




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__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					return [
					    int(10_00 * (box[0] / width)     ),
					    int(10_00 * (box[1] / height)     ),
					    int(10_00 * (box[2] / width)     ),
					    int(10_00 * (box[3] / height)     ),
					]







def 	_a      (						SCREAMING_SNAKE_CASE_							:				np.ndarray      ,      SCREAMING_SNAKE_CASE_							:				Optional[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[str] = None     ):
					__lowerCAmelCase				   =       tesseract_config if tesseract_config is not None else """"""

					# apply OCR
					__lowerCAmelCase				   =       to_pil_image(_UpperCAmelCase     )
					__lowerCAmelCase				   =       pil_image.size
					__lowerCAmelCase				   =       pytesseract.image_to_data(_UpperCAmelCase      ,      lang=_UpperCAmelCase      ,      output_type="dict"      ,      config=_UpperCAmelCase     )
					__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__    (					UpperCAmelCase__      ):
						_a							:    List[Any]					=    ["pixel_values"]






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




											"""simple docstring"""



											super().__init__(**_A			)
											__lowerCAmelCase				   =       size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											__lowerCAmelCase				   =       get_size_dict(_A			)

											__lowerCAmelCase				   =       do_resize
											__lowerCAmelCase				   =       size
											__lowerCAmelCase				   =       resample
											__lowerCAmelCase				   =       apply_ocr
											__lowerCAmelCase				   =       ocr_lang
											__lowerCAmelCase				   =       tesseract_config






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = PILImageResampling.BILINEAR		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       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()}"""			)
											__lowerCAmelCase				   =       (size["""height"""], size["""width"""])
											return resize(_A		,			size=_A		,			resample=_A		,			data_format=_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = ChannelDimension.FIRST		,			**_A		,			):




											"""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(_A			)
											__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(_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.
											__lowerCAmelCase				   =       [to_numpy_array(_A			) for image in images]

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

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

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

											__lowerCAmelCase				   =       BatchFeature(data={"pixel_values": images}		,			tensor_type=_A			)

											if apply_ocr:
																__lowerCAmelCase				   =       words_batch
																__lowerCAmelCase				   =       boxes_batch
											return data








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__:    str					    =       {}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple					    =       ['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpt_swa import GPTSwaTokenizer

else:
   import sys

   A__:    str					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


