

'''simple docstring'''





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


UpperCamelCase_    						=       logging.get_logger(__name__)


if is_vision_available():
							import PIL





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		Dict		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Dict[str, int] = None		,					UpperCAmelCase__     :		PILImageResampling = PILImageResampling.BICUBIC		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Dict[str, int] = None		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Optional[Union[float, List[float]]] = None		,					UpperCAmelCase__     :		Optional[Union[float, List[float]]] = None		,					UpperCAmelCase__     :		bool = True		,					**UpperCAmelCase__     :		Any		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )
										lowercase :							Tuple    		=size if size is not None else {'''shortest_edge''': 224}
										lowercase :							List[str]    		=get_size_dict(UpperCAmelCase__		,					default_to_square=UpperCAmelCase__   )
										lowercase :							Dict    		=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										lowercase :							Tuple    		=get_size_dict(UpperCAmelCase__		,					default_to_square=UpperCAmelCase__		,					param_name='''crop_size'''   )

										lowercase :							Dict    		=do_resize
										lowercase :							Tuple    		=size
										lowercase :							List[Any]    		=resample
										lowercase :							Any    		=do_center_crop
										lowercase :							Optional[Any]    		=crop_size
										lowercase :							List[Any]    		=do_rescale
										lowercase :							List[str]    		=rescale_factor
										lowercase :							List[Any]    		=do_normalize
										lowercase :							Tuple    		=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowercase :							Optional[int]    		=image_std if image_std is not None else OPENAI_CLIP_STD
										lowercase :							List[Any]    		=do_convert_rgb







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Dict[str, int]		,					UpperCAmelCase__     :		PILImageResampling = PILImageResampling.BICUBIC		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Tuple		,					):
										'''simple docstring'''
										lowercase :							Any    		=get_size_dict(UpperCAmelCase__		,					default_to_square=UpperCAmelCase__   )
										if "shortest_edge" not in size:
																raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
										lowercase :							List[str]    		=get_resize_output_image_size(UpperCAmelCase__		,					size=size['''shortest_edge''']		,					default_to_square=UpperCAmelCase__   )
										return resize(UpperCAmelCase__		,					size=UpperCAmelCase__		,					resample=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Dict[str, int]		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		List[Any]		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=get_size_dict(UpperCAmelCase__   )
										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(UpperCAmelCase__		,					size=(size['''height'''], size['''width'''])		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Union[int, float]		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Any		,					):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Union[float, List[float]]		,					UpperCAmelCase__     :		Union[float, List[float]]		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Union[str, Any]		,					):
										'''simple docstring'''
										return normalize(UpperCAmelCase__		,					mean=UpperCAmelCase__		,					std=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		bool = None		,					UpperCAmelCase__     :		Dict[str, int] = None		,					UpperCAmelCase__     :		PILImageResampling = None		,					UpperCAmelCase__     :		bool = None		,					UpperCAmelCase__     :		int = None		,					UpperCAmelCase__     :		bool = None		,					UpperCAmelCase__     :		float = None		,					UpperCAmelCase__     :		bool = None		,					UpperCAmelCase__     :		Optional[Union[float, List[float]]] = None		,					UpperCAmelCase__     :		Optional[Union[float, List[float]]] = None		,					UpperCAmelCase__     :		bool = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Optional[ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=do_resize if do_resize is not None else self.do_resize
										lowercase :							Optional[int]    		=size if size is not None else self.size
										lowercase :							List[str]    		=get_size_dict(UpperCAmelCase__		,					param_name='''size'''		,					default_to_square=UpperCAmelCase__   )
										lowercase :							int    		=resample if resample is not None else self.resample
										lowercase :							Optional[int]    		=do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase :							Optional[Any]    		=crop_size if crop_size is not None else self.crop_size
										lowercase :							int    		=get_size_dict(UpperCAmelCase__		,					param_name='''crop_size'''		,					default_to_square=UpperCAmelCase__   )
										lowercase :							Dict    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							List[str]    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							Union[str, Any]    		=do_normalize if do_normalize is not None else self.do_normalize
										lowercase :							Union[str, Any]    		=image_mean if image_mean is not None else self.image_mean
										lowercase :							List[str]    		=image_std if image_std is not None else self.image_std
										lowercase :							Dict    		=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowercase :							Any    		=make_list_of_images(UpperCAmelCase__   )

										if not valid_images(UpperCAmelCase__   ):
																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:
																lowercase :							Tuple    		=[convert_to_rgb(UpperCAmelCase__   ) for image in images]

										# All transformations expect numpy arrays.
										lowercase :							Any    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_resize:
																lowercase :							Dict    		=[self.resize(image=UpperCAmelCase__		,					size=UpperCAmelCase__		,					resample=UpperCAmelCase__   ) for image in images]

										if do_center_crop:
																lowercase :							Optional[int]    		=[self.center_crop(image=UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							int    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_normalize:
																lowercase :							str    		=[self.normalize(image=UpperCAmelCase__		,					mean=UpperCAmelCase__		,					std=UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )







"""simple docstring"""




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__ :       List[Any]				= logging.get_logger(__name__)

A__ :       Tuple				= {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_vision_model'''
       def __init__(  self			,					A_=1408			,					A_=6144			,					A_=39			,					A_=16			,					A_=224			,					A_=14			,					A_="gelu"			,					A_=1E-6			,					A_=0.0			,					A_=1E-10			,					A_=True			,					**A_			,					)  ->						Tuple:





         """simple docstring"""




         super().__init__(**A_							)

         _lowercase:      List[str]   			=  hidden_size
         _lowercase:      Tuple   			=  intermediate_size
         _lowercase:      List[str]   			=  num_hidden_layers
         _lowercase:      Union[str, Any]   			=  num_attention_heads
         _lowercase:      str   			=  patch_size
         _lowercase:      Union[str, Any]   			=  image_size
         _lowercase:      int   			=  initializer_range
         _lowercase:      List[str]   			=  attention_dropout
         _lowercase:      Tuple   			=  layer_norm_eps
         _lowercase:      Tuple   			=  hidden_act
         _lowercase:      Optional[int]   			=  qkv_bias



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      str   			=  cls.get_config_dict(A_			,					**A_							)

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Dict   			=  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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_qformer'''
       def __init__(  self			,					A_=3_0522			,					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_			,					)  ->						Optional[int]:





         """simple docstring"""




         super().__init__(pad_token_id=A_			,					**A_							)

         _lowercase:      Optional[Any]   			=  vocab_size
         _lowercase:      Optional[int]   			=  hidden_size
         _lowercase:      Dict   			=  num_hidden_layers
         _lowercase:      Union[str, Any]   			=  num_attention_heads
         _lowercase:      List[Any]   			=  hidden_act
         _lowercase:      Union[str, Any]   			=  intermediate_size
         _lowercase:      Union[str, Any]   			=  hidden_dropout_prob
         _lowercase:      Tuple   			=  attention_probs_dropout_prob
         _lowercase:      Optional[int]   			=  max_position_embeddings
         _lowercase:      Optional[int]   			=  initializer_range
         _lowercase:      Optional[Any]   			=  layer_norm_eps
         _lowercase:      Optional[Any]   			=  position_embedding_type
         _lowercase:      Tuple   			=  cross_attention_frequency
         _lowercase:      str   			=  encoder_hidden_size



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      Tuple   			=  cls.get_config_dict(A_			,					**A_							)

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Tuple   			=  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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip'''
       UpperCamelCase_ =		True
       def __init__(  self			,					A_=None			,					A_=None			,					A_=None			,					A_=32			,					**A_							)  ->						str:





         """simple docstring"""




         super().__init__(**A_							)

         if vision_config is None:
           _lowercase:      str   			=  {}
           logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''							)

         if qformer_config is None:
           _lowercase:      List[str]   			=  {}
           logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''							)

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

         _lowercase:      int   			=  InstructBlipVisionConfig(**A_							)
         _lowercase:      Optional[Any]   			=  InstructBlipQFormerConfig(**A_							)
         _lowercase:      Optional[Any]   			=  text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
         _lowercase:      Optional[Any]   			=  CONFIG_MAPPING[text_model_type](**A_							)

         _lowercase:      Tuple   			=  self.text_config.tie_word_embeddings
         _lowercase:      Dict   			=  self.text_config.is_encoder_decoder

         _lowercase:      List[Any]   			=  num_query_tokens
         _lowercase:      Any   			=  self.vision_config.hidden_size
         _lowercase:      List[Any]   			=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         _lowercase:      Union[str, Any]   			=  1.0
         _lowercase:      Dict   			=  0.02
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_			,					A_			,					**A_			,					)  ->						str:





         """simple docstring"""




         return cls(
             vision_config=vision_config.to_dict()			,					qformer_config=qformer_config.to_dict()			,					text_config=text_config.to_dict()			,					**A_			,					)



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      List[str]   			=  copy.deepcopy(self.__dict__							)
         _lowercase:      Tuple   			=  self.vision_config.to_dict()
         _lowercase:      Tuple   			=  self.qformer_config.to_dict()
         _lowercase:      int   			=  self.text_config.to_dict()
         _lowercase:      Any   			=  self.__class__.model_type
         return output
