





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase					,lowerCAmelCase_ ):







       def 						__lowerCamelCase   (				self					:     int      )       ->Dict:
          lowerCamelCase__       :						Optional[int]        =							load_tool('''text-classification'''      )
          self.tool.setup()
          lowerCamelCase__       :						Tuple        =							load_tool('''text-classification'''		, remote=A      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Dict:
          lowerCamelCase__       :						Optional[int]        =							self.tool('''That\'s quite cool'''		, ['''positive''', '''negative''']      )
          self.assertEqual(A		, '''positive'''      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          lowerCamelCase__       :						Tuple        =							self.remote_tool('''That\'s quite cool'''		, ['''positive''', '''negative''']      )
          self.assertEqual(A		, '''positive'''      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
          lowerCamelCase__       :						Any        =							self.tool(text='''That\'s quite cool'''		, labels=['''positive''', '''negative''']      )
          self.assertEqual(A		, '''positive'''      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->int:
          lowerCamelCase__       :						Any        =							self.remote_tool(text='''That\'s quite cool'''		, labels=['''positive''', '''negative''']      )
          self.assertEqual(A		, '''positive'''      )










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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase="shi-labs/oneformer_demo"					)    ->    Union[str, Any]:







   """simple docstring"""
   with open(hf_hub_download(UpperCAmelCase      ,  UpperCAmelCase      ,  repo_type='''dataset'''					)      ,  '''r'''					) as f:
      lowerCamelCase__       :						List[Any]        =							json.load(UpperCAmelCase					)
   lowerCamelCase__       :						Dict        =							{}
   lowerCamelCase__       :						str        =							[]
   lowerCamelCase__       :						Optional[Any]        =							[]
   for key, info in class_info.items():
      lowerCamelCase__       :						Union[str, Any]        =							info['''name''']
      class_names.append(info['''name''']					)
      if info["isthing"]:
         thing_ids.append(int(UpperCAmelCase					)					)
   lowerCamelCase__       :						Optional[int]        =							thing_ids
   lowerCamelCase__       :						Dict        =							class_names
   return metadata


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def __init__(				self					:     Tuple		, A					:     Tuple		, A					:     List[Any]=7		, A					:     str=3		, A					:     List[str]=3_0		, A					:     Optional[int]=4_0_0		, A					:     int=None		, A					:     Tuple=True		, A					:     Dict=True		, A					:     Dict=[0.5, 0.5, 0.5]		, A					:     Tuple=[0.5, 0.5, 0.5]		, A					:     int=1_0		, A					:     List[str]=False		, A					:     Optional[Any]=2_5_5		, A					:     Union[str, Any]="shi-labs/oneformer_demo"		, A					:     Optional[Any]="ade20k_panoptic.json"		, A					:     str=1_0		, )       ->Dict:
          lowerCamelCase__       :						Dict        =							parent
          lowerCamelCase__       :						List[Any]        =							batch_size
          lowerCamelCase__       :						Dict        =							num_channels
          lowerCamelCase__       :						List[str]        =							min_resolution
          lowerCamelCase__       :						str        =							max_resolution
          lowerCamelCase__       :						Optional[Any]        =							do_resize
          lowerCamelCase__       :						Any        =							{'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
          lowerCamelCase__       :						str        =							do_normalize
          lowerCamelCase__       :						List[str]        =							image_mean
          lowerCamelCase__       :						List[str]        =							image_std
          lowerCamelCase__       :						Optional[int]        =							class_info_file
          lowerCamelCase__       :						Any        =							prepare_metadata(A		, A      )
          lowerCamelCase__       :						str        =							num_text
          lowerCamelCase__       :						Dict        =							repo_path

          # for the post_process_functions
          lowerCamelCase__       :						str        =							2
          lowerCamelCase__       :						Union[str, Any]        =							1_0
          lowerCamelCase__       :						List[Any]        =							1_0
          lowerCamelCase__       :						List[Any]        =							3
          lowerCamelCase__       :						Tuple        =							4
          lowerCamelCase__       :						List[Any]        =							num_labels
          lowerCamelCase__       :						List[Any]        =							do_reduce_labels
          lowerCamelCase__       :						List[Any]        =							ignore_index







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

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

          return expected_height, expected_width







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[str]:
          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      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[int]			   =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
       # only for test_image_processing_common.test_image_proc_to_json_string
       _UpperCAmelCase				:  Dict			   =   image_processing_class







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						Optional[int]        =							OneFormerImageProcessorTester(self      )







       @property
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Any:
          return self.image_processing_tester.prepare_image_processor_dict()







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          lowerCamelCase__       :						List[str]        =							self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A		, '''image_mean'''      )      )
          self.assertTrue(hasattr(A		, '''image_std'''      )      )
          self.assertTrue(hasattr(A		, '''do_normalize'''      )      )
          self.assertTrue(hasattr(A		, '''do_resize'''      )      )
          self.assertTrue(hasattr(A		, '''size'''      )      )
          self.assertTrue(hasattr(A		, '''ignore_index'''      )      )
          self.assertTrue(hasattr(A		, '''class_info_file'''      )      )
          self.assertTrue(hasattr(A		, '''num_text'''      )      )
          self.assertTrue(hasattr(A		, '''repo_path'''      )      )
          self.assertTrue(hasattr(A		, '''metadata'''      )      )
          self.assertTrue(hasattr(A		, '''do_reduce_labels'''      )      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Tuple:
          pass







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

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

          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

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

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

          lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							self.image_processing_tester.get_expected_values(A      )

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

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

          lowerCamelCase__       :						int        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, 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 						__lowerCamelCase   (				self					:     int      )       ->str:
          # Initialize image_processor
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						int        =							prepare_image_inputs(self.image_processing_tester		, equal_resolution=A		, torchify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, torch.Tensor      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

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

          lowerCamelCase__       :						int        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, A		, return_tensors='''pt'''		, instance_id_to_semantic_id=A		, pad_and_return_pixel_mask=A		, )

          return inputs







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







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Any:
          def common(A					:     Dict=False		, A					:     Tuple=None      ):
             lowerCamelCase__       :						str        =							self.comm_get_image_processor_inputs(
                 with_segmentation_maps=A		, is_instance_map=A		, segmentation_type=A      )

             lowerCamelCase__       :						Union[str, Any]        =							inputs['''mask_labels''']
             lowerCamelCase__       :						List[Any]        =							inputs['''class_labels''']
             lowerCamelCase__       :						List[str]        =							inputs['''pixel_values''']
             lowerCamelCase__       :						Union[str, Any]        =							inputs['''text_inputs''']

             # check the batch_size
             for mask_label, class_label, text_input in zip(A		, A		, A      ):
                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(A      )		, self.image_processing_tester.num_text      )

          common()
          common(is_instance_map=A      )
          common(is_instance_map=A		, segmentation_type='''pil'''      )
          common(is_instance_map=A		, segmentation_type='''pil'''      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							np.zeros((2_0, 5_0)      )
          lowerCamelCase__       :						Any        =							1
          lowerCamelCase__       :						Union[str, Any]        =							1
          lowerCamelCase__       :						List[str]        =							1

          lowerCamelCase__       :						str        =							binary_mask_to_rle(A      )
          self.assertEqual(len(A      )		, 4      )
          self.assertEqual(rle[0]		, 2_1      )
          self.assertEqual(rle[1]		, 4_5      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Dict:
          lowerCamelCase__       :						int        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						str        =							self.image_processing_tester.get_fake_oneformer_outputs()

          lowerCamelCase__       :						List[Any]        =							fature_extractor.post_process_semantic_segmentation(A      )

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

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

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







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						Optional[int]        =							self.image_processing_tester.get_fake_oneformer_outputs()
          lowerCamelCase__       :						Union[str, Any]        =							image_processor.post_process_instance_segmentation(A		, threshold=0      )

          self.assertTrue(len(A      ) == 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''']      )		, A      )
             self.assertEqual(
                 el['''segmentation'''].shape		, (self.image_processing_tester.height, self.image_processing_tester.width)      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          lowerCamelCase__       :						Union[str, Any]        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_fake_oneformer_outputs()
          lowerCamelCase__       :						List[str]        =							image_processor.post_process_panoptic_segmentation(A		, threshold=0      )

          self.assertTrue(len(A      ) == 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''']      )		, A      )
             self.assertEqual(
                 el['''segmentation'''].shape		, (self.image_processing_tester.height, self.image_processing_tester.width)      )



