




"""simple docstring"""

from datetime import datetime as dt
import os

from github import Github


_lowerCamelCase  :			Optional[Any]        =       [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Any	   =		Github(os.environ['''GITHUB_TOKEN''']	)
	A_  :				List[Any]	   =		g.get_repo('''huggingface/transformers'''	)
	A_  :				List[Any]	   =		repo.get_issues(state='''open'''	)

	for issue in open_issues:
		A_  :				Any	   =		sorted([comment for comment in issue.get_comments()]  ,      key=lambda _UpperCAmelCase	: i.created_at  ,      reverse=_UpperCAmelCase	)
		A_  :				int	   =		comments[0] if len(_UpperCAmelCase	) > 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 >= 30
		    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 > 23
		    and (dt.utcnow() - issue.created_at).days >= 30
		    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()











"""simple docstring"""

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCamelCase  :			List[str]        =       logging.get_logger(__name__)

_lowerCamelCase  :			str        =       {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    int         =   """umt5"""
				__lowerCAmelCase    :    List[str]         =   ["""past_key_values"""]







				def __init__(							self   :					Any      ,     _lowerCamelCase   :					Union[str, Any]=25_01_12      ,     _lowerCamelCase   :					Any=5_12      ,     _lowerCamelCase   :					Optional[int]=64      ,     _lowerCamelCase   :					str=10_24      ,     _lowerCamelCase   :					List[str]=8      ,     _lowerCamelCase   :					Union[str, Any]=None      ,     _lowerCamelCase   :					Any=6      ,     _lowerCamelCase   :					List[Any]=32      ,     _lowerCamelCase   :					Optional[Any]=1_28      ,     _lowerCamelCase   :					int=0.1      ,     _lowerCamelCase   :					Union[str, Any]=1E-6      ,     _lowerCamelCase   :					Tuple=1.0      ,     _lowerCamelCase   :					Optional[int]="gated-gelu"      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					Tuple="T5Tokenizer"      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					Optional[int]=0      ,     _lowerCamelCase   :					Tuple=1      ,     _lowerCamelCase   :					Tuple=0      ,     **_lowerCamelCase   :					List[str]      ,     ):



					"""simple docstring"""

					super().__init__(
					    is_encoder_decoder=_lowerCamelCase      ,     tokenizer_class=_lowerCamelCase      ,     tie_word_embeddings=_lowerCamelCase      ,     pad_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     decoder_start_token_id=_lowerCamelCase      ,     **_lowerCamelCase      ,     )
					A_  :				str	   =		vocab_size
					A_  :				List[Any]	   =		d_model
					A_  :				Optional[int]	   =		d_kv
					A_  :				int	   =		d_ff
					A_  :				Union[str, Any]	   =		num_layers
					A_  :				Optional[Any]	   =		(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					A_  :				Dict	   =		num_heads
					A_  :				Optional[int]	   =		relative_attention_num_buckets
					A_  :				Union[str, Any]	   =		relative_attention_max_distance
					A_  :				Any	   =		dropout_rate
					A_  :				Optional[int]	   =		layer_norm_epsilon
					A_  :				Tuple	   =		initializer_factor
					A_  :				Optional[int]	   =		feed_forward_proj
					A_  :				Dict	   =		use_cache

					A_  :				Any	   =		self.feed_forward_proj.split('''-'''       )
					A_  :				Tuple	   =		act_info[-1]
					A_  :				Any	   =		act_info[0] == '''gated'''

					if len(_lowerCamelCase       ) > 1 and act_info[0] != "gated" or len(_lowerCamelCase       ) > 2:
						raise ValueError(
						    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
						    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
						    '''\'gated-gelu\' or \'relu\''''       )

					if feed_forward_proj == "gated-gelu":
						A_  :				Dict	   =		'''gelu_new'''







				@property
				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					return self.d_model







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					return self.num_heads







				@property
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					return self.num_layers

class        lowercase      (    __UpperCAmelCase):







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				str	   =		{
					    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
					    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
					}
					if self.use_past:
						A_  :				Optional[Any]	   =		'''past_encoder_sequence + sequence'''
						A_  :				Optional[Any]	   =		{0: '''batch'''}
						A_  :				List[Any]	   =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
						A_  :				Optional[int]	   =		{0: '''batch''', 1: '''decoder_sequence'''}
						A_  :				List[str]	   =		{0: '''batch''', 1: '''decoder_sequence'''}

					if self.use_past:
						self.fill_with_past_key_values_(_lowerCamelCase      ,     direction='''inputs'''       )

					return common_inputs







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					return 13







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return 5E-4





