



'''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


__lowerCAmelCase									=   logging.get_logger(__name__)

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

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Tuple   				=					'blip_2_vision_model'







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       List[Any]=1408	,_UpperCAmelCase					:       Optional[int]=6144	,_UpperCAmelCase					:       List[str]=39	,_UpperCAmelCase					:       Dict=16	,_UpperCAmelCase					:       Optional[Any]=224	,_UpperCAmelCase					:       Any=14	,_UpperCAmelCase					:       Optional[int]="gelu"	,_UpperCAmelCase					:       Optional[int]=0.0_00_01	,_UpperCAmelCase					:       Tuple=0.0	,_UpperCAmelCase					:       Union[str, Any]=1E-10	,_UpperCAmelCase					:       Any=True	,**_UpperCAmelCase					:       Dict	,):
									super().__init__(**_UpperCAmelCase		)

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







							@classmethod
							def 							__lowercase			(							cls					:       Tuple	,_UpperCAmelCase					:       Union[str, os.PathLike]	,**_UpperCAmelCase					:       Any		):
									cls._set_token_in_kwargs(_UpperCAmelCase		)

									_a       ,					_a     :      List[Any]        =							cls.get_config_dict(_UpperCAmelCase	,**_UpperCAmelCase		)

									# get the vision config dict if we are loading from Blip2Config
									if config_dict.get('model_type'		) == "blip-2":
											_a     :      Union[str, Any]        =							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(_UpperCAmelCase	,**_UpperCAmelCase		)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Tuple   				=					'blip_2_qformer'







							def __init__(							self					:       int	,_UpperCAmelCase					:       List[str]=30522	,_UpperCAmelCase					:       Dict=768	,_UpperCAmelCase					:       Tuple=12	,_UpperCAmelCase					:       Any=12	,_UpperCAmelCase					:       int=3072	,_UpperCAmelCase					:       str="gelu"	,_UpperCAmelCase					:       Dict=0.1	,_UpperCAmelCase					:       str=0.1	,_UpperCAmelCase					:       int=512	,_UpperCAmelCase					:       Union[str, Any]=0.02	,_UpperCAmelCase					:       List[str]=1E-12	,_UpperCAmelCase					:       int=0	,_UpperCAmelCase					:       Optional[int]="absolute"	,_UpperCAmelCase					:       List[str]=2	,_UpperCAmelCase					:       Any=1408	,**_UpperCAmelCase					:       Optional[int]	,):
									super().__init__(pad_token_id=_UpperCAmelCase	,**_UpperCAmelCase		)

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







							@classmethod
							def 							__lowercase			(							cls					:       Dict	,_UpperCAmelCase					:       Union[str, os.PathLike]	,**_UpperCAmelCase					:       Union[str, Any]		):
									cls._set_token_in_kwargs(_UpperCAmelCase		)

									_a       ,					_a     :      str        =							cls.get_config_dict(_UpperCAmelCase	,**_UpperCAmelCase		)

									# get the qformer config dict if we are loading from Blip2Config
									if config_dict.get('model_type'		) == "blip-2":
											_a     :      Any        =							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(_UpperCAmelCase	,**_UpperCAmelCase		)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Tuple   				=					'blip-2'
							lowerCAmelCase       :    Any   				=					True







							def __init__(							self					:       Any	,_UpperCAmelCase					:       Union[str, Any]=None	,_UpperCAmelCase					:       Tuple=None	,_UpperCAmelCase					:       Dict=None	,_UpperCAmelCase					:       int=32	,**_UpperCAmelCase					:       str		):
									super().__init__(**_UpperCAmelCase		)

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

									if qformer_config is None:
											_a     :      List[str]        =							{}
											logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'		)

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

									_a     :      Optional[Any]        =							BlipaVisionConfig(**_UpperCAmelCase		)
									_a     :      Tuple        =							BlipaQFormerConfig(**_UpperCAmelCase		)
									_a     :      Optional[int]        =							text_config['model_type'] if 'model_type' in text_config else 'opt'
									_a     :      Any        =							CONFIG_MAPPING[text_model_type](**_UpperCAmelCase		)

									_a     :      Dict        =							self.text_config.tie_word_embeddings
									_a     :      Union[str, Any]        =							self.text_config.is_encoder_decoder

									_a     :      int        =							num_query_tokens
									_a     :      str        =							self.vision_config.hidden_size
									_a     :      Optional[Any]        =							self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									_a     :      Optional[int]        =							1.0
									_a     :      str        =							0.02







							@classmethod
							def 							__lowercase			(							cls					:       str	,_UpperCAmelCase					:       BlipaVisionConfig	,_UpperCAmelCase					:       BlipaQFormerConfig	,_UpperCAmelCase					:       PretrainedConfig	,**_UpperCAmelCase					:       List[Any]	,):
									return cls(
									    vision_config=vision_config.to_dict()	,qformer_config=qformer_config.to_dict()	,text_config=text_config.to_dict()	,**_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       int		):
									_a     :      Dict        =							copy.deepcopy(self.__dict__		)
									_a     :      Any        =							self.vision_config.to_dict()
									_a     :      Optional[int]        =							self.qformer_config.to_dict()
									_a     :      Dict        =							self.text_config.to_dict()
									_a     :      Optional[Any]        =							self.__class__.model_type
									return output









'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




