if search is None:
						return None
			UpperCAmelCase__					:   Tuple       				=       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.
__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       _UpperCamelCase (						UpperCamelCase__ = None	, UpperCamelCase__ = None   ):
			if new_cache_dir is None:
						UpperCAmelCase__					:   Union[str, Any]       				=       DIFFUSERS_CACHE
			if old_cache_dir is None:
						UpperCAmelCase__					:   Optional[int]       				=       old_diffusers_cache

			UpperCAmelCase__					:   str       				=       Path(UpperCamelCase__   ).expanduser()
			UpperCAmelCase__					:   Union[str, Any]       				=       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():
									UpperCAmelCase__					:   List[Any]       				=       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).


__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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = None   ):
			if variant is not None:
						UpperCAmelCase__					:   int       				=       weights_name.split("""."""   )
						UpperCAmelCase__					:   str       				=       splits[:-1] + [variant] + splits[-1:]
						UpperCAmelCase__					:   Dict       				=       """.""".join(UpperCamelCase__   )

			return weights_name







def       _UpperCamelCase (						UpperCamelCase__	, *,
    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, ):
			UpperCAmelCase__					:   List[str]       				=       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
									UpperCAmelCase__					:   str       				=       os.path.join(UpperCamelCase__	, UpperCamelCase__   )
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )   ):
									UpperCAmelCase__					:   Dict       				=       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:
												UpperCAmelCase__					:   Optional[Any]       				=       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
									UpperCAmelCase__					:   Optional[int]       				=       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}'''   )



'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[Any]       				=       x
			UpperCAmelCase__					:   Optional[int]       				=       y
			for step in range(UpperCamelCase__   ):  # noqa: B007
						UpperCAmelCase__					:   List[str]       				=       a * a - b * b + x
						UpperCAmelCase__					:   Optional[int]       				=       2 * a * b + y
						UpperCAmelCase__					:   int       				=       a_new

						# divergence happens for all complex number with an absolute value
						# greater than 4
						if a * a + b * b > 4:
									break
			return step / (max_step - 1)







def       _UpperCamelCase (						UpperCamelCase__   ):
			if distance == 1:
						return (0, 0, 0)
			else:
						return (2_5_5, 2_5_5, 2_5_5)







def       _UpperCamelCase (						UpperCamelCase__   ):
			if distance == 1:
						return (0, 0, 0)
			else:
						return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(UpperCamelCase__	, 1	, 1   )   )







def       _UpperCamelCase (						UpperCamelCase__ = 8_0_0	, UpperCamelCase__ = 6_0_0	, UpperCamelCase__ = -0.6	, UpperCamelCase__ = 0	, UpperCamelCase__ = 3.2	, UpperCamelCase__ = 5_0	, UpperCamelCase__ = True	, ):
			UpperCAmelCase__					:   str       				=       Image.new("""RGB"""	, (image_width, image_height)   )
			UpperCAmelCase__					:   Optional[int]       				=       img.load()

			# loop through the image-coordinates
			for image_x in range(UpperCamelCase__   ):
						for image_y in range(UpperCamelCase__   ):
									# determine the figure-coordinates based on the image-coordinates
									UpperCAmelCase__					:   Union[str, Any]       				=       figure_width / image_width * image_height
									UpperCAmelCase__					:   Union[str, Any]       				=       figure_center_x + (image_x / image_width - 0.5) * figure_width
									UpperCAmelCase__					:   Tuple       				=       figure_center_y + (image_y / image_height - 0.5) * figure_height

									UpperCAmelCase__					:   List[str]       				=       get_distance(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )

									# color the corresponding pixel based on the selected coloring-function
									if use_distance_color_coding:
												UpperCAmelCase__					:   Any       				=       get_color_coded_rgb(UpperCamelCase__   )
									else:
												UpperCAmelCase__					:   Tuple       				=       get_black_and_white_rgb(UpperCamelCase__   )

			return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		__A		=get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()