


import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
							    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    DistilBertForMaskedLM,
							    DistilBertForMultipleChoice,
							    DistilBertForQuestionAnswering,
							    DistilBertForSequenceClassification,
							    DistilBertForTokenClassification,
							    DistilBertModel,
							)





class        lowerCAmelCase__ (					_A  ):






						"""simple docstring"""




						def __init__(       self ,				a_ ,				a_=13 ,				a_=7 ,				a_=True ,				a_=True ,				a_=False ,				a_=True ,				a_=99 ,				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 ,				):
							lowerCamelCase_					:				List[str]              =  parent
							lowerCamelCase_					:				int              =  batch_size
							lowerCamelCase_					:				Dict              =  seq_length
							lowerCamelCase_					:				Optional[Any]              =  is_training
							lowerCamelCase_					:				Dict              =  use_input_mask
							lowerCamelCase_					:				Optional[int]              =  use_token_type_ids
							lowerCamelCase_					:				Dict              =  use_labels
							lowerCamelCase_					:				Tuple              =  vocab_size
							lowerCamelCase_					:				Any              =  hidden_size
							lowerCamelCase_					:				Dict              =  num_hidden_layers
							lowerCamelCase_					:				Optional[int]              =  num_attention_heads
							lowerCamelCase_					:				str              =  intermediate_size
							lowerCamelCase_					:				Tuple              =  hidden_act
							lowerCamelCase_					:				str              =  hidden_dropout_prob
							lowerCamelCase_					:				Tuple              =  attention_probs_dropout_prob
							lowerCamelCase_					:				Any              =  max_position_embeddings
							lowerCamelCase_					:				Any              =  type_vocab_size
							lowerCamelCase_					:				Tuple              =  type_sequence_label_size
							lowerCamelCase_					:				List[str]              =  initializer_range
							lowerCamelCase_					:				Any              =  num_labels
							lowerCamelCase_					:				str              =  num_choices
							lowerCamelCase_					:				Optional[int]              =  scope




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Optional[Any]              =  ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

							lowerCamelCase_					:				int              =  None
							if self.use_input_mask:
								lowerCamelCase_					:				str              =  random_attention_mask([self.batch_size, self.seq_length]      )

							lowerCamelCase_					:				str              =  None
							lowerCamelCase_					:				Optional[Any]              =  None
							lowerCamelCase_					:				Any              =  None
							if self.use_labels:
								lowerCamelCase_					:				Optional[Any]              =  ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )
								lowerCamelCase_					:				Union[str, Any]              =  ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels      )
								lowerCamelCase_					:				Any              =  ids_tensor([self.batch_size] ,				self.num_choices      )

							lowerCamelCase_					:				int              =  self.get_config()

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




						def 	_UpperCamelCase					(       self      ):
							return DistilBertConfig(
							    vocab_size=self.vocab_size ,				dim=self.hidden_size ,				n_layers=self.num_hidden_layers ,				n_heads=self.num_attention_heads ,				hidden_dim=self.intermediate_size ,				hidden_act=self.hidden_act ,				dropout=self.hidden_dropout_prob ,				attention_dropout=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				initializer_range=self.initializer_range ,				)




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ ,				a_ ,				a_ ,				a_ ,				a_      ):
							lowerCamelCase_					:				str              =  DistilBertModel(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							lowerCamelCase_					:				Dict              =  model(__lowerCAmelCase ,				__lowerCAmelCase      )
							lowerCamelCase_					:				str              =  model(__lowerCAmelCase      )
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ ,				a_ ,				a_ ,				a_ ,				a_      ):
							lowerCamelCase_					:				List[Any]              =  DistilBertForMaskedLM(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							lowerCamelCase_					:				Dict              =  model(__lowerCAmelCase ,				attention_mask=__lowerCAmelCase ,				labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)      )




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ ,				a_ ,				a_ ,				a_ ,				a_      ):
							lowerCamelCase_					:				Tuple              =  DistilBertForQuestionAnswering(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							lowerCamelCase_					:				Optional[Any]              =  model(
							    __lowerCAmelCase ,				attention_mask=__lowerCAmelCase ,				start_positions=__lowerCAmelCase ,				end_positions=__lowerCAmelCase      )
							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 	_UpperCamelCase					(       self ,				a_ ,				a_ ,				a_ ,				a_ ,				a_ ,				a_      ):
							lowerCamelCase_					:				Optional[Any]              =  self.num_labels
							lowerCamelCase_					:				List[str]              =  DistilBertForSequenceClassification(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							lowerCamelCase_					:				int              =  model(__lowerCAmelCase ,				attention_mask=__lowerCAmelCase ,				labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ ,				a_ ,				a_ ,				a_ ,				a_      ):
							lowerCamelCase_					:				Union[str, Any]              =  self.num_labels
							lowerCamelCase_					:				Tuple              =  DistilBertForTokenClassification(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							lowerCamelCase_					:				List[Any]              =  model(__lowerCAmelCase ,				attention_mask=__lowerCAmelCase ,				labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ ,				a_ ,				a_ ,				a_ ,				a_      ):
							lowerCamelCase_					:				Dict              =  self.num_choices
							lowerCamelCase_					:				Union[str, Any]              =  DistilBertForMultipleChoice(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							lowerCamelCase_					:				str              =  input_ids.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
							lowerCamelCase_					:				Dict              =  input_mask.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
							lowerCamelCase_					:				Union[str, Any]              =  model(
							    __lowerCAmelCase ,				attention_mask=__lowerCAmelCase ,				labels=__lowerCAmelCase ,				)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)      )




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Optional[int]              =  self.prepare_config_and_inputs()
							((lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_))					:				Union[str, Any]              =  config_and_inputs
							lowerCamelCase_					:				Any              =  {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_torch
class        lowerCAmelCase__ (					_A,							_A,							unittest.TestCase  ):






						"""simple docstring"""
						__UpperCAmelCase       :						int    	=			(
						    (
						        DistilBertModel,
						        DistilBertForMaskedLM,
						        DistilBertForMultipleChoice,
						        DistilBertForQuestionAnswering,
						        DistilBertForSequenceClassification,
						        DistilBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						__UpperCAmelCase       :						Dict    	=			(
						    {
						        "feature-extraction": DistilBertModel,
						        "fill-mask": DistilBertForMaskedLM,
						        "question-answering": DistilBertForQuestionAnswering,
						        "text-classification": DistilBertForSequenceClassification,
						        "token-classification": DistilBertForTokenClassification,
						        "zero-shot": DistilBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCAmelCase       :						int    	=			True
						__UpperCAmelCase       :						Union[str, Any]    	=			True
						__UpperCAmelCase       :						Optional[Any]    	=			True
						__UpperCAmelCase       :						Dict    	=			True




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Any              =  DistilBertModelTester(self      )
							lowerCamelCase_					:				List[str]              =  ConfigTester(self ,				config_class=__lowerCAmelCase ,				dim=37      )




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




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Dict              =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_model(*__lowerCAmelCase      )




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Optional[int]              =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_masked_lm(*__lowerCAmelCase      )




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				int              =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_question_answering(*__lowerCAmelCase      )




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Tuple              =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_sequence_classification(*__lowerCAmelCase      )




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Any              =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_token_classification(*__lowerCAmelCase      )




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Tuple              =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_multiple_choice(*__lowerCAmelCase      )




						@slow
						def 	_UpperCamelCase					(       self      ):
							for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCamelCase_					:				Dict              =  DistilBertModel.from_pretrained(__lowerCAmelCase      )
								self.assertIsNotNone(__lowerCAmelCase      )




						@slow
						@require_torch_gpu
						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_       ,lowerCamelCase_					:				Optional[int]              =  self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								# BertForMultipleChoice behaves incorrectly in JIT environments.
								if model_class == DistilBertForMultipleChoice:
									return

								lowerCamelCase_					:				str              =  True
								lowerCamelCase_					:				Optional[int]              =  model_class(config=__lowerCAmelCase      )

								lowerCamelCase_					:				List[str]              =  self._prepare_for_class(__lowerCAmelCase ,				__lowerCAmelCase      )
								lowerCamelCase_					:				List[Any]              =  torch.jit.trace(
								    __lowerCAmelCase ,				(inputs_dict["input_ids"].to("cpu"      ), inputs_dict["attention_mask"].to("cpu"      ))      )

								with tempfile.TemporaryDirectory() as tmp:
									torch.jit.save(__lowerCAmelCase ,				os.path.join(__lowerCAmelCase ,				"traced_model.pt"      )      )
									lowerCamelCase_					:				int              =  torch.jit.load(os.path.join(__lowerCAmelCase ,				"traced_model.pt"      ) ,				map_location=__lowerCAmelCase      )
									loaded(inputs_dict["input_ids"].to(__lowerCAmelCase      ) ,				inputs_dict["attention_mask"].to(__lowerCAmelCase      )      )





@require_torch
class        lowerCAmelCase__ (					unittest.TestCase  ):






						"""simple docstring"""




						@slow
						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Union[str, Any]              =  DistilBertModel.from_pretrained("distilbert-base-uncased"      )
							lowerCamelCase_					:				List[Any]              =  torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
							lowerCamelCase_					:				int              =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
							with torch.no_grad():
								lowerCamelCase_					:				int              =  model(__lowerCAmelCase ,				attention_mask=__lowerCAmelCase      )[0]
							lowerCamelCase_					:				Dict              =  torch.Size((1, 11, 768)      )
							self.assertEqual(output.shape ,				__lowerCAmelCase      )
							lowerCamelCase_					:				Optional[int]              =  torch.tensor(
							    [[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]      )

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






import itertools
import math

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5	,   int(math.sqrt(_snake_case				) + 1				)	,   6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Dict:
   _A      =       2
   while True:
      if is_prime(_snake_case				):
         yield num
      num += 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 10_001				)  ->     int:
   return next(itertools.islice(prime_generator()	,   nth - 1	,   _snake_case				)				)


if __name__ == "__main__":
   print(f'{solution() = }')



