


'''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


__snake_case											=					get_logger(__name__)


__snake_case											=					Path(__file__).parent / '''model_card_template.md'''
__snake_case											=					uuida().hex
__snake_case											=					os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
__snake_case											=					os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
__snake_case											=					HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''


def       a				(      __a = None       )  ->     str:



						'''simple docstring'''






						UpperCamelCase__       :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(__a			, __a       ):
												ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()       )
						elif isinstance(__a			, __a       ):
												ua += "; " + user_agent
						return ua


def       a				(      __a			, __a = None			, __a = None       )  ->     List[Any]:



						'''simple docstring'''






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


def       a				(      __a			, __a       )  ->     int:



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

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

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

						UpperCamelCase__       :Optional[Any]							 =			os.path.join(args.output_dir			, '''README.md'''       )
						model_card.save(__a       )


def       a				(      __a			, __a = None       )  ->     int:



						'''simple docstring'''






						if resolved_file is None or commit_hash is not None:
												return commit_hash
						UpperCamelCase__       :Any							 =			str(Path(__a       ).as_posix()       )
						UpperCamelCase__       :Optional[int]							 =			re.search(R'''snapshots/([^/]+)/'''			, __a       )
						if search is None:
												return None
						UpperCamelCase__       :Dict							 =			search.groups()[0]
						return commit_hash if REGEX_COMMIT_HASH.match(__a       ) 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.
__snake_case											=					os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
__snake_case											=					os.path.join(hf_cache_home, '''diffusers''')


def       a				(      __a = None			, __a = None       )  ->     None:



						'''simple docstring'''






						if new_cache_dir is None:
												UpperCamelCase__       :Dict							 =			DIFFUSERS_CACHE
						if old_cache_dir is None:
												UpperCamelCase__       :int							 =			old_diffusers_cache

						UpperCamelCase__       :Optional[int]							 =			Path(__a       ).expanduser()
						UpperCamelCase__       :List[Any]							 =			Path(__a       ).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(__a       )
																		new_blob_path.parent.mkdir(parents=__a			, exist_ok=__a       )
																		os.replace(__a			, __a       )
																		try:
																								os.symlink(__a			, __a       )
																		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).


__snake_case											=					os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
		__snake_case											=					0
else:
		with open(cache_version_file) as f:
				try:
						__snake_case											=					int(f.read())
				except ValueError:
						__snake_case											=					0

if cache_version < 1:
		__snake_case											=					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:
						__snake_case											=					'''\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				(      __a			, __a = None       )  ->     str:



						'''simple docstring'''






						if variant is not None:
												UpperCamelCase__       :Union[str, Any]							 =			weights_name.split('''.'''       )
												UpperCamelCase__       :Optional[int]							 =			splits[:-1] + [variant] + splits[-1:]
												UpperCamelCase__       :List[str]							 =			'''.'''.join(__a       )

						return weights_name







def       a				(      __a			, *,
    __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a=None			, )  ->     Tuple:



						'''simple docstring'''






						UpperCamelCase__       :int							 =			str(__a       )
						if os.path.isfile(__a       ):
												return pretrained_model_name_or_path
						elif os.path.isdir(__a       ):
												if os.path.isfile(os.path.join(__a			, __a       )       ):
																		# Load from a PyTorch checkpoint
																		UpperCamelCase__       :Any							 =			os.path.join(__a			, __a       )
																		return model_file
												elif subfolder is not None and os.path.isfile(
												    os.path.join(__a			, __a			, __a       )       ):
																		UpperCamelCase__       :Union[str, Any]							 =			os.path.join(__a			, __a			, __a       )
																		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(__a       ).base_version       ) >= version.parse('''0.20.0'''       )
												):
																		try:
																								UpperCamelCase__       :str							 =			hf_hub_download(
																								    __a			, filename=_add_variant(__a			, __a       )			, cache_dir=__a			, force_download=__a			, proxies=__a			, resume_download=__a			, local_files_only=__a			, use_auth_token=__a			, user_agent=__a			, subfolder=__a			, 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.'''			, __a			, )
																								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(__a			, __a       )} 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(__a			, __a       )}\' so that the correct variant file can be added.'''			, __a			, )
												try:
																		# 2. Load model file as usual
																		UpperCamelCase__       :Tuple							 =			hf_hub_download(
																		    __a			, filename=__a			, cache_dir=__a			, force_download=__a			, proxies=__a			, resume_download=__a			, local_files_only=__a			, use_auth_token=__a			, user_agent=__a			, subfolder=__a			, 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'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

__snake_case											=					get_logger()

__snake_case											=					None
class        lowercase     (							TensorFormatter[Mapping, 'jax.Array', Mapping] ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(features=UpperCamelCase_							)
							import jax
							from jaxlib.xla_client import Device

							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
												raise ValueError(
												    F'''Expected {device} to be a `str` not {type(UpperCamelCase_							)}, as `jaxlib.xla_extension.Device` '''
												    '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
												    '''the device with `str()` to get its string identifier that will be internally mapped '''
												    '''to the actual `jaxlib.xla_extension.Device`.'''							)
							UpperCamelCase__       :Tuple							 =			device if isinstance(UpperCamelCase_   ,       UpperCamelCase_							) else str(jax.devices()[0]							)
							# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
							# with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
												UpperCamelCase__       :Optional[Any]							 =			self._map_devices_to_str()
							if self.device not in list(DEVICE_MAPPING.keys()							):
												logger.warning(
												    F'''Device with string identifier {self.device} not listed among the available '''
												    F'''devices: {list(DEVICE_MAPPING.keys()							)}, so falling back to the default '''
												    F'''device: {str(jax.devices()[0]							)}.'''							)
												UpperCamelCase__       :Optional[int]							 =			str(jax.devices()[0]							)
							UpperCamelCase__       :Tuple							 =			jnp_array_kwargs




		@staticmethod
		def    lowerCAmelCase__    (		):

							'''simple docstring'''


							import jax

							return {str(UpperCamelCase_							): device for device in jax.devices()}




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax
							import jax.numpy as jnp

							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							) and column:
												if all(
												    isinstance(UpperCamelCase_   ,       jax.Array							) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column							):
																	return jnp.stack(UpperCamelCase_   ,       axis=0							)
							return column




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax
							import jax.numpy as jnp

							if isinstance(UpperCamelCase_   ,       (str, bytes, type(UpperCamelCase_							))							):
												return value
							elif isinstance(UpperCamelCase_   ,       (np.character, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.character							):
												return value.tolist()

							UpperCamelCase__       :Optional[int]							 =			{}

							if isinstance(UpperCamelCase_   ,       (np.number, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.integer							):
												# the default int precision depends on the jax config
												# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
												if jax.config.jax_enable_xaa:
																	UpperCamelCase__       :List[str]							 =			{'''dtype''': jnp.intaa}
												else:
																	UpperCamelCase__       :Union[str, Any]							 =			{'''dtype''': jnp.intaa}
							elif isinstance(UpperCamelCase_   ,       (np.number, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.floating							):
												UpperCamelCase__       :Optional[Any]							 =			{'''dtype''': jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(UpperCamelCase_   ,       PIL.Image.Image							):
																	UpperCamelCase__       :str							 =			np.asarray(UpperCamelCase_							)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
												UpperCamelCase__       :Dict							 =			self._map_devices_to_str()

							with jax.default_device(DEVICE_MAPPING[self.device]							):
												# calling jnp.array on a np.ndarray does copy the data
												# see https://github.com/google/jax/issues/4486
												return jnp.array(UpperCamelCase_   ,       **{**default_dtype, **self.jnp_array_kwargs}							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax

							# support for torch, tf, jax etc.
							if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(UpperCamelCase_   ,       torch.Tensor							):
																	return self._tensorize(data_struct.detach().cpu().numpy()[()]							)
							if hasattr(UpperCamelCase_   ,       '''__array__'''							) and not isinstance(UpperCamelCase_   ,       jax.Array							):
												UpperCamelCase__       :int							 =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(UpperCamelCase_   ,       np.ndarray							):
												if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																	return self._consolidate([self.recursive_tensorize(UpperCamelCase_							) for substruct in data_struct]							)
							elif isinstance(UpperCamelCase_   ,       (list, tuple)							):
												return self._consolidate([self.recursive_tensorize(UpperCamelCase_							) for substruct in data_struct]							)
							return self._tensorize(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return map_nested(self._recursive_tensorize   ,       UpperCamelCase_   ,       map_list=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_row(UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			self.python_features_decoder.decode_row(UpperCamelCase_							)
							return self.recursive_tensorize(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_column(UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			self.python_features_decoder.decode_column(UpperCamelCase_   ,       pa_table.column_names[0]							)
							UpperCamelCase__       :Dict							 =			self.recursive_tensorize(UpperCamelCase_							)
							UpperCamelCase__       :str							 =			self._consolidate(UpperCamelCase_							)
							return column




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.numpy_arrow_extractor().extract_batch(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			self.python_features_decoder.decode_batch(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			self.recursive_tensorize(UpperCamelCase_							)
							for column_name in batch:
												UpperCamelCase__       :Optional[int]							 =			self._consolidate(batch[column_name]							)
							return batch