






'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class     a_						(      unittest.TestCase    ):

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = tempfile.mkdtemp()

        # fmt: off
        UpperCamelCase      = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
        # fmt: on
        UpperCamelCase      = dict(zip(_A      ,		range(len(_A				)				)				)				)
        UpperCamelCase      = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
        UpperCamelCase      = {'unk_token': '<unk>'}

        UpperCamelCase      = os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["""vocab_file"""]				)
        UpperCamelCase      = os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["""merges_file"""]				)
        with open(self.vocab_file      ,		"""w"""      ,		encoding="""utf-8"""				) as fp:
              fp.write(json.dumps(_A				) + """\n"""				)
        with open(self.merges_file      ,		"""w"""      ,		encoding="""utf-8"""				) as fp:
              fp.write("""\n""".join(_A				)				)

        UpperCamelCase      = {
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
        }
        UpperCamelCase      = os.path.join(self.tmpdirname      ,		_A				)
        with open(self.image_processor_file      ,		"""w"""      ,		encoding="""utf-8"""				) as fp:
              json.dump(_A      ,		_A				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return CLIPTokenizer.from_pretrained(self.tmpdirname      ,		**_A				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,		**_A				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return ViTImageProcessor.from_pretrained(self.tmpdirname      ,		**_A				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        shutil.rmtree(self.tmpdirname				)

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = [np.random.randint(255      ,		size=(3, 30, 400)      ,		dtype=np.uinta				)]

        UpperCamelCase      = [Image.fromarray(np.moveaxis(_A      ,		0      ,		-1				)				) for x in image_inputs]

        return image_inputs

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = self.get_tokenizer()
        UpperCamelCase      = self.get_rust_tokenizer()
        UpperCamelCase      = self.get_image_processor()

        UpperCamelCase      = CLIPSegProcessor(tokenizer=_A      ,		image_processor=_A				)
        processor_slow.save_pretrained(self.tmpdirname				)
        UpperCamelCase      = CLIPSegProcessor.from_pretrained(self.tmpdirname      ,		use_fast=_A				)

        UpperCamelCase      = CLIPSegProcessor(tokenizer=_A      ,		image_processor=_A				)
        processor_fast.save_pretrained(self.tmpdirname				)
        UpperCamelCase      = CLIPSegProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor_slow.tokenizer.get_vocab()      ,		tokenizer_slow.get_vocab()				)
        self.assertEqual(processor_fast.tokenizer.get_vocab()      ,		tokenizer_fast.get_vocab()				)
        self.assertEqual(tokenizer_slow.get_vocab()      ,		tokenizer_fast.get_vocab()				)
        self.assertIsInstance(processor_slow.tokenizer      ,		_A				)
        self.assertIsInstance(processor_fast.tokenizer      ,		_A				)

        self.assertEqual(processor_slow.image_processor.to_json_string()      ,		image_processor.to_json_string()				)
        self.assertEqual(processor_fast.image_processor.to_json_string()      ,		image_processor.to_json_string()				)
        self.assertIsInstance(processor_slow.image_processor      ,		_A				)
        self.assertIsInstance(processor_fast.image_processor      ,		_A				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,		image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        UpperCamelCase      = self.get_tokenizer(bos_token="""(BOS)"""      ,		eos_token="""(EOS)"""				)
        UpperCamelCase      = self.get_image_processor(do_normalize=_A      ,		padding_value=1.0				)

        UpperCamelCase      = CLIPSegProcessor.from_pretrained(
            self.tmpdirname      ,		bos_token="""(BOS)"""      ,		eos_token="""(EOS)"""      ,		do_normalize=_A      ,		padding_value=1.0				)

        self.assertEqual(processor.tokenizer.get_vocab()      ,		tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.tokenizer      ,		_A				)

        self.assertEqual(processor.image_processor.to_json_string()      ,		image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor      ,		_A				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = CLIPSegProcessor(tokenizer=_A      ,		image_processor=_A				)

        UpperCamelCase      = self.prepare_image_inputs()

        UpperCamelCase      = image_processor(_A      ,		return_tensors="""np"""				)
        UpperCamelCase      = processor(images=_A      ,		return_tensors="""np"""				)

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()      ,		input_processor[key].sum()      ,		delta=1e-2				)

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = CLIPSegProcessor(tokenizer=_A      ,		image_processor=_A				)

        UpperCamelCase      = 'lower newer'

        UpperCamelCase      = processor(text=_A				)

        UpperCamelCase      = tokenizer(_A				)

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]      ,		encoded_processor[key]				)

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = CLIPSegProcessor(tokenizer=_A      ,		image_processor=_A				)

        UpperCamelCase      = 'lower newer'
        UpperCamelCase      = self.prepare_image_inputs()

        UpperCamelCase      = processor(text=_A      ,		images=_A				)

        self.assertListEqual(list(inputs.keys()				)      ,		["""input_ids""", """attention_mask""", """pixel_values"""]				)

        # test if it raises when no input is passed
        with pytest.raises(_A				):
              processor()

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = CLIPSegProcessor(tokenizer=_A      ,		image_processor=_A				)

        UpperCamelCase      = self.prepare_image_inputs()
        UpperCamelCase      = self.prepare_image_inputs()

        UpperCamelCase      = processor(images=_A      ,		visual_prompt=_A				)

        self.assertListEqual(list(inputs.keys()				)      ,		["""pixel_values""", """conditional_pixel_values"""]				)

        # test if it raises when no input is passed
        with pytest.raises(_A				):
              processor()


  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = CLIPSegProcessor(tokenizer=_A      ,		image_processor=_A				)

        UpperCamelCase      = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCamelCase      = processor.batch_decode(_A				)
        UpperCamelCase      = tokenizer.batch_decode(_A				)

        self.assertListEqual(_A      ,		_A				)








'''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

SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			int:
      return [
          int(1000 * (box[0] / width)   ),
          int(1000 * (box[1] / height)   ),
          int(1000 * (box[2] / width)   ),
          int(1000 * (box[3] / height)   ),
      ]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = None   )->			Dict:
      UpperCamelCase      = tesseract_config if tesseract_config is not None else """"""

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

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

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

      # finally, normalize the bounding boxes
      UpperCamelCase      = []
      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_						(      lowerCamelCase    ):
  lowercase							=				["""pixel_values"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ""      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = size if size is not None else {"""height""": 224, """width""": 224}
        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = resample
        UpperCamelCase      = apply_ocr
        UpperCamelCase      = ocr_lang
        UpperCamelCase      = tesseract_config

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				np.ndarray:



        """simple docstring"""






        UpperCamelCase      = get_size_dict(_SCREAMING_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()}"				)
        UpperCamelCase      = (size["""height"""], size["""width"""])
        return resize(_SCREAMING_SNAKE_CASE      ,		size=_SCREAMING_SNAKE_CASE      ,		resample=_SCREAMING_SNAKE_CASE      ,		data_format=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)


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



        """simple docstring"""






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

        UpperCamelCase      = make_list_of_images(_SCREAMING_SNAKE_CASE				)

        if not valid_images(_SCREAMING_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.
        UpperCamelCase      = [to_numpy_array(_SCREAMING_SNAKE_CASE				) for image in images]

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

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

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

        UpperCamelCase      = BatchFeature(data={"""pixel_values""": images}      ,		tensor_type=_SCREAMING_SNAKE_CASE				)

        if apply_ocr:
              UpperCamelCase      = words_batch
              UpperCamelCase      = boxes_batch
        return data
