



from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
	import tensorflow as tf

	from transformers import TFBlipTextModel
	from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=12  ,  a_=7  ,  a_=True  ,  a_=True  ,  a_=True  ,  a_=99  ,  a_=32  ,  a_=32  ,  a_=2  ,  a_=4  ,  a_=37  ,  a_=0.1  ,  a_=0.1  ,  a_=512  ,  a_=0.02  ,  a_=0  ,  a_=None  ,  ):
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		seq_length
											a__				      =		is_training
											a__				      =		use_input_mask
											a__				      =		use_labels
											a__				      =		vocab_size
											a__				      =		hidden_size
											a__				      =		projection_dim
											a__				      =		num_hidden_layers
											a__				      =		num_attention_heads
											a__				      =		intermediate_size
											a__				      =		dropout
											a__				      =		attention_dropout
											a__				      =		max_position_embeddings
											a__				      =		initializer_range
											a__				      =		scope
											a__				      =		bos_token_id


							def _a      (							self		):
											a__				      =		ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size		)

											a__				      =		None
											if self.use_input_mask:
															a__				      =		random_attention_mask([self.batch_size, self.seq_length]		)

											if input_mask is not None:
															a__				      =		input_mask.numpy()
															a__      ,			a__				      =		input_mask.shape
															a__				      =		np.random.randint(1  ,  seq_length - 1  ,  size=(batch_size,)		)
															for batch_idx, start_index in enumerate(a_		):
																			a__				      =		1
																			a__				      =		0

											a__				      =		self.get_config()

											return config, input_ids, tf.convert_to_tensor(a_		)


							def _a      (							self		):
											return BlipTextConfig(
											    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  projection_dim=self.projection_dim  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  intermediate_size=self.intermediate_size  ,  dropout=self.dropout  ,  attention_dropout=self.attention_dropout  ,  max_position_embeddings=self.max_position_embeddings  ,  initializer_range=self.initializer_range  ,  bos_token_id=self.bos_token_id  ,  )


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		TFBlipTextModel(config=a_		)
											a__				      =		model(a_  ,  attention_mask=a_  ,  training=a_		)
											a__				      =		model(a_  ,  training=a_		)
											self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)		)
											self.parent.assertEqual(result.pooler_output.shape  ,  (self.batch_size, self.hidden_size)		)







							def _a      (							self		):
											a__				      =		self.prepare_config_and_inputs()
											a__      ,			a__      ,			a__				      =		config_and_inputs
											a__				      =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
											return config, inputs_dict




@require_tf
class  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					(TFBlipTextModel,) if is_tf_available() else ()
							UpperCamelCase__				:      Dict         =					False
							UpperCamelCase__				:      Any         =					False
							UpperCamelCase__				:      Tuple         =					False


							def _a      (							self		):
											a__				      =		BlipTextModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=a_  ,  hidden_size=37		)


							def _a      (							self		):
											self.config_tester.run_common_tests()


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a_		)


							def _a      (							self		):
											pass


							def _a      (							self		):
											pass


							@unittest.skip(reason="""Blip does not use inputs_embeds"""		)
							def _a      (							self		):
											pass


							@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""		)
							def _a      (							self		):
											pass


							@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""		)
							def _a      (							self		):
											pass


							@slow
							def _a      (							self		):
											for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__				      =		TFBlipTextModel.from_pretrained(a_		)
															self.assertIsNotNone(a_		)







							def _a      (							self  ,  a_=True		):
											super().test_pt_tf_model_equivalence(allow_missing_keys=a_		)






import os
from datetime import datetime as dt

from github import Github


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

def 			A_      (	):

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

				for issue in open_issues:
								a__				      =		sorted(issue.get_comments() ,		key=lambda __a		: i.created_at ,		reverse=__a		)
								a__				      =		comments[0] if len(__a		) > 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()

