


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A			=						logging.get_logger(__name__)

_A			=						{
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple              =  "blip_2_vision_model"






    def __init__(      self ,       A_=1408 ,       A_=6144 ,       A_=39 ,       A_=16 ,       A_=224 ,       A_=14 ,       A_="gelu" ,       A_=0.0_0001 ,       A_=0.0 ,       A_=1E-10 ,       A_=True ,       **A_ ,       )  ->	Union[str, Any]:
         super().__init__(**A_   )

         __UpperCamelCase   							=hidden_size
         __UpperCamelCase   							=intermediate_size
         __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
         __UpperCamelCase   							=qkv_bias






    @classmethod
    def _a (      cls ,       A_ ,       **A_   )  ->	"PretrainedConfig":
         cls._set_token_in_kwargs(A_   )

         __UpperCamelCase							,				__UpperCamelCase   							=cls.get_config_dict(A_ ,       **A_   )

         # get the vision config dict if we are loading from Blip2Config
         if config_dict.get('model_type'   ) == "blip-2":
              __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(A_ ,       **A_   )






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  "blip_2_qformer"






    def __init__(      self ,       A_=30522 ,       A_=768 ,       A_=12 ,       A_=12 ,       A_=3072 ,       A_="gelu" ,       A_=0.1 ,       A_=0.1 ,       A_=512 ,       A_=0.02 ,       A_=1E-12 ,       A_=0 ,       A_="absolute" ,       A_=2 ,       A_=1408 ,       **A_ ,       )  ->	List[str]:
         super().__init__(pad_token_id=A_ ,       **A_   )

         __UpperCamelCase   							=vocab_size
         __UpperCamelCase   							=hidden_size
         __UpperCamelCase   							=num_hidden_layers
         __UpperCamelCase   							=num_attention_heads
         __UpperCamelCase   							=hidden_act
         __UpperCamelCase   							=intermediate_size
         __UpperCamelCase   							=hidden_dropout_prob
         __UpperCamelCase   							=attention_probs_dropout_prob
         __UpperCamelCase   							=max_position_embeddings
         __UpperCamelCase   							=initializer_range
         __UpperCamelCase   							=layer_norm_eps
         __UpperCamelCase   							=position_embedding_type
         __UpperCamelCase   							=cross_attention_frequency
         __UpperCamelCase   							=encoder_hidden_size






    @classmethod
    def _a (      cls ,       A_ ,       **A_   )  ->	"PretrainedConfig":
         cls._set_token_in_kwargs(A_   )

         __UpperCamelCase							,				__UpperCamelCase   							=cls.get_config_dict(A_ ,       **A_   )

         # get the qformer config dict if we are loading from Blip2Config
         if config_dict.get('model_type'   ) == "blip-2":
              __UpperCamelCase   							=config_dict['qformer_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(A_ ,       **A_   )






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]              =  "blip-2"
    UpperCAmelCase__						:		Optional[Any]              =  True






    def __init__(      self ,       A_=None ,       A_=None ,       A_=None ,       A_=32 ,       **A_   )  ->	Optional[int]:
         super().__init__(**A_   )

         if vision_config is None:
              __UpperCamelCase   							={}
              logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'   )

         if qformer_config is None:
              __UpperCamelCase   							={}
              logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'   )

         if text_config is None:
              __UpperCamelCase   							={}
              logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'   )

         __UpperCamelCase   							=BlipaVisionConfig(**A_   )
         __UpperCamelCase   							=BlipaQFormerConfig(**A_   )
         __UpperCamelCase   							=text_config['model_type'] if 'model_type' in text_config else 'opt'
         __UpperCamelCase   							=CONFIG_MAPPING[text_model_type](**A_   )

         __UpperCamelCase   							=self.text_config.tie_word_embeddings
         __UpperCamelCase   							=self.text_config.is_encoder_decoder

         __UpperCamelCase   							=num_query_tokens
         __UpperCamelCase   							=self.vision_config.hidden_size
         __UpperCamelCase   							=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         __UpperCamelCase   							=1.0
         __UpperCamelCase   							=0.02






    @classmethod
    def _a (      cls ,       A_ ,       A_ ,       A_ ,       **A_ ,       )  ->	List[str]:
         return cls(
             vision_config=vision_config.to_dict() ,       qformer_config=qformer_config.to_dict() ,       text_config=text_config.to_dict() ,       **A_ ,       )






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=copy.deepcopy(self.__dict__   )
         __UpperCamelCase   							=self.vision_config.to_dict()
         __UpperCamelCase   							=self.qformer_config.to_dict()
         __UpperCamelCase   							=self.text_config.to_dict()
         __UpperCamelCase   							=self.__class__.model_type
         return output









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A			=						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  ["pixel_values"]






    def __init__(      self ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BICUBIC ,       A_ = True ,       A_ = None ,       A_ = True ,       A_ = 1 / 255 ,       A_ = True ,       A_ = None ,       A_ = None ,       A_ = True ,       **A_ ,       )  ->	None:
         super().__init__(**A_   )
         __UpperCamelCase   							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         __UpperCamelCase   							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_ ,       param_name='crop_size'   )

         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_center_crop
         __UpperCamelCase   							=crop_size
         __UpperCamelCase   							=do_rescale
         __UpperCamelCase   							=rescale_factor
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __UpperCamelCase   							=image_std if image_std is not None else OPENAI_CLIP_STD
         __UpperCamelCase   							=do_convert_rgb






    def _a (      self ,       A_ ,       A_ ,       A_ = PILImageResampling.BICUBIC ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
         __UpperCamelCase   							=get_resize_output_image_size(A_ ,       size=size['shortest_edge'] ,       default_to_square=A_   )
         return resize(A_ ,       size=A_ ,       resample=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
         return center_crop(A_ ,       size=(size['height'], size['width']) ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	Union[str, Any]:
         return rescale(A_ ,       scale=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         return normalize(A_ ,       mean=A_ ,       std=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='size' ,       default_to_square=A_   )
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase   							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='crop_size' ,       default_to_square=A_   )
         __UpperCamelCase   							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase   							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase   							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase   							=image_std if image_std is not None else self.image_std
         __UpperCamelCase   							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __UpperCamelCase   							=make_list_of_images(A_   )

         if not valid_images(A_   ):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.'   )

         if do_resize and size is None:
              raise ValueError('Size must be specified if do_resize is True.'   )

         if do_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop is True.'   )

         if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              __UpperCamelCase   							=[convert_to_rgb(A_   ) for image in images]

         # All transformations expect numpy arrays.
         __UpperCamelCase   							=[to_numpy_array(A_   ) for image in images]

         if do_resize:
              __UpperCamelCase   							=[self.resize(image=A_ ,       size=A_ ,       resample=A_   ) for image in images]

         if do_center_crop:
              __UpperCamelCase   							=[self.center_crop(image=A_ ,       size=A_   ) for image in images]

         if do_rescale:
              __UpperCamelCase   							=[self.rescale(image=A_ ,       scale=A_   ) for image in images]

         if do_normalize:
              __UpperCamelCase   							=[self.normalize(image=A_ ,       mean=A_ ,       std=A_   ) for image in images]

         __UpperCamelCase   							=[to_channel_dimension_format(A_ ,       A_   ) for image in images]

         __UpperCamelCase   							={'pixel_values': images}
         return BatchFeature(data=A_ ,       tensor_type=A_   )





