



'''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

a     :    Any      						=					get_logger()

a     :    Optional[dict]      						=					None

class     UpperCamelCase_				(							TensorFormatter[Mapping, 'jax.Array', Mapping]    ):





	def __init__(						self	,	A=None	,	A=None	,	**A					)    ->    str:
								super().__init__(features=A					)
								import jax
								from jaxlib.xla_client import Device

								if isinstance(A	,	A					):
															raise ValueError(
															    f'''Expected {device} to be a `str` not {type(A					)}, 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  :	Optional[int]							  =	device if isinstance(A	,	A					) 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  :	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  :	List[Any]							  =	str(jax.devices()[0]					)
								UpperCAmelCase  :	Union[str, Any]							  =	jnp_array_kwargs





	@staticmethod
	def 				_lowercase(						)    ->    Dict[str, "jaxlib.xla_extension.Device"]:
								import jax

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





	def 				_lowercase(						self	,	A					)    ->    str:
								import jax
								import jax.numpy as jnp

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





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax
								import jax.numpy as jnp

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

								UpperCAmelCase  :	List[str]							  =	{}

								if isinstance(A	,	(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  :	str							  =	{"""dtype""": jnp.intaa}
															else:
																						UpperCAmelCase  :	int							  =	{"""dtype""": jnp.intaa}
								elif isinstance(A	,	(np.number, np.ndarray)					) and np.issubdtype(value.dtype	,	np.floating					):
															UpperCAmelCase  :	Any							  =	{"""dtype""": jnp.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
															import PIL.Image

															if isinstance(A	,	PIL.Image.Image					):
																						UpperCAmelCase  :	List[str]							  =	np.asarray(A					)

        # 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(A	,	**{**default_dtype, **self.jnp_array_kwargs}					)





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax

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

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





	def 				_lowercase(						self	,	A					)    ->    Dict:
								return map_nested(self._recursive_tensorize	,	A	,	map_list=A					)





	def 				_lowercase(						self	,	A					)    ->    Mapping:
								UpperCAmelCase  :	Union[str, Any]							  =	self.numpy_arrow_extractor().extract_row(A					)
								UpperCAmelCase  :	Dict							  =	self.python_features_decoder.decode_row(A					)
								return self.recursive_tensorize(A					)





	def 				_lowercase(						self	,	A					)    ->    "jax.Array":
								UpperCAmelCase  :	int							  =	self.numpy_arrow_extractor().extract_column(A					)
								UpperCAmelCase  :	Optional[Any]							  =	self.python_features_decoder.decode_column(A	,	pa_table.column_names[0]					)
								UpperCAmelCase  :	Optional[int]							  =	self.recursive_tensorize(A					)
								UpperCAmelCase  :	Any							  =	self._consolidate(A					)
								return column






	def 				_lowercase(						self	,	A					)    ->    Mapping:
								UpperCAmelCase  :	Optional[int]							  =	self.numpy_arrow_extractor().extract_batch(A					)
								UpperCAmelCase  :	List[str]							  =	self.python_features_decoder.decode_batch(A					)
								UpperCAmelCase  :	Union[str, Any]							  =	self.recursive_tensorize(A					)
								for column_name in batch:
															UpperCAmelCase  :	Optional[Any]							  =	self._consolidate(batch[column_name]					)
								return batch






'''simple docstring'''




from datetime import datetime as dt
import os

from github import Github


a     :    int      						=					[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	str							  =	Github(os.environ["""GITHUB_TOKEN"""]	)
							UpperCAmelCase  :	Dict							  =	g.get_repo("""huggingface/transformers"""	)
							UpperCAmelCase  :	int							  =	repo.get_issues(state="""open"""	)

							for issue in open_issues:
														UpperCAmelCase  :	Optional[int]							  =	sorted([comment for comment in issue.get_comments()]			,       key=lambda _lowercase	: i.created_at			,       reverse=_lowercase	)
														UpperCAmelCase  :	Any							  =	comments[0] if len(_lowercase	) > 0 else None
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and (dt.utcnow() - issue.updated_at).days > 7
														    and (dt.utcnow() - issue.created_at).days >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																					issue.edit(state="""closed"""	)
														elif (
														    (dt.utcnow() - issue.updated_at).days > 2_3
														    and (dt.utcnow() - issue.created_at).days >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would add stale comment to {issue.number}")
																					issue.create_comment(
																					    """This issue has been automatically marked as stale because it has not had """
																					    """recent activity. If you think this still needs to be addressed """
																					    """please comment on this thread.\n\nPlease note that issues that do not follow the """
																					    """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored."""	)


if __name__ == "__main__":
							main()

