



import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    MobileBertForMaskedLM,
	    MobileBertForMultipleChoice,
	    MobileBertForNextSentencePrediction,
	    MobileBertForPreTraining,
	    MobileBertForQuestionAnswering,
	    MobileBertForSequenceClassification,
	    MobileBertForTokenClassification,
	    MobileBertModel,
	)

class  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=13  ,  a_=7  ,  a_=True  ,  a_=True  ,  a_=True  ,  a_=True  ,  a_=99  ,  a_=64  ,  a_=32  ,  a_=5  ,  a_=4  ,  a_=37  ,  a_="gelu"  ,  a_=0.1  ,  a_=0.1  ,  a_=512  ,  a_=16  ,  a_=2  ,  a_=0.02  ,  a_=3  ,  a_=4  ,  a_=None  ,  ):
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		seq_length
											a__				      =		is_training
											a__				      =		use_input_mask
											a__				      =		use_token_type_ids
											a__				      =		use_labels
											a__				      =		vocab_size
											a__				      =		hidden_size
											a__				      =		embedding_size
											a__				      =		num_hidden_layers
											a__				      =		num_attention_heads
											a__				      =		intermediate_size
											a__				      =		hidden_act
											a__				      =		hidden_dropout_prob
											a__				      =		attention_probs_dropout_prob
											a__				      =		max_position_embeddings
											a__				      =		type_vocab_size
											a__				      =		type_sequence_label_size
											a__				      =		initializer_range
											a__				      =		num_labels
											a__				      =		num_choices
											a__				      =		scope


							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]		)

											a__				      =		None
											if self.use_token_type_ids:
															a__				      =		ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size		)

											a__				      =		None
											a__				      =		None
											a__				      =		None
											if self.use_labels:
															a__				      =		ids_tensor([self.batch_size]  ,  self.type_sequence_label_size		)
															a__				      =		ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels		)
															a__				      =		ids_tensor([self.batch_size]  ,  self.num_choices		)

											a__				      =		self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


							def _a      (							self		):
											return MobileBertConfig(
											    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_size=self.intermediate_size  ,  embedding_size=self.embedding_size  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  type_vocab_size=self.type_vocab_size  ,  is_decoder=a_  ,  initializer_range=self.initializer_range  ,  )


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertModel(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_  ,  attention_mask=a_  ,  token_type_ids=a_		)
											a__				      =		model(a_  ,  token_type_ids=a_		)
											a__				      =		model(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_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForMaskedLM(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_		)
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForNextSentencePrediction(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(
											    a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_  ,  )
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, 2)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForPreTraining(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(
											    a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_  ,  next_sentence_label=a_  ,  )
											self.parent.assertEqual(result.prediction_logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)		)
											self.parent.assertEqual(result.seq_relationship_logits.shape  ,  (self.batch_size, 2)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForQuestionAnswering(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(
											    a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  start_positions=a_  ,  end_positions=a_  ,  )
											self.parent.assertEqual(result.start_logits.shape  ,  (self.batch_size, self.seq_length)		)
											self.parent.assertEqual(result.end_logits.shape  ,  (self.batch_size, self.seq_length)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.num_labels
											a__				      =		MobileBertForSequenceClassification(a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_		)
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.num_labels
											a__				      =		MobileBertForTokenClassification(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_		)
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.num_choices
											a__				      =		MobileBertForMultipleChoice(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		input_ids.unsqueeze(1		).expand(-1  ,  self.num_choices  ,  -1		).contiguous()
											a__				      =		token_type_ids.unsqueeze(1		).expand(-1  ,  self.num_choices  ,  -1		).contiguous()
											a__				      =		input_mask.unsqueeze(1		).expand(-1  ,  self.num_choices  ,  -1		).contiguous()
											a__				      =		model(
											    a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_  ,  )
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)		)







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




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







							'''simple docstring'''






							UpperCamelCase__				:      Tuple         =					(
							    (
							        MobileBertModel,
							        MobileBertForMaskedLM,
							        MobileBertForMultipleChoice,
							        MobileBertForNextSentencePrediction,
							        MobileBertForPreTraining,
							        MobileBertForQuestionAnswering,
							        MobileBertForSequenceClassification,
							        MobileBertForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase__				:      str         =					(
							    {
							        """feature-extraction""": MobileBertModel,
							        """fill-mask""": MobileBertForMaskedLM,
							        """question-answering""": MobileBertForQuestionAnswering,
							        """text-classification""": MobileBertForSequenceClassification,
							        """token-classification""": MobileBertForTokenClassification,
							        """zero-shot""": MobileBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase__				:      Dict         =					True


							def _a      (							self  ,  a_  ,  a_  ,  a_=False		):
											a__				      =		super()._prepare_for_class(a_  ,  a_  ,  return_labels=a_		)

											if return_labels:
															if model_class in get_values(a_		):
																			a__				      =		torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.seq_length)  ,  dtype=torch.long  ,  device=a_		)
																			a__				      =		torch.zeros(
																			    self.model_tester.batch_size  ,  dtype=torch.long  ,  device=a_		)
											return inputs_dict


							def _a      (							self		):
											a__				      =		MobileBertModelTester(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_mobilebert_model(*a_		)


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


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


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


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


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


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







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




def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				return torch.tensor(
				    __a ,		dtype=torch.long ,		device=__a ,		)


UpperCAmelCase							 =					1E-3




@require_torch
@require_sentencepiece
@require_tokenizers
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							@slow
							def _a      (							self		):
											a__				      =		MobileBertModel.from_pretrained("""google/mobilebert-uncased"""		).to(a_		)
											a__				      =		_long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]		)
											with torch.no_grad():
															a__				      =		model(a_		)[0]
											a__				      =		torch.Size((1, 9, 512)		)
											self.assertEqual(output.shape  ,  a_		)
											a__				      =		torch.tensor(
											    [
											        [
											            [-2.4_7_3_6_5_2_6E0_7, 8.2_6_9_1_6_5_6E0_4, 1.6_5_2_1_8_3_8E0_5],
											            [-5.7_5_4_1_7_0_4E-0_1, 3.9_0_5_6_0_2_2E0_0, 4.4_0_1_1_5_0_7E0_0],
											            [2.6_0_4_7_3_5_9E0_0, 1.5_6_7_7_6_5_2E0_0, -1.7_3_2_4_1_8_8E-0_1],
											        ]
											    ]  ,  device=a_  ,  )

											# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
											# ~1 difference, it's therefore not a good idea to measure using addition.
											# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
											# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
											a__				      =		torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE		)
											a__				      =		torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE		)

											self.assertTrue(lower_bound and upper_bound		)






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()

