


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




class 			lowerCamelCase_   (	unittest.TestCase      ):






      def __init__(					self				:    Tuple						,						_A				:    List[str]						,						_A				:    List[str]=7						,						_A				:    str=3						,						_A				:    Union[str, Any]=30						,						_A				:    int=400						,						_A				:    Optional[int]=None						,						_A				:    Dict=True						,						_A				:    Optional[int]=True						,						_A				:    List[Any]=[0.5, 0.5, 0.5]						,						_A				:    List[str]=[0.5, 0.5, 0.5]						,						_A				:    int=10						,						_A				:    Any=False						,						_A				:    List[Any]=255						,						_A				:    str="shi-labs/oneformer_demo"						,						_A				:    Dict="ade20k_panoptic.json"						,						_A				:    List[str]=10						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				parent
          UpperCAmelCase__			:		str													=				batch_size
          UpperCAmelCase__			:		Union[str, Any]													=				num_channels
          UpperCAmelCase__			:		Optional[int]													=				min_resolution
          UpperCAmelCase__			:		List[str]													=				max_resolution
          UpperCAmelCase__			:		List[Any]													=				do_resize
          UpperCAmelCase__			:		List[Any]													=				{'''shortest_edge''': 32, '''longest_edge''': 1_333} if size is None else size
          UpperCAmelCase__			:		Optional[int]													=				do_normalize
          UpperCAmelCase__			:		Any													=				image_mean
          UpperCAmelCase__			:		Union[str, Any]													=				image_std
          UpperCAmelCase__			:		int													=				class_info_file
          UpperCAmelCase__			:		str													=				prepare_metadata(_A						,						_A )
          UpperCAmelCase__			:		List[str]													=				num_text
          UpperCAmelCase__			:		Optional[int]													=				repo_path

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






      def lowercase_							(					self				:    Optional[int] ):


          '''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				:    Any						,						_A				:    str						,						_A				:    int=False ):


          '''simple docstring'''


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

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

          return expected_height, expected_width






      def lowercase_							(					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 			lowerCamelCase_   (	__a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      lowerCAmelCase__		     =     image_processing_class






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				OneFormerImageProcessorTester(self )






      @property
      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          return self.image_processing_tester.prepare_image_processor_dict()






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(_A						,						'''image_mean''' ) )
          self.assertTrue(hasattr(_A						,						'''image_std''' ) )
          self.assertTrue(hasattr(_A						,						'''do_normalize''' ) )
          self.assertTrue(hasattr(_A						,						'''do_resize''' ) )
          self.assertTrue(hasattr(_A						,						'''size''' ) )
          self.assertTrue(hasattr(_A						,						'''ignore_index''' ) )
          self.assertTrue(hasattr(_A						,						'''class_info_file''' ) )
          self.assertTrue(hasattr(_A						,						'''num_text''' ) )
          self.assertTrue(hasattr(_A						,						'''repo_path''' ) )
          self.assertTrue(hasattr(_A						,						'''metadata''' ) )
          self.assertTrue(hasattr(_A						,						'''do_reduce_labels''' ) )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          UpperCAmelCase__			:		List[str]													=				prepare_image_inputs(self.image_processing_tester						,						equal_resolution=_A )
          for image in image_inputs:
              self.assertIsInstance(_A						,						Image.Image )

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

          UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				self.image_processing_tester.get_expected_values(_A )

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

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

          UpperCAmelCase__			:		Dict													=				image_processor(
              _A						,						['''semantic'''] * len(_A )						,						return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_images.shape						,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )						,						)






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          UpperCAmelCase__			:		Optional[Any]													=				prepare_image_inputs(self.image_processing_tester						,						equal_resolution=_A						,						numpify=_A )
          for image in image_inputs:
              self.assertIsInstance(_A						,						np.ndarray )

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

          UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				self.image_processing_tester.get_expected_values(_A )

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

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

          UpperCAmelCase__			:		Optional[int]													=				image_processor(
              _A						,						['''semantic'''] * len(_A )						,						return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_images.shape						,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )						,						)






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          UpperCAmelCase__			:		Optional[int]													=				prepare_image_inputs(self.image_processing_tester						,						equal_resolution=_A						,						torchify=_A )
          for image in image_inputs:
              self.assertIsInstance(_A						,						torch.Tensor )

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

          UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				self.image_processing_tester.get_expected_values(_A )

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

          # Test batched
          UpperCAmelCase__						,						UpperCAmelCase__			:		Any													=				self.image_processing_tester.get_expected_values(_A						,						batched=_A )

          UpperCAmelCase__			:		str													=				image_processor(
              _A						,						['''semantic'''] * len(_A )						,						return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_images.shape						,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )						,						)






      def lowercase_							(					self				:    str						,						_A				:    Optional[int]=False						,						_A				:    Optional[Any]=False						,						_A				:    Any="np" ):


          '''simple docstring'''


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

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

          return inputs






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          def common(_A				:    Dict=False						,						_A				:    Tuple=None ):
              UpperCAmelCase__			:		Union[str, Any]													=				self.comm_get_image_processor_inputs(
                  with_segmentation_maps=_A						,						is_instance_map=_A						,						segmentation_type=_A )

              UpperCAmelCase__			:		Tuple													=				inputs['''mask_labels''']
              UpperCAmelCase__			:		str													=				inputs['''class_labels''']
              UpperCAmelCase__			:		Dict													=				inputs['''pixel_values''']
              UpperCAmelCase__			:		Optional[Any]													=				inputs['''text_inputs''']

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

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






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				np.zeros((20, 50) )
          UpperCAmelCase__			:		Any													=				1
          UpperCAmelCase__			:		str													=				1
          UpperCAmelCase__			:		Tuple													=				1

          UpperCAmelCase__			:		Any													=				binary_mask_to_rle(_A )
          self.assertEqual(len(_A )						,						4 )
          self.assertEqual(rle[0]						,						21 )
          self.assertEqual(rle[1]						,						45 )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				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'''						,						)
          UpperCAmelCase__			:		Tuple													=				self.image_processing_tester.get_fake_oneformer_outputs()

          UpperCAmelCase__			:		List[Any]													=				fature_extractor.post_process_semantic_segmentation(_A )

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

          UpperCAmelCase__			:		int													=				[(1, 4) for i in range(self.image_processing_tester.batch_size )]
          UpperCAmelCase__			:		Tuple													=				fature_extractor.post_process_semantic_segmentation(_A						,						target_sizes=_A )

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






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, 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'''						,						)
          UpperCAmelCase__			:		List[Any]													=				self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase__			:		List[Any]													=				image_processor.post_process_instance_segmentation(_A						,						threshold=0 )

          self.assertTrue(len(_A ) == self.image_processing_tester.batch_size )
          for el in segmentation:
              self.assertTrue('''segmentation''' in el )
              self.assertTrue('''segments_info''' in el )
              self.assertEqual(type(el['''segments_info'''] )						,						_A )
              self.assertEqual(
                  el['''segmentation'''].shape						,						(self.image_processing_tester.height, self.image_processing_tester.width) )






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, 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'''						,						)
          UpperCAmelCase__			:		Optional[int]													=				self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase__			:		Dict													=				image_processor.post_process_panoptic_segmentation(_A						,						threshold=0 )

          self.assertTrue(len(_A ) == self.image_processing_tester.batch_size )
          for el in segmentation:
              self.assertTrue('''segmentation''' in el )
              self.assertTrue('''segments_info''' in el )
              self.assertEqual(type(el['''segments_info'''] )						,						_A )
              self.assertEqual(
                  el['''segmentation'''].shape						,						(self.image_processing_tester.height, self.image_processing_tester.width) )




'''simple docstring'''
import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
  # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
  # setup instructions, if using on-demand hardware

  # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
  # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
  # Throw an error if user passes both BYO and on-demand cluster args
  # Otherwise, use default values
  UpperCamelCase__    					=      argparse.ArgumentParser()
  parser.add_argument('''--user''', type=str, default='''ubuntu''')
  parser.add_argument('''--host''', type=str, default='''localhost''')
  parser.add_argument('''--key_path''', type=str, default=None)
  parser.add_argument('''--instance''', type=str, default='''V100:1''')
  parser.add_argument('''--provider''', type=str, default='''cheapest''')
  parser.add_argument('''--use_spot''', type=bool, default=False)
  parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
  UpperCamelCase__					,   UpperCamelCase__    					=      parser.parse_known_args()
  if args.host != "localhost":
    if args.instance != "V100:1" or args.provider != "cheapest":
      raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
    )
  else:
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
    )
  UpperCamelCase__    					=      args.example.rsplit('''/''', 1)[0]

  # Set up remote environment
  cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
  # Note transformers is copied into the home directory on the remote machine, so we can install from there
  cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
  cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

  # Run example. You can bypass the CLI wrapper and paste your own code here.
  cluster.run([F"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

  # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
  # from my_script... import train
  # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
  # launch_train_gpu = rh.function(fn=train,
  #                                system=gpu,
  #                                reqs=reqs,
  #                                name='train_bert_glue')
  #
  # We can pass in arguments just like we would to a function:
  # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
  #                  stream_logs=True)
