


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     UpperCamelCase    (      _a       ,		_a="shi-labs/oneformer_demo"		)					->							int:
     '''simple docstring'''





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






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







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

        # for the post_process_functions
        lowercase_       :Optional[Any]     =       2
        lowercase_       :str     =       10
        lowercase_       :Optional[int]     =       10
        lowercase_       :Any     =       3
        lowercase_       :List[str]     =       4
        lowercase_       :str     =       num_labels
        lowercase_       :str     =       do_reduce_labels
        lowercase_       :List[str]     =       ignore_index
   def 		UpperCamelCase  (     self		):
        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   ,							UpperCamelCase_   ,							UpperCamelCase_=False		):
        if not batched:
             lowercase_       :Optional[Any]     =       image_inputs[0]
             if isinstance(UpperCamelCase_   ,							Image.Image		):
                  lowercase_    ,      lowercase_       :Any     =       image.size
             else:
                  lowercase_    ,      lowercase_       :Union[str, Any]     =       image.shape[1], image.shape[2]
             if w < h:
                  lowercase_       :int     =       int(self.size['''shortest_edge'''] * h / w		)
                  lowercase_       :Dict     =       self.size['''shortest_edge''']
             elif w > h:
                  lowercase_       :Any     =       self.size['''shortest_edge''']
                  lowercase_       :Union[str, Any]     =       int(self.size['''shortest_edge'''] * w / h		)
             else:
                  lowercase_       :Dict     =       self.size['''shortest_edge''']
                  lowercase_       :Dict     =       self.size['''shortest_edge''']

        else:
             lowercase_       :Dict     =       []
             for image in image_inputs:
                  lowercase_    ,      lowercase_       :Optional[int]     =       self.get_expected_values([image]		)
                  expected_values.append((expected_height, expected_width)		)
             lowercase_       :Dict     =       max(UpperCamelCase_   ,							key=lambda UpperCamelCase_		: item[0]		)[0]
             lowercase_       :int     =       max(UpperCamelCase_   ,							key=lambda UpperCamelCase_		: item[1]		)[1]

        return expected_height, expected_width




   def 		UpperCamelCase  (     self		):
        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      UpperCamelCase					(		lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Optional[Any]									=OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   lowercase					:	Union[str, Any]									=image_processing_class
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       OneFormerImageProcessorTester(self		)
   @property
   def 		UpperCamelCase  (     self		):
        return self.image_processing_tester.prepare_image_processor_dict()
   def 		UpperCamelCase  (     self		):
        lowercase_       :Tuple     =       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_resize'''		)		)
        self.assertTrue(hasattr(UpperCamelCase_   ,							'''size'''		)		)
        self.assertTrue(hasattr(UpperCamelCase_   ,							'''ignore_index'''		)		)
        self.assertTrue(hasattr(UpperCamelCase_   ,							'''class_info_file'''		)		)
        self.assertTrue(hasattr(UpperCamelCase_   ,							'''num_text'''		)		)
        self.assertTrue(hasattr(UpperCamelCase_   ,							'''repo_path'''		)		)
        self.assertTrue(hasattr(UpperCamelCase_   ,							'''metadata'''		)		)
        self.assertTrue(hasattr(UpperCamelCase_   ,							'''do_reduce_labels'''		)		)
   def 		UpperCamelCase  (     self		):
        pass
   def 		UpperCamelCase  (     self		):
        # Initialize image_processor
        lowercase_       :Optional[int]     =       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        lowercase_       :int     =       prepare_image_inputs(self.image_processing_tester   ,							equal_resolution=UpperCamelCase_		)
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_   ,							Image.Image		)

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

        lowercase_    ,      lowercase_       :List[Any]     =       self.image_processing_tester.get_expected_values(UpperCamelCase_		)

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

        # Test batched
        lowercase_    ,      lowercase_       :List[Any]     =       self.image_processing_tester.get_expected_values(UpperCamelCase_   ,							batched=UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       image_processor(
            UpperCamelCase_   ,							['''semantic'''] * len(UpperCamelCase_		)   ,							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		):
        # Initialize image_processor
        lowercase_       :Dict     =       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        lowercase_       :Dict     =       prepare_image_inputs(self.image_processing_tester   ,							equal_resolution=UpperCamelCase_   ,							numpify=UpperCamelCase_		)
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_   ,							np.ndarray		)

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

        lowercase_    ,      lowercase_       :Tuple     =       self.image_processing_tester.get_expected_values(UpperCamelCase_		)

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

        # Test batched
        lowercase_    ,      lowercase_       :Any     =       self.image_processing_tester.get_expected_values(UpperCamelCase_   ,							batched=UpperCamelCase_		)

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

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

        lowercase_    ,      lowercase_       :int     =       self.image_processing_tester.get_expected_values(UpperCamelCase_		)

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

        # Test batched
        lowercase_    ,      lowercase_       :Tuple     =       self.image_processing_tester.get_expected_values(UpperCamelCase_   ,							batched=UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       image_processor(
            UpperCamelCase_   ,							['''semantic'''] * len(UpperCamelCase_		)   ,							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   ,							UpperCamelCase_=False   ,							UpperCamelCase_=False   ,							UpperCamelCase_="np"		):
        lowercase_       :Any     =       self.image_processing_class(**self.image_processor_dict		)
        # prepare image and target
        lowercase_       :Optional[int]     =       self.image_processing_tester.num_labels
        lowercase_       :Optional[Any]     =       None
        lowercase_       :Optional[Any]     =       None
        lowercase_       :Optional[int]     =       prepare_image_inputs(self.image_processing_tester   ,							equal_resolution=UpperCamelCase_		)
        if with_segmentation_maps:
             lowercase_       :Union[str, Any]     =       num_labels
             if is_instance_map:
                  lowercase_       :List[Any]     =       list(range(UpperCamelCase_		)		) * 2
                  lowercase_       :int     =       dict(enumerate(UpperCamelCase_		)		)
             lowercase_       :Dict     =       [
                 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_       :Any     =       [Image.fromarray(UpperCamelCase_		) for annotation in annotations]

        lowercase_       :Any     =       image_processor(
            UpperCamelCase_   ,							['''semantic'''] * len(UpperCamelCase_		)   ,							UpperCamelCase_   ,							return_tensors='''pt'''   ,							instance_id_to_semantic_id=UpperCamelCase_   ,							pad_and_return_pixel_mask=UpperCamelCase_   ,							)

        return inputs
   def 		UpperCamelCase  (     self		):
        pass
   def 		UpperCamelCase  (     self		):
        def common(UpperCamelCase_=False   ,							UpperCamelCase_=None		):
             lowercase_       :str     =       self.comm_get_image_processor_inputs(
                 with_segmentation_maps=UpperCamelCase_   ,							is_instance_map=UpperCamelCase_   ,							segmentation_type=UpperCamelCase_		)

             lowercase_       :List[str]     =       inputs['''mask_labels''']
             lowercase_       :Dict     =       inputs['''class_labels''']
             lowercase_       :Optional[int]     =       inputs['''pixel_values''']
             lowercase_       :List[str]     =       inputs['''text_inputs''']

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

        common()
        common(is_instance_map=UpperCamelCase_		)
        common(is_instance_map=UpperCamelCase_   ,							segmentation_type='''pil'''		)
        common(is_instance_map=UpperCamelCase_   ,							segmentation_type='''pil'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       np.zeros((20, 50)		)
        lowercase_       :Union[str, Any]     =       1
        lowercase_       :Any     =       1
        lowercase_       :Optional[Any]     =       1

        lowercase_       :List[str]     =       binary_mask_to_rle(UpperCamelCase_		)
        self.assertEqual(len(UpperCamelCase_		)   ,							4		)
        self.assertEqual(rle[0]   ,							21		)
        self.assertEqual(rle[1]   ,							45		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       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[str]     =       self.image_processing_tester.get_fake_oneformer_outputs()

        lowercase_       :Optional[Any]     =       fature_extractor.post_process_semantic_segmentation(UpperCamelCase_		)

        self.assertEqual(len(UpperCamelCase_		)   ,							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(UpperCamelCase_   ,							target_sizes=UpperCamelCase_		)

        self.assertEqual(segmentation[0].shape   ,							target_sizes[0]		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[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_       :Optional[int]     =       self.image_processing_tester.get_fake_oneformer_outputs()
        lowercase_       :Optional[Any]     =       image_processor.post_process_instance_segmentation(UpperCamelCase_   ,							threshold=0		)

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




   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       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_       :List[str]     =       image_processor.post_process_panoptic_segmentation(UpperCamelCase_   ,							threshold=0		)

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





from torch import nn




def     UpperCamelCase    (      _a		)					->							str:
     '''simple docstring'''





     if act_fn in ["swish", "silu"]:
          return nn.SiLU()
     elif act_fn == "mish":
          return nn.Mish()
     elif act_fn == "gelu":
          return nn.GELU()
     else:
          raise ValueError(f"Unsupported activation function: {act_fn}"		)

