
"""simple docstring"""




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

A__ :       Optional[int]				= logging.get_logger(__name__)




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  return [
      int(1_000 * (box[0] / width)    ),
      int(1_000 * (box[1] / height)    ),
      int(1_000 * (box[2] / width)    ),
      int(1_000 * (box[3] / height)    ),
  ]




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase = None    ):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  tesseract_config if tesseract_config is not None else ''''''

  # apply OCR
  _lowercase:      Union[str, Any]   			=  to_pil_image(_UpperCamelCase    )
  _lowercase			,					_lowercase:      Union[str, Any]   			=  pil_image.size
  _lowercase:      List[str]   			=  pytesseract.image_to_data(_UpperCamelCase			,					lang=_UpperCamelCase			,					output_type='''dict'''			,					config=_UpperCamelCase    )
  _lowercase			,					_lowercase			,					_lowercase			,					_lowercase			,					_lowercase:      Optional[Any]   			=  data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

  # filter empty words and corresponding coordinates
  _lowercase:      Optional[Any]   			=  [idx for idx, word in enumerate(_UpperCamelCase    ) if not word.strip()]
  _lowercase:      List[str]   			=  [word for idx, word in enumerate(_UpperCamelCase    ) if idx not in irrelevant_indices]
  _lowercase:      List[str]   			=  [coord for idx, coord in enumerate(_UpperCamelCase    ) if idx not in irrelevant_indices]
  _lowercase:      List[str]   			=  [coord for idx, coord in enumerate(_UpperCamelCase    ) if idx not in irrelevant_indices]
  _lowercase:      List[str]   			=  [coord for idx, coord in enumerate(_UpperCamelCase    ) if idx not in irrelevant_indices]
  _lowercase:      List[str]   			=  [coord for idx, coord in enumerate(_UpperCamelCase    ) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  _lowercase:      List[str]   			=  []
  for x, y, w, h in zip(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):
    _lowercase:      Any   			=  [x, y, x + w, y + h]
    actual_boxes.append(_UpperCamelCase    )

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

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

  return words, normalized_boxes







class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		['''pixel_values''']
       def __init__(  self			,					A_ = True			,					A_ = None			,					A_ = PILImageResampling.BILINEAR			,					A_ = True			,					A_ = None			,					A_ = ""			,					**A_			,					)  ->						None:





         """simple docstring"""




         super().__init__(**A_							)
         _lowercase:      int   			=  size if size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:      Optional[Any]   			=  get_size_dict(A_							)

         _lowercase:      List[Any]   			=  do_resize
         _lowercase:      str   			=  size
         _lowercase:      Union[str, Any]   			=  resample
         _lowercase:      Union[str, Any]   			=  apply_ocr
         _lowercase:      Tuple   			=  ocr_lang
         _lowercase:      List[str]   			=  tesseract_config
       def 				lowercase_				(  self			,					A_			,					A_			,					A_ = PILImageResampling.BILINEAR			,					A_ = None			,					**A_			,					)  ->						np.ndarray:





         """simple docstring"""




         _lowercase:      Tuple   			=  get_size_dict(A_							)
         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()}'''							)
         _lowercase:      List[Any]   			=  (size['''height'''], size['''width'''])
         return resize(A_			,					size=A_			,					resample=A_			,					data_format=A_			,					**A_							)



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





         """simple docstring"""




         _lowercase:      Any   			=  do_resize if do_resize is not None else self.do_resize
         _lowercase:      Dict   			=  size if size is not None else self.size
         _lowercase:      List[str]   			=  get_size_dict(A_							)
         _lowercase:      Union[str, Any]   			=  resample if resample is not None else self.resample
         _lowercase:      Tuple   			=  apply_ocr if apply_ocr is not None else self.apply_ocr
         _lowercase:      Dict   			=  ocr_lang if ocr_lang is not None else self.ocr_lang
         _lowercase:      Dict   			=  tesseract_config if tesseract_config is not None else self.tesseract_config

         _lowercase:      List[str]   			=  make_list_of_images(A_							)

         if not valid_images(A_							):
           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.
         _lowercase:      int   			=  [to_numpy_array(A_							) for image in images]

         if apply_ocr:
           requires_backends(self			,					'''pytesseract'''							)
           _lowercase:      List[str]   			=  []
           _lowercase:      Optional[Any]   			=  []
           for image in images:
             _lowercase			,					_lowercase:      str   			=  apply_tesseract(A_			,					A_			,					A_							)
             words_batch.append(A_							)
             boxes_batch.append(A_							)

         if do_resize:
           _lowercase:      List[Any]   			=  [self.resize(image=A_			,					size=A_			,					resample=A_							) for image in images]

         # flip color channels from RGB to BGR (as Detectron2 requires this)
         _lowercase:      Tuple   			=  [flip_channel_order(A_							) for image in images]
         _lowercase:      Dict   			=  [to_channel_dimension_format(A_			,					A_							) for image in images]

         _lowercase:      Union[str, Any]   			=  BatchFeature(data={'''pixel_values''': images}			,					tensor_type=A_							)

         if apply_ocr:
           _lowercase:      Optional[int]   			=  words_batch
           _lowercase:      str   			=  boxes_batch
         return data


"""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
