"""simple docstring"""
from functools import lru_cache







@lru_cache
def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
  if num < 0:
    raise ValueError('Number should not be negative.'       )

  return 1 if num in (0, 1) else num * factorial(num - 1       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



"""simple docstring"""
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=12   ,      lowercase=7   ,      lowercase=True   ,      lowercase=True   ,      lowercase=True   ,      lowercase=99   ,      lowercase=32   ,      lowercase=32   ,      lowercase=2   ,      lowercase=4   ,      lowercase=37   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=5_12   ,      lowercase=0.02   ,      lowercase=0   ,      lowercase=None   ,      )     ->      Optional[int]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





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

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

        if input_mask is not None:
          a__:					str      							=     input_mask.numpy()
          a__							,   a__:					Any      							=     input_mask.shape
          a__:					Tuple      							=     np.random.randint(1   ,      seq_length - 1   ,      size=(batch_size,))
          for batch_idx, start_index in enumerate(lowercase):
            a__:					Tuple      							=     1
            a__:					Any      							=     0

        a__:					Any      							=     self.get_config()

        return config, input_ids, tf.convert_to_tensor(lowercase)



      def   lowerCamelCase_       (						self)     ->      Union[str, 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   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Any      							=     TFBlipTextModel(config=lowercase)
        a__:					Union[str, Any]      							=     model(lowercase   ,      attention_mask=lowercase   ,      training=lowercase)
        a__:					List[Any]      							=     model(lowercase   ,      training=lowercase)
        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   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Tuple      							=     self.prepare_config_and_inputs()
        a__							,   a__							,   a__:					Optional[Any]      							=     config_and_inputs
        a__:					Dict      							=     {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		(TFBlipTextModel,) if is_tf_available() else ()
      a__				=		False
      a__				=		False
      a__				=		False



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





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



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



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





        '''simple docstring'''





        a__:					List[str]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        pass



      @unittest.skip(reason='Blip does not use inputs_embeds')
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					Tuple      							=     TFBlipTextModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase=True)     ->      str:





        '''simple docstring'''





        super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase)


