





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					:     List[str]		, A					:     Optional[int]		, A					:     List[Any]=1_2		, A					:     str=7		, A					:     int=True		, A					:     Optional[Any]=True		, A					:     Dict=True		, A					:     Tuple=9_9		, A					:     Dict=3_2		, A					:     str=3_2		, A					:     int=2		, A					:     str=4		, A					:     Optional[Any]=3_7		, A					:     Tuple=0.1		, A					:     Optional[int]=0.1		, A					:     Tuple=5_1_2		, A					:     Optional[int]=0.02		, A					:     List[str]=0		, A					:     List[str]=None		, )       ->int:
          lowerCamelCase__       :						Optional[Any]        =							parent
          lowerCamelCase__       :						Any        =							batch_size
          lowerCamelCase__       :						int        =							seq_length
          lowerCamelCase__       :						Any        =							is_training
          lowerCamelCase__       :						int        =							use_input_mask
          lowerCamelCase__       :						Optional[Any]        =							use_labels
          lowerCamelCase__       :						List[Any]        =							vocab_size
          lowerCamelCase__       :						Union[str, Any]        =							hidden_size
          lowerCamelCase__       :						Optional[int]        =							projection_dim
          lowerCamelCase__       :						Optional[int]        =							num_hidden_layers
          lowerCamelCase__       :						str        =							num_attention_heads
          lowerCamelCase__       :						List[Any]        =							intermediate_size
          lowerCamelCase__       :						List[Any]        =							dropout
          lowerCamelCase__       :						int        =							attention_dropout
          lowerCamelCase__       :						Union[str, Any]        =							max_position_embeddings
          lowerCamelCase__       :						Dict        =							initializer_range
          lowerCamelCase__       :						List[str]        =							scope
          lowerCamelCase__       :						Tuple        =							bos_token_id







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[int]:
          lowerCamelCase__       :						Any        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

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

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

          lowerCamelCase__       :						str        =							self.get_config()

          return config, input_ids, tf.convert_to_tensor(A      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Dict:
          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					:     Optional[Any]		, A					:     Union[str, Any]		, A					:     Union[str, Any]		, A					:     str      )       ->Dict:
          lowerCamelCase__       :						Union[str, Any]        =							TFBlipTextModel(config=A      )
          lowerCamelCase__       :						Any        =							model(A		, attention_mask=A		, training=A      )
          lowerCamelCase__       :						Dict        =							model(A		, training=A      )
          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					:     Any      )       ->Tuple:
          lowerCamelCase__       :						Optional[Any]        =							self.prepare_config_and_inputs()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							config_and_inputs
          lowerCamelCase__       :						List[Any]        =							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict


@require_tf
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Tuple			   =   (TFBlipTextModel,) if is_tf_available() else ()
       _UpperCAmelCase				:  Optional[int]			   =   False
       _UpperCAmelCase				:  List[str]			   =   False
       _UpperCAmelCase				:  int			   =   False







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          lowerCamelCase__       :						Optional[Any]        =							BlipTextModelTester(self      )
          lowerCamelCase__       :						Any        =							ConfigTester(self		, config_class=A		, hidden_size=3_7      )







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







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[str]:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Union[str, Any]:
          pass







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          pass







       @unittest.skip(reason='''Blip does not use inputs_embeds'''      )
       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[str]:
          pass







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







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







       @slow
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						List[Any]        =							TFBlipTextModel.from_pretrained(A      )
             self.assertIsNotNone(A      )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Tuple=True      )       ->Dict:
          super().test_pt_tf_model_equivalence(allow_missing_keys=A      )










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







   """simple docstring"""
   if deriv:
      return value * (1 - value)
   return 1 / (1 + math.exp(-value					))


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, Any]        =							layer_1_error * sigmoid_function(UpperCAmelCase      ,  UpperCAmelCase					)
      # Update weight
      weight += INITIAL_VALUE * layer_1_delta

   return layer_a * 100


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



