





'''simple docstring'''





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    """Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
    """Salesforce/blip-vqa-capfit-large""": (
        """https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-base""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-large""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
    ),
    """Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
    """Salesforce/blip-itm-large-flikr""": (
        """https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
    ),
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[str]         =   "blip_text_model"

       def __init__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Dict=30524					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=768					,   _SCREAMING_SNAKE_CASE: Optional[int]=768					,   _SCREAMING_SNAKE_CASE: List[str]=3072					,   _SCREAMING_SNAKE_CASE: Tuple=768					,   _SCREAMING_SNAKE_CASE: str=12					,   _SCREAMING_SNAKE_CASE: Optional[Any]=8					,   _SCREAMING_SNAKE_CASE: List[Any]=512					,   _SCREAMING_SNAKE_CASE: List[str]="gelu"					,   _SCREAMING_SNAKE_CASE: Optional[int]=1e-12					,   _SCREAMING_SNAKE_CASE: Tuple=0.0					,   _SCREAMING_SNAKE_CASE: int=0.0					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0.02					,   _SCREAMING_SNAKE_CASE: Tuple=30522					,   _SCREAMING_SNAKE_CASE: Optional[Any]=2					,   _SCREAMING_SNAKE_CASE: Optional[int]=0					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=102					,   _SCREAMING_SNAKE_CASE: int=True					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   **_SCREAMING_SNAKE_CASE: int					,   )       -> List[Any]:




              """simple docstring"""


              super().__init__(
                  pad_token_id=_SCREAMING_SNAKE_CASE					,   bos_token_id=_SCREAMING_SNAKE_CASE					,   eos_token_id=_SCREAMING_SNAKE_CASE					,   sep_token_id=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )

              UpperCamelCase_          =  vocab_size
              UpperCamelCase_          =  hidden_size
              UpperCamelCase_          =  encoder_hidden_size
              UpperCamelCase_          =  intermediate_size
              UpperCamelCase_          =  projection_dim
              UpperCamelCase_          =  hidden_dropout_prob
              UpperCamelCase_          =  num_hidden_layers
              UpperCamelCase_          =  num_attention_heads
              UpperCamelCase_          =  max_position_embeddings
              UpperCamelCase_          =  layer_norm_eps
              UpperCamelCase_          =  hidden_act
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  attention_probs_dropout_prob
              UpperCamelCase_          =  is_decoder
              UpperCamelCase_          =  use_cache



       @classmethod
       def lowercase (		cls: Tuple					,   _SCREAMING_SNAKE_CASE: Union[str, os.PathLike]					,   **_SCREAMING_SNAKE_CASE: Any		)       -> "PretrainedConfig":




              """simple docstring"""


              cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_       , UpperCamelCase_          =  cls.get_config_dict(_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

              # get the text config dict if we are loading from BlipConfig
              if config_dict.get("model_type"		) == "blip":
                     UpperCamelCase_          =  config_dict["text_config"]

              if "model_type" in config_dict and hasattr(cls					,   "model_type"		) and config_dict["model_type"] != cls.model_type:
                     logger.warning(
                         f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                         f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

              return cls.from_dict(_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					int         =   "blip_vision_model"

       def __init__(		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple=768					,   _SCREAMING_SNAKE_CASE: str=3072					,   _SCREAMING_SNAKE_CASE: Dict=512					,   _SCREAMING_SNAKE_CASE: List[Any]=12					,   _SCREAMING_SNAKE_CASE: int=12					,   _SCREAMING_SNAKE_CASE: List[str]=384					,   _SCREAMING_SNAKE_CASE: Optional[Any]=16					,   _SCREAMING_SNAKE_CASE: str="gelu"					,   _SCREAMING_SNAKE_CASE: Dict=1e-5					,   _SCREAMING_SNAKE_CASE: Tuple=0.0					,   _SCREAMING_SNAKE_CASE: Optional[Any]=1e-10					,   **_SCREAMING_SNAKE_CASE: Optional[Any]					,   )       -> Dict:




              """simple docstring"""


              super().__init__(**_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  hidden_size
              UpperCamelCase_          =  intermediate_size
              UpperCamelCase_          =  projection_dim
              UpperCamelCase_          =  num_hidden_layers
              UpperCamelCase_          =  num_attention_heads
              UpperCamelCase_          =  patch_size
              UpperCamelCase_          =  image_size
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  attention_dropout
              UpperCamelCase_          =  layer_norm_eps
              UpperCamelCase_          =  hidden_act



       @classmethod
       def lowercase (		cls: Any					,   _SCREAMING_SNAKE_CASE: Union[str, os.PathLike]					,   **_SCREAMING_SNAKE_CASE: int		)       -> "PretrainedConfig":




              """simple docstring"""


              cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_       , UpperCamelCase_          =  cls.get_config_dict(_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

              # get the vision config dict if we are loading from BlipConfig
              if config_dict.get("model_type"		) == "blip":
                     UpperCamelCase_          =  config_dict["vision_config"]

              if "model_type" in config_dict and hasattr(cls					,   "model_type"		) and config_dict["model_type"] != cls.model_type:
                     logger.warning(
                         f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                         f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

              return cls.from_dict(_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[Any]         =   "blip"
       _UpperCamelCase				:					int         =   True

       def __init__(		self: Any					,   _SCREAMING_SNAKE_CASE: Dict=None					,   _SCREAMING_SNAKE_CASE: Optional[Any]=None					,   _SCREAMING_SNAKE_CASE: int=512					,   _SCREAMING_SNAKE_CASE: str=2.65_92					,   _SCREAMING_SNAKE_CASE: str=256					,   **_SCREAMING_SNAKE_CASE: List[Any]					,   )       -> Any:




              """simple docstring"""


              super().__init__(**_SCREAMING_SNAKE_CASE		)

              if text_config is None:
                     UpperCamelCase_          =  {}
                     logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."		)

              if vision_config is None:
                     UpperCamelCase_          =  {}
                     logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."		)

              UpperCamelCase_          =  BlipTextConfig(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  BlipVisionConfig(**_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.vision_config.hidden_size

              UpperCamelCase_          =  projection_dim
              UpperCamelCase_          =  logit_scale_init_value
              UpperCamelCase_          =  1.0
              UpperCamelCase_          =  0.02
              UpperCamelCase_          =  image_text_hidden_size

       @classmethod
       def lowercase (		cls: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: BlipTextConfig					,   _SCREAMING_SNAKE_CASE: BlipVisionConfig					,   **_SCREAMING_SNAKE_CASE: int		)       -> str:




              """simple docstring"""


              return cls(text_config=text_config.to_dict()					,   vision_config=vision_config.to_dict()					,   **_SCREAMING_SNAKE_CASE		)



       def lowercase (		self: str		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  copy.deepcopy(self.__dict__		)
              UpperCamelCase_          =  self.text_config.to_dict()
              UpperCamelCase_          =  self.vision_config.to_dict()
              UpperCamelCase_          =  self.__class__.model_type
              return output








import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
     # Initialising the CNN
     # (Sequential- Building the model layer by layer)
     _UpperCAmelCase        =				models.Sequential()

     # Step 1 - Convolution
     # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
     # (3,3) is the kernel size (filter matrix)
     classifier.add(
         layers.ConvaD(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

     # Step 2 - Pooling
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (3, 3), activation='relu'))
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Step 3 - Flattening
     classifier.add(layers.Flatten())

     # Step 4 - Full connection
     classifier.add(layers.Dense(units=1_2_8, activation='relu'))
     classifier.add(layers.Dense(units=1, activation='sigmoid'))

     # Compiling the CNN
     classifier.compile(
         optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
     )

     # Part 2 - Fitting the CNN to the images

     # Load Trained model weights

     # from keras.models import load_model
     # regressor=load_model('cnn.h5')

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
     )

     classifier.save('cnn.h5')

     # Part 3 - Making new predictions

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

