

'''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 					_snake_case		:






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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size     )

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

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

				UpperCAmelCase_     :      int						=  self.get_config()

				return config, input_ids, tf.convert_to_tensor(_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				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			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  TFBlipTextModel(config=_snake_case     )
				UpperCAmelCase_     :      Tuple						=  model(_snake_case			,attention_mask=_snake_case			,training=_snake_case     )
				UpperCAmelCase_     :      int						=  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     ):
				UpperCAmelCase_     :      Optional[Any]						=  self.prepare_config_and_inputs()
				UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      Tuple						=  config_and_inputs
				UpperCAmelCase_     :      List[str]						=  {"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict




@require_tf
class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	Optional[Any]        =(TFBlipTextModel,) if is_tf_available() else ()
			__A  :	Dict        =False
			__A  :	Optional[Any]        =False
			__A  :	Union[str, Any]        =False






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  BlipTextModelTester(self     )
				UpperCAmelCase_     :      Dict						=  ConfigTester(self			,config_class=_snake_case			,hidden_size=37     )






			def 		UpperCamelCase__							(  self     ):
				self.config_tester.run_common_tests()






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






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






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






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






			@slow
			def 		UpperCamelCase__							(  self     ):
				for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_     :      Any						=  TFBlipTextModel.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case=True     ):
				super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case     )










"""simple docstring"""

import math






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * power_factor






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

