

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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


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






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['pixel_values']





			def __init__(		self					,  __snake_case = True					,  __snake_case = None					,  __snake_case = 0.9					,  __snake_case = PILImageResampling.BICUBIC					,  __snake_case = True					,  __snake_case = None					,  __snake_case = 1 / 255					,  __snake_case = True					,  __snake_case = True					,  __snake_case = None					,  __snake_case = None					,  **__snake_case					,  )   -> None:




						'''simple docstring'''
						super().__init__(**__snake_case					)
						__a        =size if size is not None else {'shortest_edge': 224}
						__a        =get_size_dict(__snake_case					,  default_to_square=__snake_case					)
						__a        =crop_size if crop_size is not None else {'height': 224, 'width': 224}
						__a        =get_size_dict(__snake_case					,  param_name='crop_size'					)

						__a        =do_resize
						__a        =size
						__a        =crop_pct
						__a        =resample
						__a        =do_center_crop
						__a        =crop_size
						__a        =do_rescale
						__a        =rescale_factor
						__a        =do_normalize
						__a        =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__a        =image_std if image_std is not None else IMAGENET_DEFAULT_STD





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case = None					,  __snake_case = PILImageResampling.BICUBIC					,  __snake_case = None					,  **__snake_case					,  )   -> np.ndarray:




						'''simple docstring'''
						__a        =get_size_dict(__snake_case					,  default_to_square=__snake_case					)
						if "shortest_edge" not in size and ("height" not in size or "width" not in size):
									raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
						if crop_pct is not None:
									if "shortest_edge" in size:
												__a        =int(size['shortest_edge'] / crop_pct					)
									elif "height" in size and "width" in size:
												if size["height"] == size["width"]:
															__a        =int(size['height'] / crop_pct					)
												else:
															__a        =(int(size['height'] / crop_pct					), int(size['width'] / crop_pct					))
									else:
												raise ValueError('Invalid size for resize: {}'.format(__snake_case					)					)

									__a        =get_resize_output_image_size(__snake_case					,  size=__snake_case					,  default_to_square=__snake_case					)
						else:
									if "shortest_edge" in size:
												__a        =get_resize_output_image_size(__snake_case					,  size=size['shortest_edge']					,  default_to_square=__snake_case					)
									elif "height" in size and "width" in size:
												__a        =(size['height'], size['width'])
									else:
												raise ValueError('Invalid size for resize: {}'.format(__snake_case					)					)

						return resize(__snake_case					,  size=__snake_case					,  resample=__snake_case					,  data_format=__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case = None					,  **__snake_case					,  )   -> np.ndarray:




						'''simple docstring'''
						__a        =get_size_dict(__snake_case					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'					)
						return center_crop(__snake_case					,  size=(size['height'], size['width'])					,  data_format=__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case = None					,  **__snake_case					,  )   -> Any:




						'''simple docstring'''
						return rescale(__snake_case					,  scale=__snake_case					,  data_format=__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = None					,  **__snake_case					,  )   -> np.ndarray:




						'''simple docstring'''
						return normalize(__snake_case					,  mean=__snake_case					,  std=__snake_case					,  data_format=__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = ChannelDimension.FIRST					,  **__snake_case					,  )   -> PIL.Image.Image:




						'''simple docstring'''
						__a        =do_resize if do_resize is not None else self.do_resize
						__a        =crop_pct if crop_pct is not None else self.crop_pct
						__a        =resample if resample is not None else self.resample
						__a        =do_center_crop if do_center_crop is not None else self.do_center_crop
						__a        =do_rescale if do_rescale is not None else self.do_rescale
						__a        =rescale_factor if rescale_factor is not None else self.rescale_factor
						__a        =do_normalize if do_normalize is not None else self.do_normalize
						__a        =image_mean if image_mean is not None else self.image_mean
						__a        =image_std if image_std is not None else self.image_std

						__a        =size if size is not None else self.size
						__a        =get_size_dict(__snake_case					,  default_to_square=__snake_case					)
						__a        =crop_size if crop_size is not None else self.crop_size
						__a        =get_size_dict(__snake_case					,  param_name='crop_size'					)

						__a        =make_list_of_images(__snake_case					)

						if not valid_images(__snake_case					):
									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 or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.'					)

						if do_center_crop and crop_pct is None:
									raise ValueError('Crop_pct 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.'					)

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

						if do_resize:
									__a        =[self.resize(image=__snake_case					,  size=__snake_case					,  crop_pct=__snake_case					,  resample=__snake_case					) for image in images]

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

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

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

						__a        =[to_channel_dimension_format(__snake_case					,  __snake_case					) for image in images]

						__a        ={'pixel_values': images}
						return BatchFeature(data=__snake_case					,  tensor_type=__snake_case					)









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


_lowerCAmelCase					:			List[str]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_vision_model'





			def __init__(		self					,  __snake_case=1408					,  __snake_case=6144					,  __snake_case=39					,  __snake_case=16					,  __snake_case=224					,  __snake_case=14					,  __snake_case="gelu"					,  __snake_case=1e-6					,  __snake_case=0.0					,  __snake_case=1e-10					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act
						__a        =qkv_bias





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__a        =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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_qformer'





			def __init__(		self					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=2					,  __snake_case=1408					,  **__snake_case					,  )   -> List[str]:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  **__snake_case					)

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





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__a        =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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip'
			SCREAMING_SNAKE_CASE							   =						True





			def __init__(		self					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=32					,  **__snake_case					)   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

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

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

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

						__a        =InstructBlipVisionConfig(**__snake_case					)
						__a        =InstructBlipQFormerConfig(**__snake_case					)
						__a        =text_config['model_type'] if 'model_type' in text_config else 'opt'
						__a        =CONFIG_MAPPING[text_model_type](**__snake_case					)

						__a        =self.text_config.tie_word_embeddings
						__a        =self.text_config.is_encoder_decoder

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





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					,  )   -> Optional[Any]:




						'''simple docstring'''
						return cls(
						    vision_config=vision_config.to_dict()					,  qformer_config=qformer_config.to_dict()					,  text_config=text_config.to_dict()					,  **__snake_case					,  )





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.qformer_config.to_dict()
						__a        =self.text_config.to_dict()
						__a        =self.__class__.model_type
						return output






