

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


_UpperCAmelCase		:   int										=       get_logger(__name__)


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





def 					__lowerCamelCase	( UpperCamelCase__ = None      ):







							'''simple docstring'''






							snake_case_							=    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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None   ,							UpperCamelCase__ = None      ):







							'''simple docstring'''






							if token is None:
														snake_case_							=    HfFolder.get_token()
							if organization is None:
														snake_case_							=    whoami(UpperCamelCase__      )['name']
														return F'''{username}/{model_id}'''
							else:
														return F'''{organization}/{model_id}'''





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







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

							snake_case_							=    args.hub_token if hasattr(UpperCamelCase__   ,							'hub_token'      ) else None
							snake_case_							=    get_full_repo_name(UpperCamelCase__   ,							token=UpperCamelCase__      )

							snake_case_							=    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   ,							)

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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__ = None   ,							UpperCamelCase__ = None      ):







							'''simple docstring'''






							if new_cache_dir is None:
														snake_case_							=    DIFFUSERS_CACHE
							if old_cache_dir is None:
														snake_case_							=    old_diffusers_cache

							snake_case_							=    Path(UpperCamelCase__      ).expanduser()
							snake_case_							=    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():
																					snake_case_							=    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).


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

if cache_version < 1:
					_UpperCAmelCase		:   List[Any]										=       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:
															_UpperCAmelCase		:   Optional[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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None      ):







							'''simple docstring'''






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

							return weights_name





def 					__lowerCamelCase	( UpperCamelCase__   ,							*,
    UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=None   ,							):







							'''simple docstring'''






							snake_case_							=    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
																					snake_case_							=    os.path.join(UpperCamelCase__   ,							UpperCamelCase__      )
																					return model_file
														elif subfolder is not None and os.path.isfile(
														    os.path.join(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )      ):
																					snake_case_							=    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:
																												snake_case_							=    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
																					snake_case_							=    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 __future__ import annotations

import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    np.shape(UpperCamelCase__      )
							if rows != columns:
														snake_case_							=    (
														    '\'table\' has to be of square shaped array but got a '
														    F'''{rows}x{columns} array:\n{table}'''
														)
														raise ValueError(UpperCamelCase__      )

							snake_case_							=    np.zeros((rows, columns)      )
							snake_case_							=    np.zeros((rows, columns)      )
							for i in range(UpperCamelCase__      ):
														for j in range(UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					if upper[j][j] == 0:
																												raise ArithmeticError('No LU decomposition exists'      )
																					snake_case_							=    (table[i][j] - total) / upper[j][j]
														snake_case_							=    1
														for j in range(UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					snake_case_							=    table[i][j] - total
							return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()




