

"""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 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"       )     ->str:

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




class        __snake_case	(					unittest.TestCase    ):



      def __init__(						self   ,      lowercase   ,      lowercase=7   ,      lowercase=3   ,      lowercase=30   ,      lowercase=4_00   ,      lowercase=None   ,      lowercase=True   ,      lowercase=True   ,      lowercase=[0.5, 0.5, 0.5]   ,      lowercase=[0.5, 0.5, 0.5]   ,      lowercase=10   ,      lowercase=False   ,      lowercase=2_55   ,      lowercase="shi-labs/oneformer_demo"   ,      lowercase="ade20k_panoptic.json"   ,      lowercase=10   ,      )     ->      Optional[int]:





        '''simple docstring'''





        a__:					int      							=     parent
        a__:					Tuple      							=     batch_size
        a__:					List[str]      							=     num_channels
        a__:					Any      							=     min_resolution
        a__:					int      							=     max_resolution
        a__:					List[Any]      							=     do_resize
        a__:					Any      							=     {'shortest_edge': 32, 'longest_edge': 13_33} if size is None else size
        a__:					List[str]      							=     do_normalize
        a__:					Union[str, Any]      							=     image_mean
        a__:					str      							=     image_std
        a__:					Any      							=     class_info_file
        a__:					List[str]      							=     prepare_metadata(lowercase_   ,      lowercase_)
        a__:					Tuple      							=     num_text
        a__:					List[Any]      							=     repo_path

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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        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   ,      lowercase   ,      lowercase=False)     ->      Tuple:





        '''simple docstring'''





        if not batched:
          a__:					Any      							=     image_inputs[0]
          if isinstance(lowercase_   ,      Image.Image):
            a__							,   a__:					Any      							=     image.size
          else:
            a__							,   a__:					Union[str, Any]      							=     image.shape[1], image.shape[2]
          if w < h:
            a__:					List[str]      							=     int(self.size['shortest_edge'] * h / w)
            a__:					List[str]      							=     self.size['shortest_edge']
          elif w > h:
            a__:					Union[str, Any]      							=     self.size['shortest_edge']
            a__:					List[str]      							=     int(self.size['shortest_edge'] * w / h)
          else:
            a__:					str      							=     self.size['shortest_edge']
            a__:					int      							=     self.size['shortest_edge']

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

        return expected_height, expected_width



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





        '''simple docstring'''





        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        __snake_case	(					_lowerCamelCase	,				unittest.TestCase    ):
      a__				=		OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      a__				=		image_processing_class



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[int]      							=     OneFormerImageProcessorTester(self)



      @property
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        return self.image_processing_tester.prepare_image_processor_dict()



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase_   ,      'image_mean'))
        self.assertTrue(hasattr(lowercase_   ,      'image_std'))
        self.assertTrue(hasattr(lowercase_   ,      'do_normalize'))
        self.assertTrue(hasattr(lowercase_   ,      'do_resize'))
        self.assertTrue(hasattr(lowercase_   ,      'size'))
        self.assertTrue(hasattr(lowercase_   ,      'ignore_index'))
        self.assertTrue(hasattr(lowercase_   ,      'class_info_file'))
        self.assertTrue(hasattr(lowercase_   ,      'num_text'))
        self.assertTrue(hasattr(lowercase_   ,      'repo_path'))
        self.assertTrue(hasattr(lowercase_   ,      'metadata'))
        self.assertTrue(hasattr(lowercase_   ,      'do_reduce_labels'))



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        a__:					Tuple      							=     self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:					Optional[int]      							=     prepare_image_inputs(self.image_processing_tester   ,      equal_resolution=lowercase_)
        for image in image_inputs:
          self.assertIsInstance(lowercase_   ,      Image.Image)

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

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

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

        # Test batched
        a__							,   a__:					Union[str, Any]      							=     self.image_processing_tester.get_expected_values(lowercase_   ,      batched=lowercase_)

        a__:					Union[str, Any]      							=     image_processor(
            lowercase_   ,      ['semantic'] * len(lowercase_)   ,      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)     ->      Any:





        '''simple docstring'''





        a__:					Dict      							=     self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        a__:					int      							=     prepare_image_inputs(self.image_processing_tester   ,      equal_resolution=lowercase_   ,      numpify=lowercase_)
        for image in image_inputs:
          self.assertIsInstance(lowercase_   ,      np.ndarray)

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

        a__							,   a__:					Optional[int]      							=     self.image_processing_tester.get_expected_values(lowercase_)

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

        # Test batched
        a__							,   a__:					str      							=     self.image_processing_tester.get_expected_values(lowercase_   ,      batched=lowercase_)

        a__:					int      							=     image_processor(
            lowercase_   ,      ['semantic'] * len(lowercase_)   ,      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)     ->      Optional[Any]:





        '''simple docstring'''





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

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

        a__							,   a__:					Any      							=     self.image_processing_tester.get_expected_values(lowercase_)

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

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

        a__:					List[Any]      							=     image_processor(
            lowercase_   ,      ['semantic'] * len(lowercase_)   ,      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   ,      lowercase=False   ,      lowercase=False   ,      lowercase="np")     ->      Union[str, Any]:





        '''simple docstring'''





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

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

        return inputs



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        def common(lowercase=False   ,      lowercase=None):
          a__:					Union[str, Any]      							=     self.comm_get_image_processor_inputs(
              with_segmentation_maps=lowercase_   ,      is_instance_map=lowercase_   ,      segmentation_type=lowercase_)

          a__:					Optional[int]      							=     inputs['mask_labels']
          a__:					int      							=     inputs['class_labels']
          a__:					Optional[Any]      							=     inputs['pixel_values']
          a__:					Tuple      							=     inputs['text_inputs']

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

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



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					int      							=     np.zeros((20, 50))
        a__:					Union[str, Any]      							=     1
        a__:					int      							=     1
        a__:					Optional[int]      							=     1

        a__:					Dict      							=     binary_mask_to_rle(lowercase_)
        self.assertEqual(len(lowercase_)   ,      4)
        self.assertEqual(rle[0]   ,      21)
        self.assertEqual(rle[1]   ,      45)



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					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'   ,      )
        a__:					Any      							=     self.image_processing_tester.get_fake_oneformer_outputs()

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

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

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

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



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					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'   ,      )
        a__:					List[Any]      							=     self.image_processing_tester.get_fake_oneformer_outputs()
        a__:					int      							=     image_processor.post_process_instance_segmentation(lowercase_   ,      threshold=0)

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



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					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'   ,      )
        a__:					Optional[int]      							=     self.image_processing_tester.get_fake_oneformer_outputs()
        a__:					Union[str, Any]      							=     image_processor.post_process_panoptic_segmentation(lowercase_   ,      threshold=0)

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



"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()




class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		PriorTransformer
      a__				=		"""hidden_states"""



      @property
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     4
        a__:					Any      							=     8
        a__:					Optional[Any]      							=     7

        a__:					Tuple      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)

        a__:					Optional[int]      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)
        a__:					List[str]      							=     floats_tensor((batch_size, num_embeddings, embedding_dim)).to(lowercase)

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }



      def   lowerCamelCase_       (						self   ,      lowercase=0)     ->      str:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Optional[Any]      							=     4
        a__:					Optional[Any]      							=     8
        a__:					Union[str, Any]      							=     7

        a__:					Optional[Any]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					Tuple      							=     torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return (4, 8)



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        return (4, 8)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					int      							=     {
            'num_attention_heads': 2,
            'attention_head_dim': 4,
            'num_layers': 2,
            'embedding_dim': 8,
            'num_embeddings': 7,
            'additional_embeddings': 4,
        }
        a__:					Union[str, Any]      							=     self.dummy_input
        return init_dict, inputs_dict



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





        '''simple docstring'''





        a__							,   a__:					Union[str, Any]      							=     PriorTransformer.from_pretrained(
            'hf-internal-testing/prior-dummy'   ,      output_loading_info=lowercase)
        self.assertIsNotNone(lowercase)
        self.assertEqual(len(loading_info['missing_keys'])   ,      0)

        model.to(lowercase)
        a__:					Any      							=     model(**self.dummy_input)[0]

        assert hidden_states is not None, "Make sure output is not None"



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





        '''simple docstring'''





        a__							,   a__:					Tuple      							=     self.prepare_init_args_and_inputs_for_common()

        a__:					Any      							=     self.model_class(**lowercase)
        a__:					str      							=     inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a__:					Tuple      							=     [*signature.parameters.keys()]

        a__:					List[Any]      							=     ['hidden_states', 'timestep']
        self.assertListEqual(arg_names[:2]   ,      lowercase)



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





        '''simple docstring'''





        a__:					str      							=     PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy')
        a__:					str      							=     model.to(lowercase)

        if hasattr(lowercase   ,      'set_default_attn_processor'):
          model.set_default_attn_processor()

        a__:					Dict      							=     self.get_dummy_seed_input()

        with torch.no_grad():
          a__:					str      							=     model(**lowercase)[0]

        a__:					str      							=     output[0, :5].flatten().cpu()
        print(lowercase)

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        a__:					Any      							=     torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
        self.assertTrue(torch_all_close(lowercase   ,      lowercase   ,      rtol=1e-2))







@slow
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self   ,      lowercase=1   ,      lowercase=7_68   ,      lowercase=77   ,      lowercase=0)     ->      int:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Union[str, Any]      							=     batch_size
        a__:					List[str]      							=     embedding_dim
        a__:					str      							=     num_embeddings

        a__:					Tuple      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					str      							=     torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }



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





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
              [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
              # fmt: on
          ])
      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					Tuple      							=     PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,      subfolder='prior')
        model.to(lowercase)
        a__:					Optional[Any]      							=     self.get_dummy_seed_input(seed=lowercase)

        with torch.no_grad():
          a__:					Optional[int]      							=     model(**lowercase)[0]

        assert list(sample.shape) == [1, 7_68]

        a__:					List[str]      							=     sample[0, :8].flatten().cpu()
        print(lowercase)
        a__:					Union[str, Any]      							=     torch.tensor(lowercase)

        assert torch_all_close(lowercase   ,      lowercase   ,      atol=1e-3)


