


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 A__     :



  """simple docstring"""



  def __init__( self			:  Any				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  List[str]=12				,				lowerCamelCase__			:  Optional[int]=7				,				lowerCamelCase__			:  int=True				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  str=True				,				lowerCamelCase__			:  Optional[Any]=99				,				lowerCamelCase__			:  List[str]=32				,				lowerCamelCase__			:  str=32				,				lowerCamelCase__			:  int=2				,				lowerCamelCase__			:  List[Any]=4				,				lowerCamelCase__			:  Union[str, Any]=37				,				lowerCamelCase__			:  List[Any]=0.1				,				lowerCamelCase__			:  List[str]=0.1				,				lowerCamelCase__			:  Optional[int]=512				,				lowerCamelCase__			:  Optional[int]=0.02				,				lowerCamelCase__			:  List[str]=0				,				lowerCamelCase__			:  Optional[Any]=None				,				):
        a__	:  List[Any]     =   parent
        a__	:  int     =   batch_size
        a__	:  Any     =   seq_length
        a__	:  str     =   is_training
        a__	:  Optional[Any]     =   use_input_mask
        a__	:  Any     =   use_labels
        a__	:  int     =   vocab_size
        a__	:  Tuple     =   hidden_size
        a__	:  Any     =   projection_dim
        a__	:  str     =   num_hidden_layers
        a__	:  Union[str, Any]     =   num_attention_heads
        a__	:  Optional[Any]     =   intermediate_size
        a__	:  List[str]     =   dropout
        a__	:  Optional[Any]     =   attention_dropout
        a__	:  int     =   max_position_embeddings
        a__	:  List[Any]     =   initializer_range
        a__	:  Tuple     =   scope
        a__	:  str     =   bos_token_id

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Dict     =   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size       )

        a__	:  int     =   None
        if self.use_input_mask:
              a__	:  Optional[int]     =   random_attention_mask([self.batch_size, self.seq_length]       )

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

        a__	:  Dict     =   self.get_config()

        return config, input_ids, tf.convert_to_tensor(_snake_case       )

  def    _UpperCamelCase( self			:  str       ):
        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			:  Tuple				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  List[str]       ):
        a__	:  Union[str, Any]     =   TFBlipTextModel(config=_snake_case       )
        a__	:  Tuple     =   model(_snake_case				,				attention_mask=_snake_case				,				training=_snake_case       )
        a__	:  Any     =   model(_snake_case				,				training=_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    _UpperCamelCase( self			:  int       ):
        a__	:  Tuple     =   self.prepare_config_and_inputs()
        a__,	a__,	a__	:  int     =   config_and_inputs
        a__	:  Any     =   {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_tf
class A__     ( _lowercase				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		(TFBlipTextModel,) if is_tf_available() else ()
  _lowercase									=		False
  _lowercase									=		False
  _lowercase									=		False

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[Any]     =   BlipTextModelTester(self       )
        a__	:  Any     =   ConfigTester(self				,				config_class=_snake_case				,				hidden_size=37       )

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

  def    _UpperCamelCase( self			:  int       ):
        a__	:  int     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_snake_case       )

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

  def    _UpperCamelCase( self			:  List[str]       ):
        pass

  @unittest.skip(reason="Blip does not use inputs_embeds"       )
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        pass

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

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

  @slow
  def    _UpperCamelCase( self			:  List[Any]       ):
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              a__	:  Dict     =   TFBlipTextModel.from_pretrained(_snake_case       )
              self.assertIsNotNone(_snake_case       )

  def    _UpperCamelCase( self			:  List[str]				,				lowerCamelCase__			:  str=True       ):
        super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case       )



"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




