'''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


__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						"blip_2_vision_model"




      def __init__(       self						:				Optional[Any]						,					A						:				Union[str, Any]=1408						,					A						:				int=6144						,					A						:				Union[str, Any]=39						,					A						:				List[str]=16						,					A						:				List[str]=224						,					A						:				List[Any]=14						,					A						:				int="gelu"						,					A						:				Optional[Any]=0.00_001						,					A						:				str=0.0						,					A						:				List[str]=1E-10						,					A						:				Dict=True						,					**A						:				Optional[Any]						,					):
             super().__init__(**A       )

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





      @classmethod
      def      _A	(       cls						:				Tuple						,					A						:				Union[str, os.PathLike]						,					**A						:				List[str]       ):
             cls._set_token_in_kwargs(A       )

             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    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     :  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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						"blip_2_qformer"




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

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





      @classmethod
      def      _A	(       cls						:				Optional[Any]						,					A						:				Union[str, os.PathLike]						,					**A						:				Optional[int]       ):
             cls._set_token_in_kwargs(A       )

             _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    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     :  List[str]							      =    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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						"blip-2"
      __UpperCamelCase:  List[Any]						=						True




      def __init__(       self						:				str						,					A						:				Optional[Any]=None						,					A						:				List[Any]=None						,					A						:				str=None						,					A						:				Optional[int]=32						,					**A						:				Union[str, Any]       ):
             super().__init__(**A       )

             if vision_config is None:
                    _UpperCAmelCase     :  Dict							      =    {}
                    logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."       )

             if qformer_config is None:
                    _UpperCAmelCase     :  int							      =    {}
                    logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."       )

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

             _UpperCAmelCase     :  Dict							      =    BlipaVisionConfig(**A       )
             _UpperCAmelCase     :  Optional[int]							      =    BlipaQFormerConfig(**A       )
             _UpperCAmelCase     :  str							      =    text_config["model_type"] if "model_type" in text_config else "opt"
             _UpperCAmelCase     :  int							      =    CONFIG_MAPPING[text_model_type](**A       )

             _UpperCAmelCase     :  Any							      =    self.text_config.tie_word_embeddings
             _UpperCAmelCase     :  Any							      =    self.text_config.is_encoder_decoder

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




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





      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    copy.deepcopy(self.__dict__       )
             _UpperCAmelCase     :  List[Any]							      =    self.vision_config.to_dict()
             _UpperCAmelCase     :  Tuple							      =    self.qformer_config.to_dict()
             _UpperCAmelCase     :  Dict							      =    self.text_config.to_dict()
             _UpperCAmelCase     :  Tuple							      =    self.__class__.model_type
             return output







from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
		import PIL

# soft dependency
if is_pytesseract_available():
		import pytesseract

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




def 			__magic_name__				( A		:					Dict,  A		:					int,  A		:					Optional[int]   ):






			'''simple docstring'''







			return [
			    int(1000 * (box[0] / width)   ),
			    int(1000 * (box[1] / height)   ),
			    int(1000 * (box[2] / width)   ),
			    int(1000 * (box[3] / height)   ),
			]




def 			__magic_name__				( A		:					np.ndarray,  A		:					Optional[str],  A		:					Optional[str] = None   ):






			'''simple docstring'''







			a    =			tesseract_config if tesseract_config is not None else ""

			# apply OCR
			a    =			to_pil_image(A   )
			a    ,		a    =			pil_image.size
			a    =			pytesseract.image_to_data(A,  lang=A,  output_type="dict",  config=A   )
			a    ,		a    ,		a    ,		a    ,		a    =			data["text"], data["left"], data["top"], data["width"], data["height"]

			# filter empty words and corresponding coordinates
			a    =			[idx for idx, word in enumerate(A   ) if not word.strip()]
			a    =			[word for idx, word in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			a    =			[]
			for x, y, w, h in zip(A,  A,  A,  A   ):
						a    =			[x, y, x + w, y + h]
						actual_boxes.append(A   )

			# finally, normalize the bounding boxes
			a    =			[]
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(A,  A,  A   )   )

			assert len(A   ) == len(A   ), "Not as many words as there are bounding boxes"

			return words, normalized_boxes






class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					["""pixel_values"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = ""					,      **__lowerCamelCase				:     Tuple					,      )					->     None:
							super().__init__(**__lowerCamelCase							)
							a    =			size if size is not None else {"height": 2_24, "width": 2_24}
							a    =			get_size_dict(__lowerCamelCase							)

							a    =			do_resize
							a    =			size
							a    =			resample
							a    =			apply_ocr
							a    =			ocr_lang
							a    =			tesseract_config





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     np.ndarray					,      __lowerCamelCase				:     Dict[str, int]					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase				:     Optional[int]					,      )					->     np.ndarray:
							a    =			get_size_dict(__lowerCamelCase							)
							if "height" not in size or "width" not in size:
										raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""							)
							a    =			(size["height"], size["width"])
							return resize(__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     ImageInput					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = None					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase				:     ChannelDimension = ChannelDimension.FIRST					,      **__lowerCamelCase				:     Optional[Any]					,      )					->     PIL.Image.Image:
							a    =			do_resize if do_resize is not None else self.do_resize
							a    =			size if size is not None else self.size
							a    =			get_size_dict(__lowerCamelCase							)
							a    =			resample if resample is not None else self.resample
							a    =			apply_ocr if apply_ocr is not None else self.apply_ocr
							a    =			ocr_lang if ocr_lang is not None else self.ocr_lang
							a    =			tesseract_config if tesseract_config is not None else self.tesseract_config

							a    =			make_list_of_images(__lowerCamelCase							)

							if not valid_images(__lowerCamelCase							):
										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."							)

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

							if apply_ocr:
										requires_backends(self					,      "pytesseract"							)
										a    =			[]
										a    =			[]
										for image in images:
													a    ,		a    =			apply_tesseract(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
													words_batch.append(__lowerCamelCase							)
													boxes_batch.append(__lowerCamelCase							)

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

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							a    =			[flip_channel_order(__lowerCamelCase							) for image in images]
							a    =			[to_channel_dimension_format(__lowerCamelCase					,      __lowerCamelCase							) for image in images]

							a    =			BatchFeature(data={"pixel_values": images}					,      tensor_type=__lowerCamelCase							)

							if apply_ocr:
										a    =			words_batch
										a    =			boxes_batch
							return data






