




"""simple docstring"""



import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
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 (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    AlbertForMaskedLM,
						    AlbertForMultipleChoice,
						    AlbertForPreTraining,
						    AlbertForQuestionAnswering,
						    AlbertForSequenceClassification,
						    AlbertForTokenClassification,
						    AlbertModel,
						)
						from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCAmelCase_  :




				def __init__(   self						:	Tuple     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]=13     ,			snake_case_						:	Optional[int]=7     ,			snake_case_						:	Any=True     ,			snake_case_						:	List[Any]=True     ,			snake_case_						:	Dict=True     ,			snake_case_						:	Dict=True     ,			snake_case_						:	List[str]=99     ,			snake_case_						:	Union[str, Any]=16     ,			snake_case_						:	Any=36     ,			snake_case_						:	List[Any]=6     ,			snake_case_						:	Optional[Any]=6     ,			snake_case_						:	Optional[Any]=6     ,			snake_case_						:	Any=37     ,			snake_case_						:	int="gelu"     ,			snake_case_						:	Any=0.1     ,			snake_case_						:	int=0.1     ,			snake_case_						:	str=512     ,			snake_case_						:	Union[str, Any]=16     ,			snake_case_						:	Tuple=2     ,			snake_case_						:	Any=0.02     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Union[str, Any]=4     ,			snake_case_						:	Any=None     ,			)				->							Tuple:




					'''simple docstring'''

					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__	=    embedding_size
					A__	=    hidden_size
					A__	=    num_hidden_layers
					A__	=    num_hidden_groups
					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 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




					'''simple docstring'''

					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 							__magic_name__							(   self						:	int				)				->							Dict:




					'''simple docstring'''

					return AlbertConfig(
					    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     ,			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     ,			initializer_range=self.initializer_range     ,			num_hidden_groups=self.num_hidden_groups     ,			)




				def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]     ,			snake_case_						:	List[str]     ,			snake_case_						:	Any     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]				)				->							Union[str, Any]:




					'''simple docstring'''

					A__	=    AlbertModel(config=snake_case_				)
					model.to(snake_case_				)
					model.eval()
					A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_				)
					A__	=    model(snake_case_     ,			token_type_ids=snake_case_				)
					A__	=    model(snake_case_				)
					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 							__magic_name__							(   self						:	int     ,			snake_case_						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]				)				->							int:




					'''simple docstring'''

					A__	=    AlbertForPreTraining(config=snake_case_				)
					model.to(snake_case_				)
					model.eval()
					A__	=    model(
					    snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_     ,			sentence_order_label=snake_case_     ,			)
					self.parent.assertEqual(result.prediction_logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)				)
					self.parent.assertEqual(result.sop_logits.shape     ,			(self.batch_size, config.num_labels)				)




				def 							__magic_name__							(   self						:	str     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]				)				->							Dict:




					'''simple docstring'''

					A__	=    AlbertForMaskedLM(config=snake_case_				)
					model.to(snake_case_				)
					model.eval()
					A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
					self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)				)




				def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	List[str]     ,			snake_case_						:	List[str]				)				->							Tuple:




					'''simple docstring'''

					A__	=    AlbertForQuestionAnswering(config=snake_case_				)
					model.to(snake_case_				)
					model.eval()
					A__	=    model(
					    snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			start_positions=snake_case_     ,			end_positions=snake_case_     ,			)
					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 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	str     ,			snake_case_						:	Any				)				->							Any:




					'''simple docstring'''

					A__	=    self.num_labels
					A__	=    AlbertForSequenceClassification(snake_case_				)
					model.to(snake_case_				)
					model.eval()
					A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
					self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)				)




				def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Dict     ,			snake_case_						:	int     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	int     ,			snake_case_						:	str     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]				)				->							List[str]:




					'''simple docstring'''

					A__	=    self.num_labels
					A__	=    AlbertForTokenClassification(config=snake_case_				)
					model.to(snake_case_				)
					model.eval()
					A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
					self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)				)




				def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any				)				->							Optional[Any]:




					'''simple docstring'''

					A__	=    self.num_choices
					A__	=    AlbertForMultipleChoice(config=snake_case_				)
					model.to(snake_case_				)
					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(
					    snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_     ,			)
					self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)				)




				def 							__magic_name__							(   self						:	Any				)				->							List[str]:




					'''simple docstring'''

					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      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
				lowercase__						 =				(
				    (
				        AlbertModel,
				        AlbertForPreTraining,
				        AlbertForMaskedLM,
				        AlbertForMultipleChoice,
				        AlbertForSequenceClassification,
				        AlbertForTokenClassification,
				        AlbertForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__						 =				(
				    {
				        '''feature-extraction''': AlbertModel,
				        '''fill-mask''': AlbertForMaskedLM,
				        '''question-answering''': AlbertForQuestionAnswering,
				        '''text-classification''': AlbertForSequenceClassification,
				        '''token-classification''': AlbertForTokenClassification,
				        '''zero-shot''': AlbertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__						 =				True




				def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]=False				)				->							Optional[int]:




					'''simple docstring'''

					A__	=    super()._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)

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




				def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




					'''simple docstring'''

					A__	=    AlbertModelTester(self				)
					A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			hidden_size=37				)




				def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Optional[int]:




					'''simple docstring'''

					self.config_tester.run_common_tests()




				def 							__magic_name__							(   self						:	Any				)				->							List[str]:




					'''simple docstring'''

					A__	=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*snake_case_				)




				def 							__magic_name__							(   self						:	Optional[int]				)				->							Union[str, Any]:




					'''simple docstring'''

					A__	=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*snake_case_				)




				def 							__magic_name__							(   self						:	int				)				->							str:




					'''simple docstring'''

					A__	=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*snake_case_				)




				def 							__magic_name__							(   self						:	Any				)				->							str:




					'''simple docstring'''

					A__	=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*snake_case_				)




				def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




					'''simple docstring'''

					A__	=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*snake_case_				)




				def 							__magic_name__							(   self						:	Optional[int]				)				->							Optional[Any]:




					'''simple docstring'''

					A__	=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*snake_case_				)




				def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




					'''simple docstring'''

					A__	=    self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						A__	=    type
						self.model_tester.create_and_check_model(*snake_case_				)




				@slow
				def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Dict:




					'''simple docstring'''

					for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A__	=    AlbertModel.from_pretrained(snake_case_				)
						self.assertIsNotNone(snake_case_				)






@require_torch
class      UpperCAmelCase_  (						unittest.TestCase							):




				@slow
				def 							__magic_name__							(   self						:	int				)				->							Tuple:




					'''simple docstring'''

					A__	=    AlbertModel.from_pretrained("albert-base-v2"				)
					A__	=    torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]				)
					A__	=    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
					with torch.no_grad():
						A__	=    model(snake_case_     ,			attention_mask=snake_case_				)[0]
					A__	=    torch.Size((1, 11, 768)				)
					self.assertEqual(output.shape     ,			snake_case_				)
					A__	=    torch.tensor(
					    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]				)

					self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,			snake_case_     ,			atol=1e-4				)				)











"""simple docstring"""



import random

from .binary_exp_mod import bin_exp_mod







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=10_00						)						->		Optional[Any]:
 if n < 2:
  return False

 if n % 2 == 0:
  return n == 2

 # this means n is odd
 A__	=    n - 1
 A__	=    0
 while d % 2 == 0:
  d /= 2
  exp += 1

 # n - 1=d*(2**exp)
 A__	=    0
 while count < prec:
  A__	=    random.randint(2      ,			n - 1						)
  A__	=    bin_exp_mod(lowercase_      ,			lowercase_      ,			lowercase_						)
  if b != 1:
   A__	=    True
   for _ in range(lowercase_						):
    if b == n - 1:
     A__	=    False
     break
    A__	=    b * b
    b %= n
   if flag:
    return False
   count += 1
 return True


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	abs(int(input("Enter bound : ").strip()))
      print("Here's the list of primes:")
      print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





