
'''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,
    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


UpperCAmelCase__ :			int											=		logging.get_logger(__name__)






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Union[str, Any]   =  ['pixel_values']





	def __init__(       self      :							Union[str, Any]   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							int = 0.9   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BICUBIC   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							Union[int, float] = 1 / 255   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				**__magic_name__      :							List[Any]   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)
				lowerCAmelCase__										=				size if size is not None else {"shortest_edge": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else {"height": 224, "width": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[float] = None   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BICUBIC   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Optional[int]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				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:
										lowerCAmelCase__										=				int(size["shortest_edge"] / crop_pct				)
							elif "height" in size and "width" in size:
										if size["height"] == size["width"]:
													lowerCAmelCase__										=				int(size["height"] / crop_pct				)
										else:
													lowerCAmelCase__										=				(int(size["height"] / crop_pct				), int(size["width"] / crop_pct				))
							else:
										raise ValueError("Invalid size for resize: {}".format(__magic_name__				)				)

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

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__				)
				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(__magic_name__   ,				size=(size["height"], size["width"])   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[int, float]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							str   ,				):


				"""simple docstring"""

				return rescale(__magic_name__   ,				scale=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				return normalize(__magic_name__   ,				mean=__magic_name__   ,				std=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							ImageInput   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							int = None   ,				__magic_name__      :							PILImageResampling = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							float = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[str, TensorType]] = None   ,				__magic_name__      :							ChannelDimension = ChannelDimension.FIRST   ,				**__magic_name__      :							Any   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				do_resize if do_resize is not None else self.do_resize
				lowerCAmelCase__										=				crop_pct if crop_pct is not None else self.crop_pct
				lowerCAmelCase__										=				resample if resample is not None else self.resample
				lowerCAmelCase__										=				do_center_crop if do_center_crop is not None else self.do_center_crop
				lowerCAmelCase__										=				do_rescale if do_rescale is not None else self.do_rescale
				lowerCAmelCase__										=				rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCAmelCase__										=				do_normalize if do_normalize is not None else self.do_normalize
				lowerCAmelCase__										=				image_mean if image_mean is not None else self.image_mean
				lowerCAmelCase__										=				image_std if image_std is not None else self.image_std

				lowerCAmelCase__										=				size if size is not None else self.size
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else self.crop_size
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)

				lowerCAmelCase__										=				make_list_of_images(__magic_name__				)

				if not valid_images(__magic_name__				):
							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.
				lowerCAmelCase__										=				[to_numpy_array(__magic_name__				) for image in images]

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

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

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

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

				lowerCAmelCase__										=				[to_channel_dimension_format(__magic_name__   ,				__magic_name__				) for image in images]

				lowerCAmelCase__										=				{"pixel_values": images}
				return BatchFeature(data=__magic_name__   ,				tensor_type=__magic_name__				)







"""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
