




"""simple docstring"""







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''gptj'''
   A_				      =       {
       '''max_position_embeddings''': '''n_positions''',
       '''hidden_size''': '''n_embd''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }







   def __init__(		self       ,					lowerCamelCase_=5_0_4_0_0       ,					lowerCamelCase_=2_0_4_8       ,					lowerCamelCase_=4_0_9_6       ,					lowerCamelCase_=2_8       ,					lowerCamelCase_=1_6       ,					lowerCamelCase_=6_4       ,					lowerCamelCase_=None       ,					lowerCamelCase_="gelu_new"       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=1e-5       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=True       ,					lowerCamelCase_=5_0_2_5_6       ,					lowerCamelCase_=5_0_2_5_6       ,					lowerCamelCase_=False       ,					**lowerCamelCase_       ,					)				->   Union[str, Any]:
       UpperCamelCase              =	vocab_size
       UpperCamelCase              =	n_positions
       UpperCamelCase              =	n_embd
       UpperCamelCase              =	n_layer
       UpperCamelCase              =	n_head
       UpperCamelCase              =	n_inner
       UpperCamelCase              =	rotary_dim
       UpperCamelCase              =	activation_function
       UpperCamelCase              =	resid_pdrop
       UpperCamelCase              =	embd_pdrop
       UpperCamelCase              =	attn_pdrop
       UpperCamelCase              =	layer_norm_epsilon
       UpperCamelCase              =	initializer_range
       UpperCamelCase              =	use_cache

       UpperCamelCase              =	bos_token_id
       UpperCamelCase              =	eos_token_id

       super().__init__(
           bos_token_id=lowerCamelCase_       ,					eos_token_id=lowerCamelCase_       ,					tie_word_embeddings=lowerCamelCase_       ,					**lowerCamelCase_)

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = "default"       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = False       ,					)				->   int:
       super().__init__(lowerCamelCase_       ,					task=lowerCamelCase_       ,					patching_specs=lowerCamelCase_       ,					use_past=lowerCamelCase_)
       if not getattr(self._config       ,					'''pad_token_id'''       ,					lowerCamelCase_):
           # TODO: how to do that better?
           UpperCamelCase              =	0







   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       UpperCamelCase              =	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
       if self.use_past:
           self.fill_with_past_key_values_(lowerCamelCase_       ,					direction='''inputs''')
           UpperCamelCase              =	{0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
           UpperCamelCase              =	{0: '''batch''', 1: '''sequence'''}

       return common_inputs







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self._config.n_layer







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self._config.n_head







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = -1       ,					lowerCamelCase_ = -1       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = None       ,					)				->   Mapping[str, Any]:
       UpperCamelCase              =	super(lowerCamelCase_       ,					self).generate_dummy_inputs(
           lowerCamelCase_       ,					batch_size=lowerCamelCase_       ,					seq_length=lowerCamelCase_       ,					is_pair=lowerCamelCase_       ,					framework=lowerCamelCase_)

       # We need to order the input in the way they appears in the forward()
       UpperCamelCase              =	OrderedDict({'''input_ids''': common_inputs['''input_ids''']})

       # Need to add the past_keys
       if self.use_past:
           if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
           else:
               import torch

               UpperCamelCase       ,	UpperCamelCase              =	common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               UpperCamelCase              =	seqlen + 2
               UpperCamelCase              =	(
                   batch,
                   self.num_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // self.num_attention_heads,
               )
               UpperCamelCase              =	[
                   (torch.zeros(lowerCamelCase_), torch.zeros(lowerCamelCase_)) for _ in range(self.num_layers)
               ]

       UpperCamelCase              =	common_inputs['''attention_mask''']
       if self.use_past:
           UpperCamelCase              =	ordered_inputs['''attention_mask'''].dtype
           UpperCamelCase              =	torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase_       ,					lowerCamelCase_       ,					dtype=lowerCamelCase_)]       ,					dim=1)

       return ordered_inputs







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return 1_3





from datetime import datetime as dt
import os

from github import Github


__A								=       [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def      lowerCamelCase_						(			)	->			int:





				"""simple docstring"""


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

				for issue in open_issues:
								__lowerCamelCase									=       sorted([comment for comment in issue.get_comments()]					,	key=lambda UpperCamelCase__ : i.created_at					,	reverse=UpperCamelCase__ )
								__lowerCamelCase									=       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()


