
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 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=7  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						__UpperCamelCase=9_9  ,						__UpperCamelCase=3_2  ,						__UpperCamelCase=3_2  ,						__UpperCamelCase=5  ,						__UpperCamelCase=4  ,						__UpperCamelCase=3_7  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=5_1_2  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=0  ,						__UpperCamelCase=None  ,						):




        """simple docstring"""


        UpperCamelCase_											=							parent
        UpperCamelCase_											=							batch_size
        UpperCamelCase_											=							seq_length
        UpperCamelCase_											=							is_training
        UpperCamelCase_											=							use_input_mask
        UpperCamelCase_											=							use_labels
        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							hidden_size
        UpperCamelCase_											=							projection_dim
        UpperCamelCase_											=							num_hidden_layers
        UpperCamelCase_											=							num_attention_heads
        UpperCamelCase_											=							intermediate_size
        UpperCamelCase_											=							dropout
        UpperCamelCase_											=							attention_dropout
        UpperCamelCase_											=							max_position_embeddings
        UpperCamelCase_											=							initializer_range
        UpperCamelCase_											=							scope
        UpperCamelCase_											=							bos_token_id

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size  )

        UpperCamelCase_											=							None
        if self.use_input_mask:
               UpperCamelCase_											=							random_attention_mask([self.batch_size, self.seq_length]  )

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

        UpperCamelCase_											=							self.get_config()

        return config, input_ids, input_mask

 def lowerCamelCase_					(   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 lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							BlipTextModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        with torch.no_grad():
               UpperCamelCase_											=							model(__UpperCamelCase  ,						attention_mask=__UpperCamelCase  )
               UpperCamelCase_											=							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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.prepare_config_and_inputs()
        UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							config_and_inputs
        UpperCamelCase_											=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict



@require_torch
class 		lowercase_							(							__SCREAMING_SNAKE_CASE				,						unittest.TestCase					):
 A__    :					Optional[Any]    =     (BlipTextModel,) if is_torch_available() else ()
 A__    :					str    =     False
 A__    :					Optional[Any]    =     False
 A__    :					Tuple    =     False

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							BlipTextModelTester(self  )
        UpperCamelCase_											=							ConfigTester(self  ,						config_class=__UpperCamelCase  ,						hidden_size=3_7  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.config_tester.run_common_tests()

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

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




        """simple docstring"""


        pass

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




        """simple docstring"""


        pass

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




        """simple docstring"""


        pass

 @slow
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCamelCase_											=							BlipTextModel.from_pretrained(__UpperCamelCase  )
               self.assertIsNotNone(__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCamelCase  )






def        _lowercase  (					a_					:       str       ,a_					:       List[str]					)					->						Optional[Any]:




					'''simple docstring'''







					sequence.append(a_					)

					__magic_name__				=     0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(a_					):
										return None

					return index


if __name__ == "__main__":
						A__							=				input("Enter numbers separated by comma:\n").strip()
						A__							=				[int(item) for item in user_input.split(",")]

						A__							=				input("Enter a single number to be found in the list:\n")
						A__							=				int(target_input)
						A__							=				sentinel_linear_search(sequence, target)
						if result is not None:
												print(f'''{target} found at positions: {result}''')
						else:
												print("Not found")




