'''simple docstring'''






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DetrImageProcessor







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def __init__(      self      :			int, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Optional[int]=7, UpperCamelCase__      :			int=3, UpperCamelCase__      :			Any=30, UpperCamelCase__      :			Union[str, Any]=4_00, UpperCamelCase__      :			List[str]=True, UpperCamelCase__      :			Union[str, Any]=None, UpperCamelCase__      :			List[Any]=True, UpperCamelCase__      :			Optional[Any]=1 / 2_55, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			Tuple=[0.5, 0.5, 0.5], UpperCamelCase__      :			Union[str, Any]=[0.5, 0.5, 0.5], UpperCamelCase__      :			Any=True, )  ->			str:
        _A    		=  size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  num_channels
        _A    		=  min_resolution
        _A    		=  max_resolution
        _A    		=  do_resize
        _A    		=  size
        _A    		=  do_rescale
        _A    		=  rescale_factor
        _A    		=  do_normalize
        _A    		=  image_mean
        _A    		=  image_std
        _A    		=  do_pad
   def 	__UpperCAmelCase   (      self      :			str     )  ->			str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }

   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Any, UpperCamelCase__      :			Optional[Any]=False     )  ->			str:
        if not batched:
             _A    		=  image_inputs[0]
             if isinstance(UpperCamelCase__, Image.Image     ):
                  _A		,       _A    		=  image.size
             else:
                  _A		,       _A    		=  image.shape[1], image.shape[2]
             if w < h:
                  _A    		=  int(self.size['shortest_edge'] * h / w     )
                  _A    		=  self.size['shortest_edge']
             elif w > h:
                  _A    		=  self.size['shortest_edge']
                  _A    		=  int(self.size['shortest_edge'] * w / h     )
             else:
                  _A    		=  self.size['shortest_edge']
                  _A    		=  self.size['shortest_edge']

        else:
             _A    		=  []
             for image in image_inputs:
                  _A		,       _A    		=  self.get_expected_values([image]     )
                  expected_values.append((expected_height, expected_width)     )
             _A    		=  max(UpperCamelCase__, key=lambda UpperCamelCase__     : item[0]     )[0]
             _A    		=  max(UpperCamelCase__, key=lambda UpperCamelCase__     : item[1]     )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   DetrImageProcessor if is_vision_available() else None
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Dict:
        _A    		=  DetrImageProcessingTester(self     )
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Tuple:
        return self.image_processor_tester.prepare_image_processor_dict()
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(UpperCamelCase__, 'image_mean'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'image_std'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'do_normalize'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'do_rescale'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'rescale_factor'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'do_resize'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'size'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'do_pad'     )     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        _A    		=  self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size, {'shortest_edge': 18, 'longest_edge': 13_33}     )
        self.assertEqual(image_processor.do_pad, UpperCamelCase__     )

        _A    		=  self.image_processing_class.from_dict(
            self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=UpperCamelCase__     )
        self.assertEqual(image_processor.size, {'shortest_edge': 42, 'longest_edge': 84}     )
        self.assertEqual(image_processor.do_pad, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Union[str, Any]:
        pass
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Tuple:
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _A    		=  prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCamelCase__     )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__, Image.Image     )

        # Test not batched input
        _A    		=  image_processing(image_inputs[0], return_tensors='pt'     ).pixel_values

        _A		,       _A    		=  self.image_processor_tester.get_expected_values(UpperCamelCase__     )

        self.assertEqual(
            encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )

        # Test batched
        _A		,       _A    		=  self.image_processor_tester.get_expected_values(UpperCamelCase__, batched=UpperCamelCase__     )

        _A    		=  image_processing(UpperCamelCase__, return_tensors='pt'     ).pixel_values
        self.assertEqual(
            encoded_images.shape, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ), )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        _A    		=  prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCamelCase__, numpify=UpperCamelCase__     )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__, np.ndarray     )

        # Test not batched input
        _A    		=  image_processing(image_inputs[0], return_tensors='pt'     ).pixel_values

        _A		,       _A    		=  self.image_processor_tester.get_expected_values(UpperCamelCase__     )

        self.assertEqual(
            encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )

        # Test batched
        _A    		=  image_processing(UpperCamelCase__, return_tensors='pt'     ).pixel_values

        _A		,       _A    		=  self.image_processor_tester.get_expected_values(UpperCamelCase__, batched=UpperCamelCase__     )

        self.assertEqual(
            encoded_images.shape, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ), )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _A    		=  prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCamelCase__, torchify=UpperCamelCase__     )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__, torch.Tensor     )

        # Test not batched input
        _A    		=  image_processing(image_inputs[0], return_tensors='pt'     ).pixel_values

        _A		,       _A    		=  self.image_processor_tester.get_expected_values(UpperCamelCase__     )

        self.assertEqual(
            encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )

        # Test batched
        _A    		=  image_processing(UpperCamelCase__, return_tensors='pt'     ).pixel_values

        _A		,       _A    		=  self.image_processor_tester.get_expected_values(UpperCamelCase__, batched=UpperCamelCase__     )

        self.assertEqual(
            encoded_images.shape, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ), )
   @slow
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        _A    		=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
        with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt', 'r'     ) as f:
             _A    		=  json.loads(f.read()     )

        _A    		=  {'image_id': 3_97_69, 'annotations': target}

        # encode them
        _A    		=  DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'     )
        _A    		=  image_processing(images=UpperCamelCase__, annotations=UpperCamelCase__, return_tensors='pt'     )

        # verify pixel values
        _A    		=  torch.Size([1, 3, 8_00, 10_66]     )
        self.assertEqual(encoding['pixel_values'].shape, UpperCamelCase__     )

        _A    		=  torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3], UpperCamelCase__, atol=1e-4     )     )

        # verify area
        _A    		=  torch.tensor([5_887.9_600, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['area'], UpperCamelCase__     )     )
        # verify boxes
        _A    		=  torch.Size([6, 4]     )
        self.assertEqual(encoding['labels'][0]['boxes'].shape, UpperCamelCase__     )
        _A    		=  torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0], UpperCamelCase__, atol=1e-3     )     )
        # verify image_id
        _A    		=  torch.tensor([3_97_69]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'], UpperCamelCase__     )     )
        # verify is_crowd
        _A    		=  torch.tensor([0, 0, 0, 0, 0, 0]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'], UpperCamelCase__     )     )
        # verify class_labels
        _A    		=  torch.tensor([75, 75, 63, 65, 17, 17]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'], UpperCamelCase__     )     )
        # verify orig_size
        _A    		=  torch.tensor([4_80, 6_40]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'], UpperCamelCase__     )     )
        # verify size
        _A    		=  torch.tensor([8_00, 10_66]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size'], UpperCamelCase__     )     )

   @slow
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Dict:
        _A    		=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
        with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt', 'r'     ) as f:
             _A    		=  json.loads(f.read()     )

        _A    		=  {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

        _A    		=  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

        # encode them
        _A    		=  DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'     )
        _A    		=  image_processing(images=UpperCamelCase__, annotations=UpperCamelCase__, masks_path=UpperCamelCase__, return_tensors='pt'     )

        # verify pixel values
        _A    		=  torch.Size([1, 3, 8_00, 10_66]     )
        self.assertEqual(encoding['pixel_values'].shape, UpperCamelCase__     )

        _A    		=  torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3], UpperCamelCase__, atol=1e-4     )     )

        # verify area
        _A    		=  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5_879.6_562, 7_634.1_147]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['area'], UpperCamelCase__     )     )
        # verify boxes
        _A    		=  torch.Size([6, 4]     )
        self.assertEqual(encoding['labels'][0]['boxes'].shape, UpperCamelCase__     )
        _A    		=  torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0], UpperCamelCase__, atol=1e-3     )     )
        # verify image_id
        _A    		=  torch.tensor([3_97_69]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'], UpperCamelCase__     )     )
        # verify is_crowd
        _A    		=  torch.tensor([0, 0, 0, 0, 0, 0]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'], UpperCamelCase__     )     )
        # verify class_labels
        _A    		=  torch.tensor([17, 17, 63, 75, 75, 93]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'], UpperCamelCase__     )     )
        # verify masks
        _A    		=  82_28_73
        self.assertEqual(encoding['labels'][0]['masks'].sum().item(), UpperCamelCase__     )
        # verify orig_size
        _A    		=  torch.tensor([4_80, 6_40]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'], UpperCamelCase__     )     )
        # verify size
        _A    		=  torch.tensor([8_00, 10_66]     )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size'], UpperCamelCase__     )     )











from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
  from ..models.auto import AutoTokenizer
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			str							)						->	Optional[int]:


         """simple docstring"""
         raise NotImplementedError()





  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         raise NotImplementedError()



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			str       ,				lowerCamelCase		:			"AutoTokenizer"       ,				lowerCamelCase		:			bool = False       ,				**lowerCamelCase		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    tokenizer
         _UpperCAmelCase							=    skip_prompt
         _UpperCAmelCase							=    decode_kwargs

         # variables used in the streaming process
         _UpperCAmelCase							=    []
         _UpperCAmelCase							=    0
         _UpperCAmelCase							=    True


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[str]							)						->	List[Any]:


         """simple docstring"""
         if len(value.shape							) > 1 and value.shape[0] > 1:
                raise ValueError("""TextStreamer only supports batch size 1"""							)
         elif len(value.shape							) > 1:
                _UpperCAmelCase							=    value[0]

         if self.skip_prompt and self.next_tokens_are_prompt:
                _UpperCAmelCase							=    False
                return

         # Add the new token to the cache and decodes the entire thing.
         self.token_cache.extend(value.tolist()							)
         _UpperCAmelCase							=    self.tokenizer.decode(self.token_cache       ,				**self.decode_kwargs							)

         # After the symbol for a new line, we flush the cache.
         if text.endswith("""\n"""							):
                _UpperCAmelCase							=    text[self.print_len :]
                _UpperCAmelCase							=    []
                _UpperCAmelCase							=    0
         # If the last token is a CJK character, we print the characters.
         elif len(lowerCamelCase							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
                _UpperCAmelCase							=    text[self.print_len :]
                self.print_len += len(lowerCamelCase							)
         # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
         # which may change with the subsequent token -- there are probably smarter ways to do this!)
         else:
                _UpperCAmelCase							=    text[self.print_len : text.rfind(""" """							) + 1]
                self.print_len += len(lowerCamelCase							)

         self.on_finalized_text(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         # Flush the cache, if it exists
         if len(self.token_cache							) > 0:
                _UpperCAmelCase							=    self.tokenizer.decode(self.token_cache       ,				**self.decode_kwargs							)
                _UpperCAmelCase							=    text[self.print_len :]
                _UpperCAmelCase							=    []
                _UpperCAmelCase							=    0
         else:
                _UpperCAmelCase							=    """"""

         _UpperCAmelCase							=    True
         self.on_finalized_text(lowerCamelCase       ,				stream_end=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			bool = False							)						->	Any:


         """simple docstring"""
         print(lowerCamelCase       ,				flush=lowerCamelCase       ,				end="""""" if not stream_end else None							)





  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			Any							)						->	int:


         """simple docstring"""
         # This defines a "chinese character" as anything in the CJK Unicode block:
         #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
         #
         # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
         # despite its name. The modern Korean Hangul alphabet is a different block,
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
         if (
             (cp >= 0X4E00 and cp <= 0X9FFF)
             or (cp >= 0X3400 and cp <= 0X4DBF)  #
             or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
             or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
             or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
             or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
             or (cp >= 0XF900 and cp <= 0XFAFF)
             or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
         ):  #
                return True

         return False



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			"AutoTokenizer"       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			Optional[float] = None       ,				**lowerCamelCase		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         super().__init__(lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							)
         _UpperCAmelCase							=    Queue()
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    timeout


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			bool = False							)						->	Optional[int]:


         """simple docstring"""
         self.text_queue.put(lowerCamelCase       ,				timeout=self.timeout							)
         if stream_end:
                self.text_queue.put(self.stop_signal       ,				timeout=self.timeout							)


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


         """simple docstring"""
         return self





  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.text_queue.get(timeout=self.timeout							)
         if value == self.stop_signal:
                raise StopIteration()
         else:
                return value