




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




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Optional[int]       ,					_lowerCAmelCase:					Dict=7       ,					_lowerCAmelCase:					str=3       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					List[str]=None       ,					_lowerCAmelCase:					List[str]=True       ,					_lowerCAmelCase:					Dict=True       ,					_lowerCAmelCase:					Any=[0.5, 0.5, 0.5]       ,					_lowerCAmelCase:					Optional[Any]=[0.5, 0.5, 0.5]       ,					_lowerCAmelCase:					Any=10       ,					_lowerCAmelCase:					List[str]=False       ,					_lowerCAmelCase:					Union[str, Any]=2_55       ,					_lowerCAmelCase:					Optional[Any]="shi-labs/oneformer_demo"       ,					_lowerCAmelCase:					Dict="ade20k_panoptic.json"       ,					_lowerCAmelCase:					Optional[Any]=10       ,					):
         lowercase    :List[str]      	=						parent
         lowercase    :Optional[Any]      	=						batch_size
         lowercase    :Optional[int]      	=						num_channels
         lowercase    :str      	=						min_resolution
         lowercase    :Any      	=						max_resolution
         lowercase    :str      	=						do_resize
         lowercase    :Tuple      	=						{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
         lowercase    :List[str]      	=						do_normalize
         lowercase    :Union[str, Any]      	=						image_mean
         lowercase    :List[Any]      	=						image_std
         lowercase    :str      	=						class_info_file
         lowercase    :List[Any]      	=						prepare_metadata(_lowerCAmelCase       ,					_lowerCAmelCase				)
         lowercase    :List[Any]      	=						num_text
         lowercase    :Union[str, Any]      	=						repo_path

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






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

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

         return expected_height, expected_width






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         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    __lowerCAmelCase       (							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 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         lowercase    :Dict      	=						OneFormerImageProcessorTester(self				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         return self.image_processing_tester.prepare_image_processor_dict()






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Dict      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         pass






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         # Initialize image_processor
         lowercase    :Tuple      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :List[str]      	=						prepare_image_inputs(self.image_processing_tester       ,					equal_resolution=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					Image.Image				)

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

         lowercase    :int      	=						self.image_processing_tester.get_expected_values(_lowerCAmelCase				)

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

         # Test batched
         lowercase    :Optional[int]      	=						self.image_processing_tester.get_expected_values(_lowerCAmelCase       ,					batched=_lowerCAmelCase				)

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         # Initialize image_processor
         lowercase    :int      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         lowercase    :str      	=						prepare_image_inputs(self.image_processing_tester       ,					equal_resolution=_lowerCAmelCase       ,					numpify=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					np.ndarray				)

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

         lowercase    :List[Any]      	=						self.image_processing_tester.get_expected_values(_lowerCAmelCase				)

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

         # Test batched
         lowercase    :Dict      	=						self.image_processing_tester.get_expected_values(_lowerCAmelCase       ,					batched=_lowerCAmelCase				)

         lowercase    :Tuple      	=						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 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         # Initialize image_processor
         lowercase    :Optional[Any]      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         lowercase    :Optional[int]      	=						prepare_image_inputs(self.image_processing_tester       ,					equal_resolution=_lowerCAmelCase       ,					torchify=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					torch.Tensor				)

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

         lowercase    :Optional[int]      	=						self.image_processing_tester.get_expected_values(_lowerCAmelCase				)

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

         # Test batched
         lowercase    :Optional[Any]      	=						self.image_processing_tester.get_expected_values(_lowerCAmelCase       ,					batched=_lowerCAmelCase				)

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					_lowerCAmelCase:					List[str]=False       ,					_lowerCAmelCase:					Any=False       ,					_lowerCAmelCase:					Union[str, Any]="np"				):
         lowercase    :List[str]      	=						self.image_processing_class(**self.image_processor_dict				)
         # prepare image and target
         lowercase    :List[Any]      	=						self.image_processing_tester.num_labels
         lowercase    :Any      	=						None
         lowercase    :int      	=						None
         lowercase    :List[str]      	=						prepare_image_inputs(self.image_processing_tester       ,					equal_resolution=_lowerCAmelCase				)
         if with_segmentation_maps:
           lowercase    :str      	=						num_labels
           if is_instance_map:
             lowercase    :Any      	=						list(range(_lowerCAmelCase				)				) * 2
             lowercase    :int      	=						dict(enumerate(_lowerCAmelCase				)				)
           lowercase    :Any      	=						[
               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    :Tuple      	=						[Image.fromarray(_lowerCAmelCase				) for annotation in annotations]

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

         return inputs






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         def common(_lowerCAmelCase:					str=False       ,					_lowerCAmelCase:					Union[str, Any]=None				):
           lowercase    :Dict      	=						self.comm_get_image_processor_inputs(
               with_segmentation_maps=_lowerCAmelCase       ,					is_instance_map=_lowerCAmelCase       ,					segmentation_type=_lowerCAmelCase				)

           lowercase    :Tuple      	=						inputs["mask_labels"]
           lowercase    :List[str]      	=						inputs["class_labels"]
           lowercase    :Optional[Any]      	=						inputs["pixel_values"]
           lowercase    :List[Any]      	=						inputs["text_inputs"]

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :int      	=						np.zeros((20, 50)				)
         lowercase    :int      	=						1
         lowercase    :str      	=						1
         lowercase    :int      	=						1

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






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Optional[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    :Union[str, Any]      	=						self.image_processing_tester.get_fake_oneformer_outputs()

         lowercase    :Any      	=						fature_extractor.post_process_semantic_segmentation(_lowerCAmelCase				)

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

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Optional[Any]      	=						self.image_processing_class(
             num_labels=self.image_processing_tester.num_classes       ,					max_seq_length=77       ,					task_seq_length=77       ,					class_info_file="ade20k_panoptic.json"       ,					num_text=self.image_processing_tester.num_text       ,					repo_path="shi-labs/oneformer_demo"       ,					)
         lowercase    :int      	=						self.image_processing_tester.get_fake_oneformer_outputs()
         lowercase    :Optional[int]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :List[str]      	=						self.image_processing_class(
             num_labels=self.image_processing_tester.num_classes       ,					max_seq_length=77       ,					task_seq_length=77       ,					class_info_file="ade20k_panoptic.json"       ,					num_text=self.image_processing_tester.num_text       ,					repo_path="shi-labs/oneformer_demo"       ,					)
         lowercase    :Union[str, Any]      	=						self.image_processing_tester.get_fake_oneformer_outputs()
         lowercase    :Optional[int]      	=						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)				)






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment




@require_huggingface_suite
class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :List[str]      	=						inspect.getfile(accelerate.test_utils				)
         lowercase    :List[Any]      	=						os.path.sep.join(
             mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

         from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

         lowercase    :str      	=						test_metrics






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         debug_launcher(self.test_metrics.main       ,					num_processes=1				)






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         debug_launcher(self.test_metrics.main				)






       @require_single_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         self.test_metrics.main()






       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         print(F"Found {torch.cuda.device_count()} devices."				)
         lowercase    :str      	=						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
         with patch_environment(omp_num_threads=1				):
           execute_subprocess_async(_lowerCAmelCase       ,					env=os.environ.copy()				)



