

import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			List[Any]   ,   a				:			List[Any]   ,   a				:			List[Any]=13   ,   a				:			Union[str, Any]=7   ,   a				:			Optional[int]=True   ,   a				:			Optional[int]=True   ,   a				:			int=True   ,   a				:			Any=True   ,   a				:			Dict=99   ,   a				:			Tuple=32   ,   a				:			Optional[int]=5   ,   a				:			List[Any]=4   ,   a				:			Optional[int]=4   ,   a				:			List[str]="gelu"   ,   a				:			Optional[int]=0.0   ,   a				:			int=0.1   ,   a				:			List[Any]=True   ,   a				:			Union[str, Any]=512   ,   a				:			Tuple=16   ,   a				:			Union[str, Any]=2   ,   a				:			List[str]=0.02   ,   a				:			Any=3   ,   a				:			int=4   ,   a				:			int=None   ,   )				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							parent
				SCREAMING_SNAKE_CASE :   Any					  =							batch_size
				SCREAMING_SNAKE_CASE :   Dict					  =							seq_length
				SCREAMING_SNAKE_CASE :   str					  =							is_training
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							use_input_mask
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							use_token_type_ids
				SCREAMING_SNAKE_CASE :   List[str]					  =							use_labels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   str					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   List[str]					  =							intermediate_multiple_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							hidden_dropout
				SCREAMING_SNAKE_CASE :   Any					  =							attention_dropout
				SCREAMING_SNAKE_CASE :   str					  =							weight_tying
				SCREAMING_SNAKE_CASE :   Any					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   int					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Any					  =							type_sequence_label_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Any					  =							num_labels
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_choices
				SCREAMING_SNAKE_CASE :   List[str]					  =							scope
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							None
				if self.use_input_mask:
					SCREAMING_SNAKE_CASE :   List[str]					  =							random_attention_mask([self.batch_size, self.seq_length]						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							None
				if self.use_labels:
					SCREAMING_SNAKE_CASE :   Dict					  =							ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.get_config()

				return config, input_ids, input_mask, token_labels
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				return GPTNeoXJapaneseConfig(
				    vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_multiple_size=self.intermediate_multiple_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout=self.hidden_dropout   ,   attention_dropout=self.attention_dropout   ,   weight_tying=self.weight_tying   ,   max_position_embeddings=self.max_position_embeddings   ,   type_vocab_size=self.type_vocab_size   ,   is_decoder=a   ,   initializer_range=self.initializer_range   ,   )
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.prepare_config_and_inputs()

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							True

				return config, input_ids, input_mask, token_labels
			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Union[str, Any]   ,   a				:			Optional[Any]   ,   a				:			int						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							GPTNeoXJapaneseModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   str					  =							model(a   ,   attention_mask=a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(a						)
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Optional[int]   ,   a				:			List[str]   ,   a				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							True
				SCREAMING_SNAKE_CASE :   Dict					  =							GPTNeoXJapaneseModel(a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							model(a   ,   attention_mask=a						)
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Tuple   ,   a				:			Union[str, Any]   ,   a				:			Union[str, Any]   ,   a				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							GPTNeoXJapaneseForCausalLM(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			Tuple   ,   a				:			Tuple   ,   a				:			Dict						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
				SCREAMING_SNAKE_CASE :   str					  =							GPTNeoXJapaneseForCausalLM(config=a						)
				model.to(a						)
				model.eval()

				# first forward pass
				SCREAMING_SNAKE_CASE :   List[str]					  =							model(a   ,   attention_mask=a   ,   use_cache=a						)
				SCREAMING_SNAKE_CASE :   Any					  =							outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				SCREAMING_SNAKE_CASE :   Dict					  =							ids_tensor((self.batch_size, 3)   ,   config.vocab_size						)
				SCREAMING_SNAKE_CASE :   str					  =							ids_tensor((self.batch_size, 3)   ,   vocab_size=2						)

				# append to next input_ids and
				SCREAMING_SNAKE_CASE :   Any					  =							torch.cat([input_ids, next_tokens]   ,   dim=-1						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.cat([input_mask, next_mask]   ,   dim=-1						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							model(a   ,   attention_mask=a   ,   output_hidden_states=a						)
				SCREAMING_SNAKE_CASE :   Any					  =							output_from_no_past["hidden_states"][0]
				SCREAMING_SNAKE_CASE :   Dict					  =							model(
				    a   ,   attention_mask=a   ,   past_key_values=a   ,   output_hidden_states=a   ,   )["hidden_states"][0]

				# select random slice
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							ids_tensor((1,)   ,   output_from_past.shape[-1]						).item()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							output_from_no_past[:, -3:, random_slice_idx].detach()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(a   ,   a   ,   atol=1e-3						)						)






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   Any					  =							{"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
			lowerCamelCase__							     =(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__							     =(
			    {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							GPTNeoXJapaneseModelTester(self						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							ConfigTester(self   ,   config_class=a   ,   hidden_size=37						)
			def 				__UpperCamelCase			(      self				:			str						)				->							Optional[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.prepare_config_and_inputs_for_decoder()

				SCREAMING_SNAKE_CASE :   Dict					  =							None

				self.model_tester.create_and_check_model_as_decoder(a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_causal_lm(*a						)






			@slow
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"abeja/gpt-neox-japanese-2.7b"

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							[
				    "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
				    "100年後に必要とされる会社は、「人」が中心の会社です。",
				    "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
				    "国境の長いトンネルを抜けると、そこは雪国だった。",
				    "美味しい日本食といえば、やっぱりお寿司ですよね。",
				]

				SCREAMING_SNAKE_CASE :   List[str]					  =							GPTNeoXJapaneseTokenizer.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							GPTNeoXJapaneseForCausalLM.from_pretrained(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							[]
				for prompt in prompts:
					SCREAMING_SNAKE_CASE :   str					  =							tokenizer(a   ,   return_tensors="pt"						).input_ids
					SCREAMING_SNAKE_CASE :   Any					  =							model.generate(a   ,   max_length=50						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							tokenizer.batch_decode(a   ,   skip_special_tokens=a						)
					predicted_outputs += generated_string
				self.assertListEqual(a   ,   a						)



import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase   :    str     =		[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  Github(os.environ["""GITHUB_TOKEN"""]      )
			lowerCamelCase    		=  g.get_repo("""huggingface/diffusers"""      )
			lowerCamelCase    		=  repo.get_issues(state="""open"""      )

			for issue in open_issues:
						lowerCamelCase    		=  sorted(issue.get_comments()  ,							key=lambda lowerCamelCase__      : i.created_at  ,							reverse=lowerCamelCase__      )
						lowerCamelCase    		=  comments[0] if len(lowerCamelCase__      ) > 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()      )
						):
									# Closes the issue after 7 days of inactivity since the Stalebot notification.
									issue.edit(state="""closed"""      )
						elif (
						    "stale" in issue.get_labels()
						    and last_comment is not None
						    and last_comment.user.login != "github-actions[bot]"
						):
									# Opens the issue if someone other than Stalebot commented.
									issue.edit(state="""open"""      )
									issue.remove_from_labels("""stale"""      )
						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()      )
						):
									# Post a Stalebot notification after 23 days of inactivity.
									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/diffusers/blob/main/CONTRIBUTING.md) """
									    """are likely to be ignored."""      )
									issue.add_to_labels("""stale"""      )


if __name__ == "__main__":
		main()

