
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      lowerCamelCase__       (       __lowerCAmelCase						:						Union[Dict, str, None] = None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			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(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       ):
						ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()       )
			elif isinstance(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       ):
						ua += "; " + user_agent
			return ua






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Optional[str] = None					,							__lowerCAmelCase						:						Optional[str] = None       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Optional[Any]       ):




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

			lowerCAmelCase_      			=			args.hub_token if hasattr(_SCREAMING_SNAKE_CASE					,							"hub_token"       ) else None
			lowerCAmelCase_      			=			get_full_repo_name(_SCREAMING_SNAKE_CASE					,							token=_SCREAMING_SNAKE_CASE       )

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

			lowerCAmelCase_      			=			os.path.join(args.output_dir					,							"README.md"       )
			model_card.save(_SCREAMING_SNAKE_CASE       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[str]					,							__lowerCAmelCase						:						Optional[str] = None       ):




			"""simple docstring"""


			if resolved_file is None or commit_hash is not None:
						return commit_hash
			lowerCAmelCase_      			=			str(Path(_SCREAMING_SNAKE_CASE       ).as_posix()       )
			lowerCAmelCase_      			=			re.search(r"snapshots/([^/]+)/"					,							_SCREAMING_SNAKE_CASE       )
			if search is None:
						return None
			lowerCAmelCase_      			=			search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(_SCREAMING_SNAKE_CASE       ) 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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[str] = None					,							__lowerCAmelCase						:						Optional[str] = None       ):




			"""simple docstring"""


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

			lowerCAmelCase_      			=			Path(_SCREAMING_SNAKE_CASE       ).expanduser()
			lowerCAmelCase_      			=			Path(_SCREAMING_SNAKE_CASE       ).expanduser()
			for old_blob_path in old_cache_dir.glob("**/blobs/*"       ):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									lowerCAmelCase_      			=			new_cache_dir / old_blob_path.relative_to(_SCREAMING_SNAKE_CASE       )
									new_blob_path.parent.mkdir(parents=_SCREAMING_SNAKE_CASE					,							exist_ok=_SCREAMING_SNAKE_CASE       )
									os.replace(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )
									try:
												os.symlink(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )
									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      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Optional[str] = None       ):




			"""simple docstring"""


			if variant is not None:
						lowerCAmelCase_      			=			weights_name.split("."       )
						lowerCAmelCase_      			=			splits[:-1] + [variant] + splits[-1:]
						lowerCAmelCase_      			=			".".join(_SCREAMING_SNAKE_CASE       )

			return weights_name






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							*,
    __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						List[Any]=None					,							):




			"""simple docstring"""


			lowerCAmelCase_      			=			str(_SCREAMING_SNAKE_CASE       )
			if os.path.isfile(_SCREAMING_SNAKE_CASE       ):
						return pretrained_model_name_or_path
			elif os.path.isdir(_SCREAMING_SNAKE_CASE       ):
						if os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )       ):
									# Load from a PyTorch checkpoint
									lowerCAmelCase_      			=			os.path.join(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )       ):
									lowerCAmelCase_      			=			os.path.join(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )
									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(_SCREAMING_SNAKE_CASE       ).base_version       ) >= version.parse("0.20.0"       )
						):
									try:
												lowerCAmelCase_      			=			hf_hub_download(
												    _SCREAMING_SNAKE_CASE					,							filename=_add_variant(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )					,							cache_dir=_SCREAMING_SNAKE_CASE					,							force_download=_SCREAMING_SNAKE_CASE					,							proxies=_SCREAMING_SNAKE_CASE					,							resume_download=_SCREAMING_SNAKE_CASE					,							local_files_only=_SCREAMING_SNAKE_CASE					,							use_auth_token=_SCREAMING_SNAKE_CASE					,							user_agent=_SCREAMING_SNAKE_CASE					,							subfolder=_SCREAMING_SNAKE_CASE					,							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."""					,							_SCREAMING_SNAKE_CASE					,							)
												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(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )} 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(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )}\' so that the correct variant file can be added."""					,							_SCREAMING_SNAKE_CASE					,							)
						try:
									# 2. Load model file as usual
									lowerCAmelCase_      			=			hf_hub_download(
									    _SCREAMING_SNAKE_CASE					,							filename=_SCREAMING_SNAKE_CASE					,							cache_dir=_SCREAMING_SNAKE_CASE					,							force_download=_SCREAMING_SNAKE_CASE					,							proxies=_SCREAMING_SNAKE_CASE					,							resume_download=_SCREAMING_SNAKE_CASE					,							local_files_only=_SCREAMING_SNAKE_CASE					,							use_auth_token=_SCREAMING_SNAKE_CASE					,							user_agent=_SCREAMING_SNAKE_CASE					,							subfolder=_SCREAMING_SNAKE_CASE					,							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}"""       )





def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						set       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), len(grid[0]       )
			if (
			    min(__lowerCAmelCase					,							__lowerCAmelCase       ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)       )

			lowerCAmelCase_      			=			0
			count += depth_first_search(__lowerCAmelCase					,							row + 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							row - 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col + 1					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col - 1					,							__lowerCAmelCase       )

			visit.remove((row, col)       )
			return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()



