


'''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 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__=12 ,			__magic_name__=7 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=99 ,			__magic_name__=32 ,			__magic_name__=32 ,			__magic_name__=2 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=5_12 ,			__magic_name__=0.0_2 ,			__magic_name__=0 ,			__magic_name__=None ,			)   ->							Any:
      _a      =							parent
      _a      =							batch_size
      _a      =							seq_length
      _a      =							is_training
      _a      =							use_input_mask
      _a      =							use_labels
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							projection_dim
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							dropout
      _a      =							attention_dropout
      _a      =							max_position_embeddings
      _a      =							initializer_range
      _a      =							scope
      _a      =							bos_token_id
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _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]  )

      if input_mask is not None:
           _a      =							input_mask.numpy()
           _a ,				_a      =							input_mask.shape
           _a      =							np.random.randint(1 ,			seq_length - 1 ,			size=(batch_size,)  )
           for batch_idx, start_index in enumerate(_lowerCamelCase  ):
                _a      =							1
                _a      =							0

      _a      =							self.get_config()

      return config, input_ids, tf.convert_to_tensor(_lowerCamelCase  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      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 ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							str:
      _a      =							TFBlipTextModel(config=_lowerCamelCase  )
      _a      =							model(_lowerCamelCase ,			attention_mask=_lowerCamelCase ,			training=_lowerCamelCase  )
      _a      =							model(_lowerCamelCase ,			training=_lowerCamelCase  )
      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]:
      _a      =							self.prepare_config_and_inputs()
      _a ,				_a ,				_a      =							config_and_inputs
      _a      =							{'input_ids': input_ids, 'attention_mask': input_mask}
      return config, inputs_dict





@require_tf
class 	a       (       A_					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(TFBlipTextModel,) if is_tf_available() else ()
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							BlipTextModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=_lowerCamelCase ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      pass
 @unittest.skip(reason='Blip does not use inputs_embeds'  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      pass
 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      pass
 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      pass
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							TFBlipTextModel.from_pretrained(_lowerCamelCase  )
           self.assertIsNotNone(_lowerCamelCase  )




 def 							__UpperCAmelCase						(	self ,			__magic_name__=True  )   ->							List[Any]:
      super().test_pt_tf_model_equivalence(allow_missing_keys=_lowerCamelCase  )





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

