



'''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							:int				    =					get_logger(__name__)


lowerCamelCase							:List[str]				    =					Path(__file__).parent / 'model_card_template.md'
lowerCamelCase							:str				    =					uuida().hex
lowerCamelCase							:List[str]				    =					os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase							:Optional[Any]				    =					os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase							:str				    =					HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'



def 	a			( lowerCamelCase__ = None    ):
			'''simple docstring'''





			A_	:						Union[str, 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(lowercase_						,      lowercase_    ):
						ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items()    )
			elif isinstance(lowercase_						,      lowercase_    ):
						ua += "; " + user_agent
			return ua



def 	a			( lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None    ):
			'''simple docstring'''





			if token is None:
						A_	:						Dict	=     HfFolder.get_token()
			if organization is None:
						A_	:						int	=     whoami(lowercase_    )["""name"""]
						return f'{username}/{model_id}'
			else:
						return f'{organization}/{model_id}'



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''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

			A_	:						Union[str, Any]	=     args.hub_token if hasattr(lowercase_						,      """hub_token"""    ) else None
			A_	:						Union[str, Any]	=     get_full_repo_name(lowercase_						,      token=lowercase_    )

			A_	:						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=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						,      )

			A_	:						int	=     os.path.join(args.output_dir						,      """README.md"""    )
			model_card.save(lowercase_    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__ = None    ):
			'''simple docstring'''





			if resolved_file is None or commit_hash is not None:
						return commit_hash
			A_	:						List[str]	=     str(Path(lowercase_    ).as_posix()    )
			A_	:						str	=     re.search(r"""snapshots/([^/]+)/"""						,      lowercase_    )
			if search is None:
						return None
			A_	:						Union[str, Any]	=     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.
lowerCamelCase							:Optional[int]				    =					os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowerCamelCase							:str				    =					os.path.join(hf_cache_home, '''diffusers''')



def 	a			( lowerCamelCase__ = None						,      lowerCamelCase__ = None    ):
			'''simple docstring'''





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

			A_	:						List[str]	=     Path(lowercase_    ).expanduser()
			A_	:						Tuple	=     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():
									A_	:						Dict	=     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).


lowerCamelCase							:int				    =					os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
						lowerCamelCase							:Union[str, Any]				    =					0
else:
						with open(cache_version_file) as f:
												try:
																		lowerCamelCase							:Optional[int]				    =					int(f.read())
												except ValueError:
																		lowerCamelCase							:int				    =					0

if cache_version < 1:
						lowerCamelCase							:int				    =					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							:int				    =					'\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 	a			( lowerCamelCase__						,      lowerCamelCase__ = None    ):
			'''simple docstring'''





			if variant is not None:
						A_	:						Union[str, Any]	=     weights_name.split("""."""    )
						A_	:						Optional[Any]	=     splits[:-1] + [variant] + splits[-1:]
						A_	:						str	=     """.""".join(lowercase_    )

			return weights_name



def 	a			( lowerCamelCase__						,      *,
    lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      ):
			'''simple docstring'''





			A_	:						Optional[int]	=     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
									A_	:						Union[str, Any]	=     os.path.join(lowercase_						,      lowercase_    )
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(lowercase_						,      lowercase_						,      lowercase_    )    ):
									A_	:						List[str]	=     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:
												A_	:						int	=     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
									A_	:						Any	=     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}'    )


import argparse
from collections import defaultdict

import yaml


A_       : List[str]         =			'docs/source/en/_toctree.yml'

def 						UpperCamelCase					(lowercase_: Optional[int]					)     ->      List[str]:
	A__							:    Dict        =		defaultdict(lowercase_					)
	A__							:    Optional[int]        =		[]
	A__							:    Union[str, Any]        =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}					)
		else:
			new_doc_list.append(lowercase_					)

	A__							:    Optional[int]        =		new_doc_list
	A__							:    Optional[int]        =		[key for key, value in counts.items() if value > 1]

	A__							:    Optional[Any]        =		[]
	for duplicate_key in duplicates:
		A__							:    List[Any]        =		list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}					)
		if len(lowercase_					) > 1:
			raise ValueError(
			    f"""{duplicate_key} is present several times in the documentation table of content at """
			    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
			    """others."""					)
		# Only add this once
		new_doc.append({"""local""": duplicate_key, """title""": titles[0]}					)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]					)
	A__							:    Dict        =		sorted(lowercase_    ,      key=lambda lowercase_					: s["title"].lower()					)

	# "overview" gets special treatment and is always first
	if len(lowercase_					) > 1:
		raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""					)

	overview_doc.extend(lowercase_					)

	# Sort
	return overview_doc

def 						UpperCamelCase					(lowercase_: Tuple=False					)     ->      List[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    Dict        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    List[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    Union[str, Any]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    Dict        =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	A__							:    List[Any]        =		api_doc[scheduler_idx]["""sections"""]
	A__							:    Union[str, Any]        =		clean_doc_toc(lowercase_					)

	A__							:    Optional[int]        =		False
	if new_scheduler_doc != scheduler_doc:
		A__							:    List[Any]        =		True
		if overwrite:
			A__							:    Optional[int]        =		new_scheduler_doc

	if diff:
		if overwrite:
			A__							:    Tuple        =		api_doc
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
				f.write(yaml.dump(lowercase_    ,      allow_unicode=lowercase_					)					)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)

def 						UpperCamelCase					(lowercase_: Dict=False					)     ->      Optional[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    int        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    Optional[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    List[str]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    List[Any]        =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	A__							:    Dict        =		False
	A__							:    Tuple        =		api_doc[pipeline_idx]["""sections"""]
	A__							:    Tuple        =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			A__							:    List[Any]        =		pipeline_doc["""section"""]
			A__							:    Dict        =		clean_doc_toc(lowercase_					)
			if overwrite:
				A__							:    Optional[Any]        =		new_sub_pipeline_doc
		new_pipeline_docs.append(lowercase_					)

	# sort overall pipeline doc
	A__							:    Optional[int]        =		clean_doc_toc(lowercase_					)

	if new_pipeline_docs != pipeline_docs:
		A__							:    int        =		True
		if overwrite:
			A__							:    List[Any]        =		new_pipeline_docs

	if diff:
		if overwrite:
			A__							:    Union[str, Any]        =		api_doc
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
				f.write(yaml.dump(lowercase_    ,      allow_unicode=lowercase_					)					)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)


if __name__ == "__main__":
	A_       : str         =			argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	A_       : str         =			parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)



