






import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
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 DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
      from transformers.models.dpr.modeling_dpr import (
          DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )
class 					a_:



     """simple docstring"""



     def __init__(						self			:					str       ,       lowerCAmelCase__			:					Optional[int]       ,       lowerCAmelCase__			:					str=1_3       ,       lowerCAmelCase__			:					str=7       ,       lowerCAmelCase__			:					Tuple=False       ,       lowerCAmelCase__			:					str=True       ,       lowerCAmelCase__			:					Dict=True       ,       lowerCAmelCase__			:					List[str]=True       ,       lowerCAmelCase__			:					Union[str, Any]=9_9       ,       lowerCAmelCase__			:					List[str]=3_2       ,       lowerCAmelCase__			:					Tuple=5       ,       lowerCAmelCase__			:					Tuple=4       ,       lowerCAmelCase__			:					List[Any]=3_7       ,       lowerCAmelCase__			:					int="gelu"       ,       lowerCAmelCase__			:					List[str]=0.1       ,       lowerCAmelCase__			:					Union[str, Any]=0.1       ,       lowerCAmelCase__			:					Any=5_1_2       ,       lowerCAmelCase__			:					List[Any]=1_6       ,       lowerCAmelCase__			:					List[Any]=2       ,       lowerCAmelCase__			:					int=0.02       ,       lowerCAmelCase__			:					Union[str, Any]=3       ,       lowerCAmelCase__			:					Optional[int]=4       ,       lowerCAmelCase__			:					str=None       ,       lowerCAmelCase__			:					Union[str, Any]=0       ,       )    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						parent
      SCREAMING_SNAKE_CASE							  =						batch_size
      SCREAMING_SNAKE_CASE							  =						seq_length
      SCREAMING_SNAKE_CASE							  =						is_training
      SCREAMING_SNAKE_CASE							  =						use_input_mask
      SCREAMING_SNAKE_CASE							  =						use_token_type_ids
      SCREAMING_SNAKE_CASE							  =						use_labels
      SCREAMING_SNAKE_CASE							  =						vocab_size
      SCREAMING_SNAKE_CASE							  =						hidden_size
      SCREAMING_SNAKE_CASE							  =						num_hidden_layers
      SCREAMING_SNAKE_CASE							  =						num_attention_heads
      SCREAMING_SNAKE_CASE							  =						intermediate_size
      SCREAMING_SNAKE_CASE							  =						hidden_act
      SCREAMING_SNAKE_CASE							  =						hidden_dropout_prob
      SCREAMING_SNAKE_CASE							  =						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE							  =						max_position_embeddings
      SCREAMING_SNAKE_CASE							  =						type_vocab_size
      SCREAMING_SNAKE_CASE							  =						type_sequence_label_size
      SCREAMING_SNAKE_CASE							  =						initializer_range
      SCREAMING_SNAKE_CASE							  =						num_labels
      SCREAMING_SNAKE_CASE							  =						num_choices
      SCREAMING_SNAKE_CASE							  =						scope
      SCREAMING_SNAKE_CASE							  =						projection_dim





     def   __UpperCamelCase		(						self			:					Tuple)    ->					str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size)

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

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

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

      SCREAMING_SNAKE_CASE							  =						self.get_config()

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





     def   __UpperCamelCase		(						self			:					Dict)    ->					Any:

      """simple docstring"""
      return DPRConfig(
          projection_dim=self.projection_dim       ,       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       ,       )





     def   __UpperCamelCase		(						self			:					Optional[int]       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					List[Any]       ,       lowerCAmelCase__			:					List[Any]       ,       lowerCAmelCase__			:					Optional[int]       ,       lowerCAmelCase__			:					Any       ,       lowerCAmelCase__			:					Optional[int]       ,       lowerCAmelCase__			:					List[str])    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						DPRContextEncoder(config=lowerCAmelCase__)
      model.to(lowerCAmelCase__)
      model.eval()
      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__       ,       attention_mask=lowerCAmelCase__       ,       token_type_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__       ,       token_type_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__)
      self.parent.assertEqual(result.pooler_output.shape       ,       (self.batch_size, self.projection_dim or self.hidden_size))





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Any       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					List[Any]       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					Tuple       ,       lowerCAmelCase__			:					Tuple       ,       lowerCAmelCase__			:					Dict)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						DPRQuestionEncoder(config=lowerCAmelCase__)
      model.to(lowerCAmelCase__)
      model.eval()
      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__       ,       attention_mask=lowerCAmelCase__       ,       token_type_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__       ,       token_type_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__)
      self.parent.assertEqual(result.pooler_output.shape       ,       (self.batch_size, self.projection_dim or self.hidden_size))





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					List[str]       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					Tuple       ,       lowerCAmelCase__			:					Tuple       ,       lowerCAmelCase__			:					Optional[int]       ,       lowerCAmelCase__			:					Any       ,       lowerCAmelCase__			:					Union[str, Any])    ->					Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						DPRReader(config=lowerCAmelCase__)
      model.to(lowerCAmelCase__)
      model.eval()
      SCREAMING_SNAKE_CASE							  =						model(
          lowerCAmelCase__       ,       attention_mask=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))
      self.parent.assertEqual(result.relevance_logits.shape       ,       (self.batch_size,))





     def   __UpperCamelCase		(						self			:					Optional[Any])    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.prepare_config_and_inputs()
      (
          (
          SCREAMING_SNAKE_CASE 
      )  , (
          SCREAMING_SNAKE_CASE 
      )  , (
          SCREAMING_SNAKE_CASE 
      )  , (
          SCREAMING_SNAKE_CASE 
      )  , (
          SCREAMING_SNAKE_CASE 
      )  , (
          SCREAMING_SNAKE_CASE 
      )  , (
          SCREAMING_SNAKE_CASE 
      )  ,  
      )							  =						config_and_inputs
      SCREAMING_SNAKE_CASE							  =						{'input_ids': input_ids}
      return config, inputs_dict

@require_torch
class 					a_(						lowercase__	,    lowercase__	,    unittest.TestCase					):



     """simple docstring"""



     __snake_case						:							List[Any]       			=(
         (
             DPRContextEncoder,
             DPRQuestionEncoder,
             DPRReader,
         )
         if is_torch_available()
         else ()
     )
     __snake_case						:							Optional[int]       			={'''feature-extraction''': DPRQuestionEncoder} if is_torch_available() else {}

     __snake_case						:							str       			=False
     __snake_case						:							Tuple       			=False  # why?
     __snake_case						:							Optional[Any]       			=False
     __snake_case						:							Optional[int]       			=False





     def   __UpperCamelCase		(						self			:					Tuple)    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						DPRModelTester(self)
      SCREAMING_SNAKE_CASE							  =						ConfigTester(self       ,       config_class=lowerCAmelCase__       ,       hidden_size=3_7)





     def   __UpperCamelCase		(						self			:					Tuple)    ->					List[Any]:

      """simple docstring"""
      self.config_tester.run_common_tests()





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_context_encoder(*lowerCAmelCase__)





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_question_encoder(*lowerCAmelCase__)





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_reader(*lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					List[Any])    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.model_tester.prepare_config_and_inputs()[0]

      SCREAMING_SNAKE_CASE							  =						DPRQuestionEncoder(config=lowerCAmelCase__)
      model.to(lowerCAmelCase__)
      model.eval()

      with tempfile.TemporaryDirectory() as tmp_dirname:
       model.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE							  =						DPRQuestionEncoder.from_pretrained(lowerCAmelCase__       ,       projection_dim=5_1_2)

      self.assertIsNotNone(lowerCAmelCase__)





     @slow
     def   __UpperCamelCase		(						self			:					List[Any])    ->					List[str]:

      """simple docstring"""
      for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE							  =						DPRContextEncoder.from_pretrained(lowerCAmelCase__)
       self.assertIsNotNone(lowerCAmelCase__)

      for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE							  =						DPRContextEncoder.from_pretrained(lowerCAmelCase__)
       self.assertIsNotNone(lowerCAmelCase__)

      for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE							  =						DPRQuestionEncoder.from_pretrained(lowerCAmelCase__)
       self.assertIsNotNone(lowerCAmelCase__)

      for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE							  =						DPRReader.from_pretrained(lowerCAmelCase__)
       self.assertIsNotNone(lowerCAmelCase__)

@require_torch
class 					a_(						unittest.TestCase					):



     """simple docstring"""



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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'       ,       return_dict=lowerCAmelCase__)
      model.to(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						torch.tensor(
          [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]       ,       dtype=torch.long       ,       device=lowerCAmelCase__)  # [CLS] hello, is my dog cute? [SEP]
      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__)[0]  # embedding shape = (1, 768)
      # compare the actual values for a slice.
      SCREAMING_SNAKE_CASE							  =						torch.tensor(
          [
              [
                  0.03_23_62_53,
                  0.12_75_33_35,
                  0.16_81_85_09,
                  0.00_27_97_86,
                  0.3_89_69_33,
                  0.24_26_49_45,
                  0.2_17_89_71,
                  -0.02_33_52_27,
                  -0.08_48_19_59,
                  -0.14_32_41_17,
              ]
          ]       ,       dtype=torch.float       ,       device=lowerCAmelCase__       ,       )
      self.assertTrue(torch.allclose(output[:, :1_0]       ,       lowerCAmelCase__       ,       atol=1e-4))





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base')
      SCREAMING_SNAKE_CASE							  =						DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base')
      model.to(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						tokenizer(
          questions='What is love ?'       ,       titles='Haddaway'       ,       texts='What Is Love is a song recorded by the artist Haddaway'       ,       padding=lowerCAmelCase__       ,       return_tensors='pt'       ,       )
      encoded_inputs.to(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						model(**lowerCAmelCase__)

      # compare the actual values for a slice.
      SCREAMING_SNAKE_CASE							  =						torch.tensor(
          [[-10.30_05, -10.77_65, -11.48_72, -11.68_41, -11.93_12, -10.30_02, -9.85_44, -11.73_78, -12.08_21, -10.29_75]]       ,       dtype=torch.float       ,       device=lowerCAmelCase__       ,       )

      SCREAMING_SNAKE_CASE							  =						torch.tensor(
          [[-11.06_84, -11.70_41, -11.53_97, -10.34_65, -10.87_91, -6.84_43, -11.99_59, -11.03_64, -10.00_96, -6.84_05]]       ,       dtype=torch.float       ,       device=lowerCAmelCase__       ,       )
      self.assertTrue(torch.allclose(outputs.start_logits[:, :1_0]       ,       lowerCAmelCase__       ,       atol=1e-4))
      self.assertTrue(torch.allclose(outputs.end_logits[:, :1_0]       ,       lowerCAmelCase__       ,       atol=1e-4))








import math





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   SCREAMING_SNAKE_CASE							  =						int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 SCREAMING_SNAKE_CASE							  =						3

 while i <= int(math.sqrt(lowercase_	)	):
  if n % i == 0:
   count += 1
   while n % i == 0:
    SCREAMING_SNAKE_CASE							  =						int(n / i	)
  SCREAMING_SNAKE_CASE							  =						i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
      __UpperCAmelCase      				=							5_1_2_4_2_1_8_3
      print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
      print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')




