

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  a     :


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







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size		)

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

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

										_UpperCAmelCase				:						str       						=			self.get_config()

										return config, input_ids, input_mask


						def 				_UpperCAmelCase (				self		):







										'''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  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			BlipTextModel(config=lowerCamelCase__		)
										model.to(lowerCamelCase__		)
										model.eval()
										with torch.no_grad():
														_UpperCAmelCase				:						List[str]       						=			model(lowerCamelCase__  ,    attention_mask=lowerCamelCase__		)
														_UpperCAmelCase				:						List[Any]       						=			model(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		):







										'''simple docstring'''


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







@require_torch
class  a     (		A__							,       unittest.TestCase ):
						_lowercase			  =	(BlipTextModel,) if is_torch_available() else ()
						_lowercase			  =	False
						_lowercase			  =	False
						_lowercase			  =	False


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			BlipTextModelTester(self		)
										_UpperCAmelCase				:						List[str]       						=			ConfigTester(self  ,    config_class=lowerCamelCase__  ,    hidden_size=37		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase__		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


						@unittest.skip(reason="Blip does not use inputs_embeds"		)
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


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







										'''simple docstring'''


										pass


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







										'''simple docstring'''


										pass


						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase				:						int       						=			BlipTextModel.from_pretrained(lowerCamelCase__		)
														self.assertIsNotNone(lowerCamelCase__		)





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__		)







"""simple docstring"""
from .stack import Stack



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				bool:
				_lowerCamelCase					     = Stack()
				_lowerCamelCase					     = {'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
				for bracket in parentheses:
								if bracket in bracket_pairs:
												stack.push(lowercase_							)
								elif bracket in (")", "]", "}"):
												if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
																return False
				return stack.is_empty()


if __name__ == "__main__":
			from doctest import testmod

			testmod()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					['''((()))''', '''((())''', '''(()))''']
			print('''Balanced parentheses demonstration:\n''')
			for example in examples:
						__SCREAMING_SNAKE_CASE     :  Any										=					'''''' if balanced_parentheses(example) else '''not '''
						print(F"""{example} is {not_str}balanced""")
