

"""simple docstring"""

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       _snake_case  :







						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_=0.0_2  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=None  ,				)      ->    int:
											__lowercase        =      parent
											__lowercase        =      batch_size
											__lowercase        =      seq_length
											__lowercase        =      is_training
											__lowercase        =      use_input_mask
											__lowercase        =      use_labels
											__lowercase        =      vocab_size
											__lowercase        =      hidden_size
											__lowercase        =      projection_dim
											__lowercase        =      num_hidden_layers
											__lowercase        =      num_attention_heads
											__lowercase        =      intermediate_size
											__lowercase        =      dropout
											__lowercase        =      attention_dropout
											__lowercase        =      max_position_embeddings
											__lowercase        =      initializer_range
											__lowercase        =      scope
											__lowercase        =      bos_token_id







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											__lowercase        =      ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size				)

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

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

											__lowercase        =      self.get_config()

											return config, input_ids, input_mask







						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  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      BlipTextModel(config=lowercase_				)
											model.to(lowercase_				)
											model.eval()
											with torch.no_grad():
																__lowercase        =      model(lowercase_  ,				attention_mask=lowercase_				)
																__lowercase        =      model(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 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      self.prepare_config_and_inputs()
											__lowercase        =      config_and_inputs
											__lowercase        =      {"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict



@require_torch
class       _snake_case  (    snake_case__    ,      unittest.TestCase							):
						__snake_case			      =  (BlipTextModel,) if is_torch_available() else ()
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											__lowercase        =      BlipTextModelTester(self				)
											__lowercase        =      ConfigTester(self  ,				config_class=lowercase_  ,				hidden_size=3_7				)







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											self.config_tester.run_common_tests()







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_				)







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											pass







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											pass







						@unittest.skip(reason='''Blip does not use inputs_embeds'''				)
						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				)      ->    List[Any]:
											pass







						@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''				)
						def 							_UpperCamelCase     (self				)      ->    List[str]:
											pass







						@slow
						def 							_UpperCamelCase     (self				)      ->    Dict:
											for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowercase        =      BlipTextModel.from_pretrained(lowercase_				)
																self.assertIsNotNone(lowercase_				)







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase_				)




'''simple docstring'''
import itertools

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE = 1_0_0_0_0_0_0							):
    """simple docstring"""






    snake_case_			:     Optional[int]        =    [1]

    for i in itertools.count(len(__SCREAMING_SNAKE_CASE							)							):
        snake_case_			:     str        =    0
        for j in itertools.count(1							):
            snake_case_			:     List[str]        =    -1 if j % 2 == 0 else +1
            snake_case_			:     str        =    (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(__SCREAMING_SNAKE_CASE							)

    return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




