


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				(						lowerCAmelCase_  ,						lowerCAmelCase_="shi-labs/oneformer_demo"   )						->str:
   with open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type="""dataset"""   )  ,						"""r"""   ) as f:
      UpperCAmelCase							=     json.load(lowerCAmelCase_   )
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     []
   UpperCAmelCase							=     []
   for key, info in class_info.items():
      UpperCAmelCase							=     info["""name"""]
      class_names.append(info["""name"""]   )
      if info["isthing"]:
         thing_ids.append(int(lowerCAmelCase_   )   )
   UpperCAmelCase							=     thing_ids
   UpperCAmelCase							=     class_names
   return metadata






class   __lowercase				(  unittest.TestCase  ):





      def __init__(		self						:					int					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Tuple=7					,       __lowerCamelCase						:					Optional[Any]=3					,       __lowerCamelCase						:					str=3_0					,       __lowerCamelCase						:					List[str]=4_0_0					,       __lowerCamelCase						:					Any=None					,       __lowerCamelCase						:					Tuple=True					,       __lowerCamelCase						:					Dict=True					,       __lowerCamelCase						:					Tuple=[0.5, 0.5, 0.5]					,       __lowerCamelCase						:					Tuple=[0.5, 0.5, 0.5]					,       __lowerCamelCase						:					Optional[int]=1_0					,       __lowerCamelCase						:					Optional[Any]=False					,       __lowerCamelCase						:					int=2_5_5					,       __lowerCamelCase						:					Optional[Any]="shi-labs/oneformer_demo"					,       __lowerCamelCase						:					Optional[int]="ade20k_panoptic.json"					,       __lowerCamelCase						:					Any=1_0					,       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     parent
         UpperCAmelCase							=     batch_size
         UpperCAmelCase							=     num_channels
         UpperCAmelCase							=     min_resolution
         UpperCAmelCase							=     max_resolution
         UpperCAmelCase							=     do_resize
         UpperCAmelCase							=     {"""shortest_edge""": 3_2, """longest_edge""": 1_3_3_3} if size is None else size
         UpperCAmelCase							=     do_normalize
         UpperCAmelCase							=     image_mean
         UpperCAmelCase							=     image_std
         UpperCAmelCase							=     class_info_file
         UpperCAmelCase							=     prepare_metadata(__lowerCamelCase					,       __lowerCamelCase       )
         UpperCAmelCase							=     num_text
         UpperCAmelCase							=     repo_path

         # for the post_process_functions
         UpperCAmelCase							=     2
         UpperCAmelCase							=     1_0
         UpperCAmelCase							=     1_0
         UpperCAmelCase							=     3
         UpperCAmelCase							=     4
         UpperCAmelCase							=     num_labels
         UpperCAmelCase							=     do_reduce_labels
         UpperCAmelCase							=     ignore_index





      def        _lowercase				(		self						:					Optional[int]       )    ->   Any:






         """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        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					int=False       )    ->   int:






         """simple docstring"""







         if not batched:
            UpperCAmelCase							=     image_inputs[0]
            if isinstance(__lowerCamelCase					,       Image.Image       ):
               UpperCAmelCase				,  UpperCAmelCase							=     image.size
            else:
               UpperCAmelCase				,  UpperCAmelCase							=     image.shape[1], image.shape[2]
            if w < h:
               UpperCAmelCase							=     int(self.size["""shortest_edge"""] * h / w       )
               UpperCAmelCase							=     self.size["""shortest_edge"""]
            elif w > h:
               UpperCAmelCase							=     self.size["""shortest_edge"""]
               UpperCAmelCase							=     int(self.size["""shortest_edge"""] * w / h       )
            else:
               UpperCAmelCase							=     self.size["""shortest_edge"""]
               UpperCAmelCase							=     self.size["""shortest_edge"""]

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

         return expected_height, expected_width





      def        _lowercase				(		self						:					Any       )    ->   Optional[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   __lowercase				(  __snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      UpperCamelCase								=       image_processing_class





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






         """simple docstring"""







         UpperCAmelCase							=     OneFormerImageProcessorTester(self       )





      @property
      def        _lowercase				(		self						:					Any       )    ->   List[Any]:






         """simple docstring"""







         return self.image_processing_tester.prepare_image_processor_dict()





      def        _lowercase				(		self						:					Optional[Any]       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     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        _lowercase				(		self						:					Tuple       )    ->   int:






         """simple docstring"""







         pass





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






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         UpperCAmelCase							=     prepare_image_inputs(self.image_processing_tester					,       equal_resolution=__lowerCamelCase       )
         for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase					,       Image.Image       )

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

         UpperCAmelCase				,  UpperCAmelCase							=     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
         UpperCAmelCase				,  UpperCAmelCase							=     self.image_processing_tester.get_expected_values(__lowerCamelCase					,       batched=__lowerCamelCase       )

         UpperCAmelCase							=     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        _lowercase				(		self						:					str       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         UpperCAmelCase							=     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
         UpperCAmelCase							=     image_processor(image_inputs[0]					,       ["""semantic"""]					,       return_tensors="""pt"""       ).pixel_values

         UpperCAmelCase				,  UpperCAmelCase							=     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
         UpperCAmelCase				,  UpperCAmelCase							=     self.image_processing_tester.get_expected_values(__lowerCamelCase					,       batched=__lowerCamelCase       )

         UpperCAmelCase							=     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        _lowercase				(		self						:					Optional[Any]       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         UpperCAmelCase							=     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
         UpperCAmelCase							=     image_processor(image_inputs[0]					,       ["""semantic"""]					,       return_tensors="""pt"""       ).pixel_values

         UpperCAmelCase				,  UpperCAmelCase							=     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
         UpperCAmelCase				,  UpperCAmelCase							=     self.image_processing_tester.get_expected_values(__lowerCamelCase					,       batched=__lowerCamelCase       )

         UpperCAmelCase							=     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        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Optional[Any]=False					,       __lowerCamelCase						:					Tuple=False					,       __lowerCamelCase						:					int="np"       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class(**self.image_processor_dict       )
         # prepare image and target
         UpperCAmelCase							=     self.image_processing_tester.num_labels
         UpperCAmelCase							=     None
         UpperCAmelCase							=     None
         UpperCAmelCase							=     prepare_image_inputs(self.image_processing_tester					,       equal_resolution=__lowerCamelCase       )
         if with_segmentation_maps:
            UpperCAmelCase							=     num_labels
            if is_instance_map:
               UpperCAmelCase							=     list(range(__lowerCamelCase       )       ) * 2
               UpperCAmelCase							=     dict(enumerate(__lowerCamelCase       )       )
            UpperCAmelCase							=     [
                np.random.randint(0					,       high * 2					,       (img.size[1], img.size[0])       ).astype(np.uinta       ) for img in image_inputs
            ]
            if segmentation_type == "pil":
               UpperCAmelCase							=     [Image.fromarray(__lowerCamelCase       ) for annotation in annotations]

         UpperCAmelCase							=     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        _lowercase				(		self						:					str       )    ->   Any:






         """simple docstring"""







         pass





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






         """simple docstring"""







         def common(__lowerCamelCase						:					Tuple=False					,       __lowerCamelCase						:					List[str]=None       ):
            UpperCAmelCase							=     self.comm_get_image_processor_inputs(
                with_segmentation_maps=__lowerCamelCase					,       is_instance_map=__lowerCamelCase					,       segmentation_type=__lowerCamelCase       )

            UpperCAmelCase							=     inputs["""mask_labels"""]
            UpperCAmelCase							=     inputs["""class_labels"""]
            UpperCAmelCase							=     inputs["""pixel_values"""]
            UpperCAmelCase							=     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        _lowercase				(		self						:					List[str]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     np.zeros((2_0, 5_0)       )
         UpperCAmelCase							=     1
         UpperCAmelCase							=     1
         UpperCAmelCase							=     1

         UpperCAmelCase							=     binary_mask_to_rle(__lowerCamelCase       )
         self.assertEqual(len(__lowerCamelCase       )					,       4       )
         self.assertEqual(rle[0]					,       2_1       )
         self.assertEqual(rle[1]					,       4_5       )





      def        _lowercase				(		self						:					Dict       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     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"""					,       )
         UpperCAmelCase							=     self.image_processing_tester.get_fake_oneformer_outputs()

         UpperCAmelCase							=     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,
             )					,       )

         UpperCAmelCase							=     [(1, 4) for i in range(self.image_processing_tester.batch_size       )]
         UpperCAmelCase							=     fature_extractor.post_process_semantic_segmentation(__lowerCamelCase					,       target_sizes=__lowerCamelCase       )

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





      def        _lowercase				(		self						:					Any       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     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"""					,       )
         UpperCAmelCase							=     self.image_processing_tester.get_fake_oneformer_outputs()
         UpperCAmelCase							=     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        _lowercase				(		self						:					Dict       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     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"""					,       )
         UpperCAmelCase							=     self.image_processing_tester.get_fake_oneformer_outputs()
         UpperCAmelCase							=     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)       )







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a     =			logging.get_logger(__name__)

__a     =			{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''ctrl'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }





      def __init__(		self						:					str					,       __lowerCamelCase						:					Optional[int]=2_4_6_5_3_4					,       __lowerCamelCase						:					Union[str, Any]=2_5_6					,       __lowerCamelCase						:					int=1_2_8_0					,       __lowerCamelCase						:					Optional[Any]=8_1_9_2					,       __lowerCamelCase						:					List[str]=4_8					,       __lowerCamelCase						:					Dict=1_6					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=1e-6					,       __lowerCamelCase						:					str=0.02					,       __lowerCamelCase						:					Union[str, Any]=True					,       **__lowerCamelCase						:					List[str]					,       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     n_positions
         UpperCAmelCase							=     n_embd
         UpperCAmelCase							=     n_layer
         UpperCAmelCase							=     n_head
         UpperCAmelCase							=     dff
         UpperCAmelCase							=     resid_pdrop
         UpperCAmelCase							=     embd_pdrop
         UpperCAmelCase							=     layer_norm_epsilon
         UpperCAmelCase							=     initializer_range

         UpperCAmelCase							=     use_cache

         super().__init__(**__lowerCamelCase       )



