





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





class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=7					,  lowercase=3					,  lowercase=30					,  lowercase=400					,  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=255					,  lowercase="shi-labs/oneformer_demo"					,  lowercase="ade20k_panoptic.json"					,  lowercase=10					,  )			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				parent
          a__						:	Optional[Any] =				batch_size
          a__						:	Union[str, Any] =				num_channels
          a__						:	Any =				min_resolution
          a__						:	Optional[Any] =				max_resolution
          a__						:	Optional[int] =				do_resize
          a__						:	Any =				{'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
          a__						:	Optional[Any] =				do_normalize
          a__						:	Tuple =				image_mean
          a__						:	List[Any] =				image_std
          a__						:	Optional[int] =				class_info_file
          a__						:	Union[str, Any] =				prepare_metadata(lowercase					,  lowercase)
          a__						:	List[Any] =				num_text
          a__						:	Dict =				repo_path

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

   def 							__lowercase		(					self)			->						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					,  lowercase					,  lowercase=False)			->						List[Any]:







          '''simple docstring'''

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

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

          return expected_height, expected_width

   def 							__lowercase		(					self)			->						List[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  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Dict         =  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   __A   :				int         =  image_processing_class

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	int =				OneFormerImageProcessorTester(self)

   @property
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          return self.image_processing_tester.prepare_image_processor_dict()

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







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	str =				self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          a__						:	Tuple =				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__						:	Tuple =				image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt').pixel_values

          a__ ,							a__						:	Union[str, 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__						:	List[Any] =				self.image_processing_tester.get_expected_values(lowercase					,  batched=lowercase)

          a__						:	Optional[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 							__lowercase		(					self)			->						Union[str, 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[int] =				image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt').pixel_values

          a__ ,							a__						:	List[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__						:	str =				self.image_processing_tester.get_expected_values(lowercase					,  batched=lowercase)

          a__						:	Optional[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 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[str] =				self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          a__						:	Optional[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__						:	str =				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__						:	Optional[Any] =				self.image_processing_tester.get_expected_values(lowercase					,  batched=lowercase)

          a__						:	Optional[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 							__lowercase		(					self					,  lowercase=False					,  lowercase=False					,  lowercase="np")			->						Tuple:







          '''simple docstring'''

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

          a__						:	Optional[int] =				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 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          pass

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







          '''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__						:	str =				inputs['mask_labels']
                 a__						:	List[Any] =				inputs['class_labels']
                 a__						:	Tuple =				inputs['pixel_values']
                 a__						:	Dict =				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 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

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

          a__						:	Optional[int] =				binary_mask_to_rle(lowercase)
          self.assertEqual(len(lowercase)					,  4)
          self.assertEqual(rle[0]					,  21)
          self.assertEqual(rle[1]					,  45)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

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

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

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







          '''simple docstring'''

          a__						:	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__						:	Any =				self.image_processing_tester.get_fake_oneformer_outputs()
          a__						:	Optional[Any] =				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 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Optional[int] =				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__						:	str =				self.image_processing_tester.get_fake_oneformer_outputs()
          a__						:	str =				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 argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_   : Dict   			= logging.getLogger(__name__)

if __name__ == "__main__":
  UpperCAmelCase_   : List[str]   			= argparse.ArgumentParser(
      description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
  )
  parser.add_argument(
      """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
  )
  parser.add_argument(
      """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
  )
  parser.add_argument("""--vocab_size""", default=30522, type=int)
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()

  logger.info(f'''Loading data from {args.data_file}''')
  with open(args.data_file, """rb""") as fp:
    UpperCAmelCase_   : Union[str, Any]   			= pickle.load(fp)

  logger.info("""Counting occurrences for MLM.""")
  UpperCAmelCase_   : Any   			= Counter()
  for tk_ids in data:
    counter.update(tk_ids)
  UpperCAmelCase_   : List[Any]   			= [0] * args.vocab_size
  for k, v in counter.items():
    UpperCAmelCase_   : Dict   			= v

  logger.info(f'''Dump to {args.token_counts_dump}''')
  with open(args.token_counts_dump, """wb""") as handle:
    pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


