






"""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_	:				int										= logging.get_logger(__name__)



def     __lowercase					(    a :				Tuple       ,		a :				Dict       ,		a :				List[str] )					->					Any:
      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     __lowercase					(    a :				np.ndarray       ,		a :				Optional[str]       ,		a :				Optional[str] = None )					->					Optional[int]:
      __snake_case      :    Optional[int]  			=tesseract_config if tesseract_config is not None else ''''''

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

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

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

      # finally, normalize the bounding boxes
      __snake_case      :    Tuple  			=[]
      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      _lowercase (     lowerCAmelCase						):
      _a   :     Tuple								=   ['''pixel_values''']


      def __init__(    self				:	Any							,   a				:	bool = True							,   a				:	Dict[str, int] = None							,   a				:	PILImageResampling = PILImageResampling.BILINEAR							,   a				:	bool = True							,   a				:	Optional[str] = None							,   a				:	Optional[str] = ""							,   **a				:	Optional[int]							,   ):





            """simple docstring"""







            super().__init__(**a						)
            __snake_case      :    Optional[Any]  			=size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
            __snake_case      :    Union[str, Any]  			=get_size_dict(a						)

            __snake_case      :    Optional[int]  			=do_resize
            __snake_case      :    Optional[Any]  			=size
            __snake_case      :    List[str]  			=resample
            __snake_case      :    Tuple  			=apply_ocr
            __snake_case      :    str  			=ocr_lang
            __snake_case      :    str  			=tesseract_config


      def _UpperCamelCase						(    self				:	int							,   a				:	np.ndarray							,   a				:	Dict[str, int]							,   a				:	PILImageResampling = PILImageResampling.BILINEAR							,   a				:	Optional[Union[str, ChannelDimension]] = None							,   **a				:	Any							,   ):





            """simple docstring"""







            __snake_case      :    int  			=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()}'''						)
            __snake_case      :    Tuple  			=(size['''height'''], size['''width'''])
            return resize(a							,   size=a							,   resample=a							,   data_format=a							,   **a						)







      def _UpperCamelCase						(    self				:	str							,   a				:	ImageInput							,   a				:	bool = None							,   a				:	Dict[str, int] = None							,   a				:	PILImageResampling = None							,   a				:	bool = None							,   a				:	Optional[str] = None							,   a				:	Optional[str] = None							,   a				:	Optional[Union[str, TensorType]] = None							,   a				:	ChannelDimension = ChannelDimension.FIRST							,   **a				:	Optional[int]							,   ):





            """simple docstring"""







            __snake_case      :    Tuple  			=do_resize if do_resize is not None else self.do_resize
            __snake_case      :    Optional[int]  			=size if size is not None else self.size
            __snake_case      :    Optional[Any]  			=get_size_dict(a						)
            __snake_case      :    List[str]  			=resample if resample is not None else self.resample
            __snake_case      :    Tuple  			=apply_ocr if apply_ocr is not None else self.apply_ocr
            __snake_case      :    Optional[Any]  			=ocr_lang if ocr_lang is not None else self.ocr_lang
            __snake_case      :    Tuple  			=tesseract_config if tesseract_config is not None else self.tesseract_config

            __snake_case      :    int  			=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.
            __snake_case      :    str  			=[to_numpy_array(a						) for image in images]

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

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

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

            __snake_case      :    List[str]  			=BatchFeature(data={'''pixel_values''': images}							,   tensor_type=a						)

            if apply_ocr:
                  __snake_case      :    Dict  			=words_batch
                  __snake_case      :    str  			=boxes_batch
            return data










"""simple docstring"""





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     List[str]								=   MgpstrTokenizer
      _a   :     int								=   False
      _a   :     List[str]								=   {}
      _a   :     Optional[Any]								=   False


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            super().setUp()

            # fmt: off
            __snake_case      :    Union[str, Any]  			=['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
            # fmt: on
            __snake_case      :    Optional[Any]  			=dict(zip(a							,   range(len(a						)						)						)						)

            __snake_case      :    int  			=os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES['''vocab_file''']						)
            with open(self.vocab_file							,   '''w'''							,   encoding='''utf-8'''						) as fp:
                  fp.write(json.dumps(a						) + '''\n'''						)


      def _UpperCamelCase						(    self				:	Any							,   **a				:	int						):





            """simple docstring"""







            return MgpstrTokenizer.from_pretrained(self.tmpdirname							,   **a						)


      def _UpperCamelCase						(    self				:	int							,   a				:	Any						):





            """simple docstring"""







            __snake_case      :    Dict  			='''tester'''
            __snake_case      :    str  			='''tester'''
            return input_text, output_text


      @unittest.skip('''MGP-STR always lower cases letters.'''						)
      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            pass


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.get_tokenizers(do_lower_case=a						)
            for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
                        __snake_case      :    List[Any]  			='''[SPECIAL_TOKEN]'''

                        tokenizer.add_special_tokens({'''cls_token''': special_token}						)
                        __snake_case      :    Tuple  			=tokenizer.encode([special_token]							,   add_special_tokens=a						)
                        self.assertEqual(len(a						)							,   1						)

                        __snake_case      :    int  			=tokenizer.decode(a							,   skip_special_tokens=a						)
                        self.assertTrue(special_token not in decoded						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
                        __snake_case			,				__snake_case      :    Tuple  			=self.get_input_output_texts(a						)

                        __snake_case      :    int  			=tokenizer.tokenize(a						)
                        __snake_case      :    str  			=tokenizer.convert_tokens_to_ids(a						)
                        __snake_case      :    Union[str, Any]  			=tokenizer.encode(a							,   add_special_tokens=a						)
                        self.assertListEqual(a							,   a						)

                        __snake_case      :    Tuple  			=tokenizer.convert_ids_to_tokens(a						)
                        self.assertNotEqual(len(a						)							,   0						)
                        __snake_case      :    Optional[Any]  			=tokenizer.decode(a						)
                        self.assertIsInstance(a							,   a						)

                        self.assertEqual(text_a.replace(''' '''							,   ''''''						)							,   a						)


      @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''						)
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            pass







      @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''						)
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            pass


