





"""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

__UpperCamelCase     :						Any						       =  logging.get_logger(__name__)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Union[str, Any]			):
 return [
     int(1000 * (box[0] / width)			),
     int(1000 * (box[1] / height)			),
     int(1000 * (box[2] / width)			),
     int(1000 * (box[3] / height)			),
 ]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    np.ndarray  ,     _UpperCAmelCase					:    Optional[str]  ,     _UpperCAmelCase					:    Optional[str] = None			):
 lowerCAmelCase    				= tesseract_config if tesseract_config is not None else ''

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

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

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

 # finally, normalize the bounding boxes
 lowerCAmelCase    				= []
 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       a							(   a__  ):
 snake_case__             =       ['''pixel_values''']



 def __init__(     self				,       _snake_case = True				,       _snake_case = None				,       _snake_case = PILImageResampling.BILINEAR				,       _snake_case = True				,       _snake_case = None				,       _snake_case = ""				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )
  lowerCAmelCase    				= size if size is not None else {'height': 2_24, 'width': 2_24}
  lowerCAmelCase    				= get_size_dict(_snake_case     )

  lowerCAmelCase    				= do_resize
  lowerCAmelCase    				= size
  lowerCAmelCase    				= resample
  lowerCAmelCase    				= apply_ocr
  lowerCAmelCase    				= ocr_lang
  lowerCAmelCase    				= tesseract_config



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case = PILImageResampling.BILINEAR				,       _snake_case = None				,       **_snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= get_size_dict(_snake_case     )
  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()}'     )
  lowerCAmelCase    				= (size['height'], size['width'])
  return resize(_snake_case				,       size=_snake_case				,       resample=_snake_case				,       data_format=_snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     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 = ChannelDimension.FIRST				,       **_snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= do_resize if do_resize is not None else self.do_resize
  lowerCAmelCase    				= size if size is not None else self.size
  lowerCAmelCase    				= get_size_dict(_snake_case     )
  lowerCAmelCase    				= resample if resample is not None else self.resample
  lowerCAmelCase    				= apply_ocr if apply_ocr is not None else self.apply_ocr
  lowerCAmelCase    				= ocr_lang if ocr_lang is not None else self.ocr_lang
  lowerCAmelCase    				= tesseract_config if tesseract_config is not None else self.tesseract_config

  lowerCAmelCase    				= 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:
   raise ValueError('Size must be specified if do_resize is True.'     )

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

  if apply_ocr:
   requires_backends(self				,       'pytesseract'     )
   lowerCAmelCase    				= []
   lowerCAmelCase    				= []
   for image in images:
    lowerCAmelCase				,lowerCAmelCase    				= apply_tesseract(_snake_case				,       _snake_case				,       _snake_case     )
    words_batch.append(_snake_case     )
    boxes_batch.append(_snake_case     )

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

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

  lowerCAmelCase    				= BatchFeature(data={'pixel_values': images}				,       tensor_type=_snake_case     )

  if apply_ocr:
   lowerCAmelCase    				= words_batch
   lowerCAmelCase    				= boxes_batch
  return data












"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     :						Union[str, Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
    '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
    '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
    '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
    '''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
    '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
    '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
    '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
    '''bert-large-uncased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
    '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
    '''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-cased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-uncased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
    ),
    '''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class       a							(   a__  ):
 snake_case__             =       '''bert'''



 def __init__(     self				,       _snake_case=3_05_22				,       _snake_case=7_68				,       _snake_case=12				,       _snake_case=12				,       _snake_case=30_72				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=0				,       _snake_case="absolute"				,       _snake_case=True				,       _snake_case=None				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case				,       **_snake_case     )

  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= position_embedding_type
  lowerCAmelCase    				= use_cache
  lowerCAmelCase    				= classifier_dropout







class       a							(   a__  ):



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self.task == "multiple-choice":
   lowerCAmelCase    				= {0: 'batch', 1: 'choice', 2: 'sequence'}
  else:
   lowerCAmelCase    				= {0: 'batch', 1: 'sequence'}
  return OrderedDict(
      [
          ('input_ids', dynamic_axis),
          ('attention_mask', dynamic_axis),
          ('token_type_ids', dynamic_axis),
      ]     )





