





import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
     import torch

     from transformers import BlipTextModel
     from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase=12			,  UpperCAmelCase=7			,  UpperCAmelCase=True			,  UpperCAmelCase=True			,  UpperCAmelCase=True			,  UpperCAmelCase=99			,  UpperCAmelCase=32			,  UpperCAmelCase=32			,  UpperCAmelCase=5			,  UpperCAmelCase=4			,  UpperCAmelCase=37			,  UpperCAmelCase=0.1			,  UpperCAmelCase=0.1			,  UpperCAmelCase=512			,  UpperCAmelCase=0.02			,  UpperCAmelCase=0			,  UpperCAmelCase=None			,  )    ->				int:






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    parent
        __snake_case				:			List[str]            =    batch_size
        __snake_case				:			Dict            =    seq_length
        __snake_case				:			List[str]            =    is_training
        __snake_case				:			int            =    use_input_mask
        __snake_case				:			Optional[Any]            =    use_labels
        __snake_case				:			Any            =    vocab_size
        __snake_case				:			List[Any]            =    hidden_size
        __snake_case				:			int            =    projection_dim
        __snake_case				:			List[Any]            =    num_hidden_layers
        __snake_case				:			Union[str, Any]            =    num_attention_heads
        __snake_case				:			Optional[Any]            =    intermediate_size
        __snake_case				:			Any            =    dropout
        __snake_case				:			Dict            =    attention_dropout
        __snake_case				:			Any            =    max_position_embeddings
        __snake_case				:			List[str]            =    initializer_range
        __snake_case				:			List[Any]            =    scope
        __snake_case				:			Union[str, Any]            =    bos_token_id







      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        __snake_case				:			List[str]            =    ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size				)

        __snake_case				:			Optional[Any]            =    None
        if self.use_input_mask:
          __snake_case				:			Optional[Any]            =    random_attention_mask([self.batch_size, self.seq_length]				)

        if input_mask is not None:
          __snake_case      ,				__snake_case				:			List[Any]            =    input_mask.shape
          __snake_case				:			int            =    np.random.randint(1			,  seq_length - 1			,  size=(batch_size,)				)
          for batch_idx, start_index in enumerate(UpperCAmelCase				):
            __snake_case				:			Optional[int]            =    1
            __snake_case				:			List[Any]            =    0

        __snake_case				:			Optional[int]            =    self.get_config()

        return config, input_ids, input_mask







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






        '''simple docstring'''






        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   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Union[str, Any]:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    BlipTextModel(config=UpperCAmelCase				)
        model.to(UpperCAmelCase				)
        model.eval()
        with torch.no_grad():
          __snake_case				:			Optional[int]            =    model(UpperCAmelCase			,  attention_mask=UpperCAmelCase				)
          __snake_case				:			Any            =    model(UpperCAmelCase				)
        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   UpperCAmelCase						(	self				)    ->				Union[str, Any]:






        '''simple docstring'''






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




@require_torch
class 			_lowerCamelCase	(      a	,				unittest.TestCase      ):



      """simple docstring"""






      UpperCAmelCase_  : List[str]				      =(BlipTextModel,) if is_torch_available() else ()
      UpperCAmelCase_  : int				      =False
      UpperCAmelCase_  : Optional[Any]				      =False
      UpperCAmelCase_  : str				      =False







      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			int            =    BlipTextModelTester(self				)
        __snake_case				:			List[Any]            =    ConfigTester(self			,  config_class=UpperCAmelCase			,  hidden_size=37				)







      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        self.config_tester.run_common_tests()







      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        pass







      def   UpperCAmelCase						(	self				)    ->				Union[str, Any]:






        '''simple docstring'''






        pass







      @unittest.skip(reason="Blip does not use inputs_embeds"				)
      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        pass







      @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"				)
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        pass







      @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"				)
      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        pass







      @slow
      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __snake_case				:			List[str]            =    BlipTextModel.from_pretrained(UpperCAmelCase				)
          self.assertIsNotNone(UpperCAmelCase				)







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






        '''simple docstring'''






        super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCAmelCase				)








def  lowerCAmelCase__(		lowercase    :      int = 10**6			)	->      int:

  if n <= 0:
    raise ValueError("Please enter an integer greater than 0"			)

  __snake_case				:			List[Any]            =    list(range(n + 1			)			)
  for number in range(2				,       n + 1			):
    if phi[number] == number:
      phi[number] -= 1
      for multiple in range(number * 2				,       n + 1				,       lowercase			):
        __snake_case				:			Optional[int]            =    (phi[multiple] // number) * (number - 1)

  __snake_case				:			List[Any]            =    1
  for number in range(1				,       n + 1			):
    if (answer / phi[answer]) < (number / phi[number]):
      __snake_case				:			List[str]            =    number

  return answer


if __name__ == "__main__":
     print(solution())

