
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 						UpperCAmelCase	:



			def __init__(self	:      Optional[Any]					,		snake_case__	:      Optional[int]					,		snake_case__	:      List[Any]=12					,		snake_case__	:      int=7					,		snake_case__	:      Tuple=True					,		snake_case__	:      Any=True					,		snake_case__	:      Optional[Any]=True					,		snake_case__	:      Union[str, Any]=99					,		snake_case__	:      Tuple=32					,		snake_case__	:      Any=32					,		snake_case__	:      Dict=5					,		snake_case__	:      Union[str, Any]=4					,		snake_case__	:      Union[str, Any]=37					,		snake_case__	:      Tuple=0.1					,		snake_case__	:      List[Any]=0.1					,		snake_case__	:      str=5_12					,		snake_case__	:      List[Any]=0.02					,		snake_case__	:      Optional[Any]=0					,		snake_case__	:      str=None					,		)	->			Optional[int]:


										'''simple docstring'''


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



			def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


										'''simple docstring'''


										snake_case :     Optional[int]           =   ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size				)

										snake_case :     List[Any]           =   None
										if self.use_input_mask:
																	snake_case :     Tuple           =   random_attention_mask([self.batch_size, self.seq_length]				)

										if input_mask is not None:
																	snake_case :     Tuple           =   input_mask.shape
																	snake_case :     List[Any]           =   np.random.randint(1					,		seq_length - 1					,		size=(batch_size,)				)
																	for batch_idx, start_index in enumerate(snake_case__				):
																								snake_case :     Any           =   1
																								snake_case :     Dict           =   0

										snake_case :     int           =   self.get_config()

										return config, input_ids, input_mask



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


										'''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        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		snake_case__	:      int					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      Optional[int]				)	->			Optional[int]:


										'''simple docstring'''


										snake_case :     List[Any]           =   BlipTextModel(config=snake_case__				)
										model.to(snake_case__				)
										model.eval()
										with torch.no_grad():
																	snake_case :     Any           =   model(snake_case__					,		attention_mask=snake_case__				)
																	snake_case :     Any           =   model(snake_case__				)
										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        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			int:


										'''simple docstring'''


										snake_case :     Any           =   self.prepare_config_and_inputs()
										snake_case :     Tuple           =   config_and_inputs
										snake_case :     str           =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict






@require_torch
class 						UpperCAmelCase	(		A_ ,unittest.TestCase				):
			A__      :	Optional[int]		  =  (BlipTextModel,) if is_torch_available() else ()
			A__      :	str		  =  False
			A__      :	List[Any]		  =  False
			A__      :	Any		  =  False



			def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Tuple:


										'''simple docstring'''


										snake_case :     Union[str, Any]           =   BlipTextModelTester(self				)
										snake_case :     Union[str, Any]           =   ConfigTester(self					,		config_class=snake_case__					,		hidden_size=37				)



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


										'''simple docstring'''


										self.config_tester.run_common_tests()



			def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			int:


										'''simple docstring'''


										snake_case :     int           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__				)



			def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Dict:


										'''simple docstring'''


										pass



			def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Dict:


										'''simple docstring'''


										pass



			@unittest.skip(reason="Blip does not use inputs_embeds"				)
			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			List[str]:


										'''simple docstring'''


										pass



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


										'''simple docstring'''


										pass



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


										'''simple docstring'''


										pass



			@slow
			def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			Dict:


										'''simple docstring'''


										for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case :     Union[str, Any]           =   BlipTextModel.from_pretrained(snake_case__				)
																	self.assertIsNotNone(snake_case__				)



			def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Optional[int]:


										'''simple docstring'''


										super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__				)







def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				int       ,      __lowerCamelCase  :				int					):
       if target == 0:
              answer.append(path.copy()					)
       else:
              for index in range(__lowerCamelCase       ,      len(__lowerCamelCase					)					):
                     if target >= candidates[index]:
                            path.append(candidates[index]					)
                            backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      target - candidates[index]       ,      __lowerCamelCase					)
                            path.pop(len(__lowerCamelCase					) - 1					)


def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				int					):
       snake_case :     Optional[Any]           =   []  # type: list[int]
       snake_case :     Tuple           =   []  # type: list[int]
       backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      0					)
       return answer


def     UpperCamelCase      (					):
       print(combination_sum([-8, 2.3, 0]       ,      1					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





