






'''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      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							Union[str, Any]					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Optional[int]=12					,      __lowercase	:							Any=7					,      __lowercase	:							int=True					,      __lowercase	:							Optional[int]=True					,      __lowercase	:							Optional[Any]=True					,      __lowercase	:							Union[str, Any]=99					,      __lowercase	:							int=32					,      __lowercase	:							Dict=32					,      __lowercase	:							int=2					,      __lowercase	:							List[Any]=4					,      __lowercase	:							Any=37					,      __lowercase	:							Union[str, Any]=0.1					,      __lowercase	:							str=0.1					,      __lowercase	:							Dict=5_12					,      __lowercase	:							Optional[Any]=0.02					,      __lowercase	:							str=0					,      __lowercase	:							Optional[int]=None					,      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				str									=parent
       SCREAMING_SNAKE_CASE__     :				Any									=batch_size
       SCREAMING_SNAKE_CASE__     :				str									=seq_length
       SCREAMING_SNAKE_CASE__     :				str									=is_training
       SCREAMING_SNAKE_CASE__     :				int									=use_input_mask
       SCREAMING_SNAKE_CASE__     :				Tuple									=use_labels
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=hidden_size
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=projection_dim
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				List[str]									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=dropout
       SCREAMING_SNAKE_CASE__     :				List[str]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				Dict									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=scope
       SCREAMING_SNAKE_CASE__     :				List[str]									=bos_token_id




 def      __magic_name__	(      self	:							int      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				str									=ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size      )

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

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

       SCREAMING_SNAKE_CASE__     :				str									=self.get_config()

       return config, input_ids, tf.convert_to_tensor(__lowercase      )




 def      __magic_name__	(      self	:							List[str]      )	->						int:
       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      __magic_name__	(      self	:							int					,      __lowercase	:							Any					,      __lowercase	:							Dict					,      __lowercase	:							List[Any]      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFBlipTextModel(config=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=model(__lowercase					,      attention_mask=__lowercase					,      training=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				str									=model(__lowercase					,      training=__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      __magic_name__	(      self	:							Any      )	->						str:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[int]									=config_and_inputs
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_tf
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							(TFBlipTextModel,) if is_tf_available() else ()
 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False




 def      __magic_name__	(      self	:							Tuple      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=BlipTextModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=ConfigTester(self					,      config_class=__lowercase					,      hidden_size=37      )




 def      __magic_name__	(      self	:							List[str]      )	->						Union[str, Any]:
       self.config_tester.run_common_tests()




 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Dict:
       pass




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




 @unittest.skip(reason='''Blip does not use inputs_embeds'''      )
 def      __magic_name__	(      self	:							Any      )	->						Optional[int]:
       pass




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




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




 @slow
 def      __magic_name__	(      self	:							str      )	->						str:
       for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=TFBlipTextModel.from_pretrained(__lowercase      )
             self.assertIsNotNone(__lowercase      )




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







'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

a_					= list[list[float | int]]





def  _a(  UpperCamelCase__				:						Matrix,	UpperCamelCase__				:						Matrix							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0 for _ in range(size + 1							)] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				float

      for row in range(UpperCamelCase__							):
            for col in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Tuple									=matrix[row][col]

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=vector[row][0]

      SCREAMING_SNAKE_CASE__     :				Any									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=0
      while row < size and col < size:
            # pivoting
            SCREAMING_SNAKE_CASE__     :				Any									=max((abs(augmented[rowa][col]							), rowa) for rowa in range(UpperCamelCase__,	UpperCamelCase__							)							)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[Any]									=augmented[pivot_row], augmented[row]

            for rowa in range(row + 1,	UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=augmented[rowa][col] / augmented[row][col]
                  SCREAMING_SNAKE_CASE__     :				Tuple									=0
                  for cola in range(col + 1,	size + 1							):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1,	UpperCamelCase__							):
            for row in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=augmented[row][col] / augmented[col][col]
                  for cola in range(UpperCamelCase__,	size + 1							):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row],	1_0							)] for row in range(UpperCamelCase__							)
      ]





def  _a(  UpperCamelCase__				:						list[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0 for _ in range(UpperCamelCase__							)] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				Matrix
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int

      for x_val, y_val in enumerate(UpperCamelCase__							):
            for col in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=(x_val + 1) ** (size - col - 1)
            SCREAMING_SNAKE_CASE__     :				Dict									=y_val

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=solve(UpperCamelCase__,	UpperCamelCase__							)

      def interpolated_func(UpperCamelCase__				:						int							) -> int:
            return sum(
                round(coeffs[x_val][0]							) * (var ** (size - x_val - 1))
                for x_val in range(UpperCamelCase__							)							)

      return interpolated_func





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )





def  _a(  UpperCamelCase__				:						Callable[[int], int] = question_function,	UpperCamelCase__				:						int = 1_0							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				list[int]									=[func(UpperCamelCase__							) for x_val in range(1,	order + 1							)]

      SCREAMING_SNAKE_CASE__     :				list[Callable[[int], int]]									=[
          interpolate(data_points[:max_coeff]							) for max_coeff in range(1,	order + 1							)
      ]

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Callable[[int], int]
      SCREAMING_SNAKE_CASE__     :				int

      for poly in polynomials:
            SCREAMING_SNAKE_CASE__     :				Any									=1
            while func(UpperCamelCase__							) == poly(UpperCamelCase__							):
                  x_val += 1

            ret += poly(UpperCamelCase__							)

      return ret


if __name__ == "__main__":
  print(F'''{solution() = }''')