


"""simple docstring"""


import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
      from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
      from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
   from PIL import Image


def    _lowerCamelCase   (     lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							str="shi-labs/oneformer_demo"  ):
 with open(hf_hub_download(lowerCamelCase__       ,		lowerCamelCase__       ,		repo_type="""dataset"""  )       ,		"""r"""  ) as f:
  lowercase__       :       Union[str, Any]				=						json.load(lowerCamelCase__  )
 lowercase__       :       Dict				=						{}
 lowercase__       :       Optional[int]				=						[]
 lowercase__       :       Any				=						[]
 for key, info in class_info.items():
  lowercase__       :       str				=						info["""name"""]
  class_names.append(info["""name"""]  )
  if info["isthing"]:
   thing_ids.append(int(lowerCamelCase__  )  )
 lowercase__       :       Union[str, Any]				=						thing_ids
 lowercase__       :       Any				=						class_names
 return metadata

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=[0.5, 0.5, 0.5] ,     lowerCamelCase__=[0.5, 0.5, 0.5] ,     lowerCamelCase__=10 ,     lowerCamelCase__=False ,     lowerCamelCase__=255 ,     lowerCamelCase__="shi-labs/oneformer_demo" ,     lowerCamelCase__="ade20k_panoptic.json" ,     lowerCamelCase__=10 ,     )      ->							Optional[Any]:
    lowercase__       :       Any				=						parent
    lowercase__       :       int				=						batch_size
    lowercase__       :       List[Any]				=						num_channels
    lowercase__       :       Optional[Any]				=						min_resolution
    lowercase__       :       int				=						max_resolution
    lowercase__       :       Tuple				=						do_resize
    lowercase__       :       Tuple				=						{"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
    lowercase__       :       List[str]				=						do_normalize
    lowercase__       :       List[str]				=						image_mean
    lowercase__       :       Dict				=						image_std
    lowercase__       :       int				=						class_info_file
    lowercase__       :       Dict				=						prepare_metadata(lowerCamelCase__ ,     lowerCamelCase__						)
    lowercase__       :       Dict				=						num_text
    lowercase__       :       Optional[int]				=						repo_path

    # for the post_process_functions
    lowercase__       :       Dict				=						2
    lowercase__       :       Tuple				=						10
    lowercase__       :       Optional[Any]				=						10
    lowercase__       :       List[str]				=						3
    lowercase__       :       str				=						4
    lowercase__       :       Dict				=						num_labels
    lowercase__       :       Optional[int]				=						do_reduce_labels
    lowercase__       :       Union[str, Any]				=						ignore_index


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "num_labels": self.num_labels,
        "do_reduce_labels": self.do_reduce_labels,
        "ignore_index": self.ignore_index,
        "class_info_file": self.class_info_file,
        "metadata": self.metadata,
        "num_text": self.num_text,
    }


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=False						)      ->							Optional[int]:
    if not batched:
     lowercase__       :       Optional[int]				=						image_inputs[0]
     if isinstance(lowerCamelCase__ ,     Image.Image						):
      lowercase__	,       lowercase__       :       Dict				=						image.size
     else:
      lowercase__	,       lowercase__       :       str				=						image.shape[1], image.shape[2]
     if w < h:
      lowercase__       :       Tuple				=						int(self.size["""shortest_edge"""] * h / w						)
      lowercase__       :       Optional[Any]				=						self.size["""shortest_edge"""]
     elif w > h:
      lowercase__       :       Any				=						self.size["""shortest_edge"""]
      lowercase__       :       List[Any]				=						int(self.size["""shortest_edge"""] * w / h						)
     else:
      lowercase__       :       str				=						self.size["""shortest_edge"""]
      lowercase__       :       Optional[int]				=						self.size["""shortest_edge"""]

    else:
     lowercase__       :       str				=						[]
     for image in image_inputs:
      lowercase__	,       lowercase__       :       int				=						self.get_expected_values([image]						)
      expected_values.append((expected_height, expected_width)						)
     lowercase__       :       Optional[Any]				=						max(lowerCamelCase__ ,     key=lambda lowerCamelCase__						: item[0]						)[0]
     lowercase__       :       Any				=						max(lowerCamelCase__ ,     key=lambda lowerCamelCase__						: item[1]						)[1]

    return expected_height, expected_width







   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    return OneFormerForUniversalSegmentationOutput(
        # +1 for null class
        class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)						) ,     masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)						) ,     )

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Any									=    OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   _a   :       str									=    image_processing_class


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       Union[str, Any]				=						OneFormerImageProcessorTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							str:
    return self.image_processing_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Union[str, Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """image_mean"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """image_std"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """ignore_index"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """class_info_file"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """num_text"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """repo_path"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """metadata"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_reduce_labels"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    pass


   def 					UpperCAmelCase__(						self						)      ->							int:
    # Initialize image_processor
    lowercase__       :       int				=						self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    lowercase__       :       List[Any]				=						prepare_image_inputs(self.image_processing_tester ,     equal_resolution=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     Image.Image						)

    # Test not batched input
    lowercase__       :       str				=						image_processor(image_inputs[0] ,     ["""semantic"""] ,     return_tensors="""pt"""						).pixel_values

    lowercase__	,       lowercase__       :       str				=						self.image_processing_tester.get_expected_values(lowerCamelCase__						)

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

    # Test batched
    lowercase__	,       lowercase__       :       int				=						self.image_processing_tester.get_expected_values(lowerCamelCase__ ,     batched=lowerCamelCase__						)

    lowercase__       :       Any				=						image_processor(
        lowerCamelCase__ ,     ["""semantic"""] * len(lowerCamelCase__						) ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape ,     (
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        ) ,     )


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    # Initialize image_processor
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    # create random numpy tensors
    lowercase__       :       Union[str, Any]				=						prepare_image_inputs(self.image_processing_tester ,     equal_resolution=lowerCamelCase__ ,     numpify=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     np.ndarray						)

    # Test not batched input
    lowercase__       :       List[Any]				=						image_processor(image_inputs[0] ,     ["""semantic"""] ,     return_tensors="""pt"""						).pixel_values

    lowercase__	,       lowercase__       :       Optional[Any]				=						self.image_processing_tester.get_expected_values(lowerCamelCase__						)

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

    # Test batched
    lowercase__	,       lowercase__       :       int				=						self.image_processing_tester.get_expected_values(lowerCamelCase__ ,     batched=lowerCamelCase__						)

    lowercase__       :       int				=						image_processor(
        lowerCamelCase__ ,     ["""semantic"""] * len(lowerCamelCase__						) ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape ,     (
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        ) ,     )


   def 					UpperCAmelCase__(						self						)      ->							str:
    # Initialize image_processor
    lowercase__       :       Tuple				=						self.image_processing_class(**self.image_processor_dict						)
    # create random PyTorch tensors
    lowercase__       :       Tuple				=						prepare_image_inputs(self.image_processing_tester ,     equal_resolution=lowerCamelCase__ ,     torchify=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     torch.Tensor						)

    # Test not batched input
    lowercase__       :       int				=						image_processor(image_inputs[0] ,     ["""semantic"""] ,     return_tensors="""pt"""						).pixel_values

    lowercase__	,       lowercase__       :       Any				=						self.image_processing_tester.get_expected_values(lowerCamelCase__						)

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

    # Test batched
    lowercase__	,       lowercase__       :       str				=						self.image_processing_tester.get_expected_values(lowerCamelCase__ ,     batched=lowerCamelCase__						)

    lowercase__       :       Optional[Any]				=						image_processor(
        lowerCamelCase__ ,     ["""semantic"""] * len(lowerCamelCase__						) ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape ,     (
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        ) ,     )


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__=False ,     lowerCamelCase__=False ,     lowerCamelCase__="np"						)      ->							int:
    lowercase__       :       Union[str, Any]				=						self.image_processing_class(**self.image_processor_dict						)
    # prepare image and target
    lowercase__       :       Tuple				=						self.image_processing_tester.num_labels
    lowercase__       :       Union[str, Any]				=						None
    lowercase__       :       Dict				=						None
    lowercase__       :       List[str]				=						prepare_image_inputs(self.image_processing_tester ,     equal_resolution=lowerCamelCase__						)
    if with_segmentation_maps:
     lowercase__       :       str				=						num_labels
     if is_instance_map:
      lowercase__       :       str				=						list(range(lowerCamelCase__						)						) * 2
      lowercase__       :       Tuple				=						dict(enumerate(lowerCamelCase__						)						)
     lowercase__       :       List[str]				=						[
         np.random.randint(0 ,     high * 2 ,     (img.size[1], img.size[0])						).astype(np.uinta						) for img in image_inputs
     ]
     if segmentation_type == "pil":
      lowercase__       :       List[str]				=						[Image.fromarray(lowerCamelCase__						) for annotation in annotations]

    lowercase__       :       Any				=						image_processor(
        lowerCamelCase__ ,     ["""semantic"""] * len(lowerCamelCase__						) ,     lowerCamelCase__ ,     return_tensors="""pt""" ,     instance_id_to_semantic_id=lowerCamelCase__ ,     pad_and_return_pixel_mask=lowerCamelCase__ ,     )

    return inputs


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    pass


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    def common(lowerCamelCase__=False ,     lowerCamelCase__=None						):
     lowercase__       :       Any				=						self.comm_get_image_processor_inputs(
         with_segmentation_maps=lowerCamelCase__ ,     is_instance_map=lowerCamelCase__ ,     segmentation_type=lowerCamelCase__						)

     lowercase__       :       Optional[Any]				=						inputs["""mask_labels"""]
     lowercase__       :       List[Any]				=						inputs["""class_labels"""]
     lowercase__       :       Union[str, Any]				=						inputs["""pixel_values"""]
     lowercase__       :       Any				=						inputs["""text_inputs"""]

     # check the batch_size
     for mask_label, class_label, text_input in zip(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						):
      self.assertEqual(mask_label.shape[0] ,     class_label.shape[0]						)
      # this ensure padding has happened
      self.assertEqual(mask_label.shape[1:] ,     pixel_values.shape[2:]						)
      self.assertEqual(len(lowerCamelCase__						) ,     self.image_processing_tester.num_text						)

    common()
    common(is_instance_map=lowerCamelCase__						)
    common(is_instance_map=lowerCamelCase__ ,     segmentation_type="""pil"""						)
    common(is_instance_map=lowerCamelCase__ ,     segmentation_type="""pil"""						)


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Tuple				=						np.zeros((20, 50)						)
    lowercase__       :       str				=						1
    lowercase__       :       List[Any]				=						1
    lowercase__       :       Tuple				=						1

    lowercase__       :       int				=						binary_mask_to_rle(lowerCamelCase__						)
    self.assertEqual(len(lowerCamelCase__						) ,     4						)
    self.assertEqual(rle[0] ,     21						)
    self.assertEqual(rle[1] ,     45						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       List[str]				=						self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,     max_seq_length=77 ,     task_seq_length=77 ,     class_info_file="""ade20k_panoptic.json""" ,     num_text=self.image_processing_tester.num_text ,     repo_path="""shi-labs/oneformer_demo""" ,     )
    lowercase__       :       Dict				=						self.image_processing_tester.get_fake_oneformer_outputs()

    lowercase__       :       Dict				=						fature_extractor.post_process_semantic_segmentation(lowerCamelCase__						)

    self.assertEqual(len(lowerCamelCase__						) ,     self.image_processing_tester.batch_size						)
    self.assertEqual(
        segmentation[0].shape ,     (
            self.image_processing_tester.height,
            self.image_processing_tester.width,
        ) ,     )

    lowercase__       :       str				=						[(1, 4) for i in range(self.image_processing_tester.batch_size						)]
    lowercase__       :       Optional[int]				=						fature_extractor.post_process_semantic_segmentation(lowerCamelCase__ ,     target_sizes=lowerCamelCase__						)

    self.assertEqual(segmentation[0].shape ,     target_sizes[0]						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Tuple				=						self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,     max_seq_length=77 ,     task_seq_length=77 ,     class_info_file="""ade20k_panoptic.json""" ,     num_text=self.image_processing_tester.num_text ,     repo_path="""shi-labs/oneformer_demo""" ,     )
    lowercase__       :       List[Any]				=						self.image_processing_tester.get_fake_oneformer_outputs()
    lowercase__       :       Optional[Any]				=						image_processor.post_process_instance_segmentation(lowerCamelCase__ ,     threshold=0						)

    self.assertTrue(len(lowerCamelCase__						) == self.image_processing_tester.batch_size						)
    for el in segmentation:
     self.assertTrue("""segmentation""" in el						)
     self.assertTrue("""segments_info""" in el						)
     self.assertEqual(type(el["""segments_info"""]						) ,     lowerCamelCase__						)
     self.assertEqual(
         el["""segmentation"""].shape ,     (self.image_processing_tester.height, self.image_processing_tester.width)						)







   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Any				=						self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,     max_seq_length=77 ,     task_seq_length=77 ,     class_info_file="""ade20k_panoptic.json""" ,     num_text=self.image_processing_tester.num_text ,     repo_path="""shi-labs/oneformer_demo""" ,     )
    lowercase__       :       int				=						self.image_processing_tester.get_fake_oneformer_outputs()
    lowercase__       :       Dict				=						image_processor.post_process_panoptic_segmentation(lowerCamelCase__ ,     threshold=0						)

    self.assertTrue(len(lowerCamelCase__						) == self.image_processing_tester.batch_size						)
    for el in segmentation:
     self.assertTrue("""segmentation""" in el						)
     self.assertTrue("""segments_info""" in el						)
     self.assertEqual(type(el["""segments_info"""]						) ,     lowerCamelCase__						)
     self.assertEqual(
         el["""segmentation"""].shape ,     (self.image_processing_tester.height, self.image_processing_tester.width)						)



"""simple docstring"""


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    LayoutLMTokenizer
   _a   :       Dict									=    LayoutLMTokenizerFast
   _a   :       Dict									=    True
   _a   :       Any									=    True


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    super().setUp()

    lowercase__       :       int				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       Dict				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							Optional[int]:
    return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       List[str]				=						"""UNwant\u00E9d,running"""
    lowercase__       :       Optional[int]				=						"""unwanted, running"""
    return input_text, output_text


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						self.tokenizer_class(self.vocab_file						)

    lowercase__       :       Optional[Any]				=						tokenizer.tokenize("""UNwant\u00E9d,running"""						)
    self.assertListEqual(lowerCamelCase__ ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__						) ,     [7, 4, 5, 10, 8, 9]						)







   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass