



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

class        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def __init__(       self							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[Any]=7,  lowerCamelCase							:  str=3,  lowerCamelCase							:  Optional[Any]=30,  lowerCamelCase							:  Union[str, Any]=400,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Optional[int]=True,  lowerCamelCase							:  str=[0.5, 0.5, 0.5],  lowerCamelCase							:  Optional[int]=[0.5, 0.5, 0.5],  lowerCamelCase							:  List[str]=10,  lowerCamelCase							:  int=False,  lowerCamelCase							:  str=255,  lowerCamelCase							:  List[Any]="shi-labs/oneformer_demo",  lowerCamelCase							:  Optional[Any]="ade20k_panoptic.json",  lowerCamelCase							:  str=10,  )->					Tuple:
								lowerCamelCase__		:		int			=parent
								lowerCamelCase__		:		List[Any]			=batch_size
								lowerCamelCase__		:		str			=num_channels
								lowerCamelCase__		:		Dict			=min_resolution
								lowerCamelCase__		:		int			=max_resolution
								lowerCamelCase__		:		Dict			=do_resize
								lowerCamelCase__		:		Union[str, Any]			={"shortest_edge": 32, "longest_edge": 1333} if size is None else size
								lowerCamelCase__		:		Optional[int]			=do_normalize
								lowerCamelCase__		:		Optional[int]			=image_mean
								lowerCamelCase__		:		Dict			=image_std
								lowerCamelCase__		:		List[str]			=class_info_file
								lowerCamelCase__		:		int			=prepare_metadata(snake_case__,  snake_case__     )
								lowerCamelCase__		:		Optional[int]			=num_text
								lowerCamelCase__		:		Any			=repo_path

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



					def 		snake_case       (       self							:  Optional[int]     )->					Dict:
								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 		snake_case       (       self							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[int]=False     )->					Tuple:


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

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

								return expected_height, expected_width




					def 		snake_case       (       self							:  Any     )->					List[Any]:
								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        __SCREAMING_SNAKE_CASE	(      A_		,   unittest.TestCase   ):


					'''simple docstring'''





					_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 		snake_case       (       self							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		Tuple			=OneFormerImageProcessorTester(self     )



					@property
					def 		snake_case       (       self							:  Optional[int]     )->					Optional[int]:
								return self.image_processing_tester.prepare_image_processor_dict()



					def 		snake_case       (       self							:  Optional[Any]     )->					List[str]:
								lowerCamelCase__		:		str			=self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(snake_case__,  '''image_mean'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''image_std'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''do_normalize'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''do_resize'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''size'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''ignore_index'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''class_info_file'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''num_text'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''repo_path'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''metadata'''     )     )
								self.assertTrue(hasattr(snake_case__,  '''do_reduce_labels'''     )     )



					def 		snake_case       (       self							:  Optional[int]     )->					str:
								pass



					def 		snake_case       (       self							:  Dict     )->					Any:
								lowerCamelCase__		:		Optional[int]			=self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								lowerCamelCase__		:		Dict			=prepare_image_inputs(self.image_processing_tester,  equal_resolution=snake_case__     )
								for image in image_inputs:
											self.assertIsInstance(snake_case__,  Image.Image     )

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

								lowerCamelCase__		:		Optional[Any]			=self.image_processing_tester.get_expected_values(snake_case__     )

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

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

								lowerCamelCase__		:		str			=image_processor(
								    snake_case__,  ['''semantic'''] * len(snake_case__     ),  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 		snake_case       (       self							:  Union[str, Any]     )->					Tuple:
								lowerCamelCase__		:		Union[str, Any]			=self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								lowerCamelCase__		:		List[str]			=prepare_image_inputs(self.image_processing_tester,  equal_resolution=snake_case__,  numpify=snake_case__     )
								for image in image_inputs:
											self.assertIsInstance(snake_case__,  np.ndarray     )

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

								lowerCamelCase__		:		int			=self.image_processing_tester.get_expected_values(snake_case__     )

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

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

								lowerCamelCase__		:		Optional[int]			=image_processor(
								    snake_case__,  ['''semantic'''] * len(snake_case__     ),  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 		snake_case       (       self							:  Union[str, Any]     )->					List[Any]:
								lowerCamelCase__		:		Union[str, Any]			=self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								lowerCamelCase__		:		Any			=prepare_image_inputs(self.image_processing_tester,  equal_resolution=snake_case__,  torchify=snake_case__     )
								for image in image_inputs:
											self.assertIsInstance(snake_case__,  torch.Tensor     )

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

								lowerCamelCase__		:		Union[str, Any]			=self.image_processing_tester.get_expected_values(snake_case__     )

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

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

								lowerCamelCase__		:		List[str]			=image_processor(
								    snake_case__,  ['''semantic'''] * len(snake_case__     ),  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 		snake_case       (       self							:  Dict,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  str="np"     )->					Optional[int]:
								lowerCamelCase__		:		Optional[int]			=self.image_processing_class(**self.image_processor_dict     )
								# prepare image and target
								lowerCamelCase__		:		int			=self.image_processing_tester.num_labels
								lowerCamelCase__		:		Tuple			=None
								lowerCamelCase__		:		List[str]			=None
								lowerCamelCase__		:		Optional[int]			=prepare_image_inputs(self.image_processing_tester,  equal_resolution=snake_case__     )
								if with_segmentation_maps:
											lowerCamelCase__		:		List[str]			=num_labels
											if is_instance_map:
														lowerCamelCase__		:		int			=list(range(snake_case__     )     ) * 2
														lowerCamelCase__		:		Union[str, Any]			=dict(enumerate(snake_case__     )     )
											lowerCamelCase__		:		Optional[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":
														lowerCamelCase__		:		List[str]			=[Image.fromarray(snake_case__     ) for annotation in annotations]

								lowerCamelCase__		:		Dict			=image_processor(
								    snake_case__,  ['''semantic'''] * len(snake_case__     ),  snake_case__,  return_tensors='''pt''',  instance_id_to_semantic_id=snake_case__,  pad_and_return_pixel_mask=snake_case__,  )

								return inputs



					def 		snake_case       (       self							:  int     )->					Dict:
								pass



					def 		snake_case       (       self							:  Any     )->					Dict:


								def common(lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  Optional[Any]=None     ):
											lowerCamelCase__		:		Dict			=self.comm_get_image_processor_inputs(
											    with_segmentation_maps=snake_case__,  is_instance_map=snake_case__,  segmentation_type=snake_case__     )

											lowerCamelCase__		:		List[str]			=inputs["mask_labels"]
											lowerCamelCase__		:		Tuple			=inputs["class_labels"]
											lowerCamelCase__		:		Dict			=inputs["pixel_values"]
											lowerCamelCase__		:		Tuple			=inputs["text_inputs"]

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

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



					def 		snake_case       (       self							:  List[Any]     )->					Union[str, Any]:
								lowerCamelCase__		:		Any			=np.zeros((20, 50)     )
								lowerCamelCase__		:		List[str]			=1
								lowerCamelCase__		:		List[str]			=1
								lowerCamelCase__		:		Optional[Any]			=1

								lowerCamelCase__		:		int			=binary_mask_to_rle(snake_case__     )
								self.assertEqual(len(snake_case__     ),  4     )
								self.assertEqual(rle[0],  21     )
								self.assertEqual(rle[1],  45     )



					def 		snake_case       (       self							:  Optional[Any]     )->					int:
								lowerCamelCase__		:		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''',  )
								lowerCamelCase__		:		Optional[int]			=self.image_processing_tester.get_fake_oneformer_outputs()

								lowerCamelCase__		:		Tuple			=fature_extractor.post_process_semantic_segmentation(snake_case__     )

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

								lowerCamelCase__		:		Union[str, Any]			=[(1, 4) for i in range(self.image_processing_tester.batch_size     )]
								lowerCamelCase__		:		Optional[int]			=fature_extractor.post_process_semantic_segmentation(snake_case__,  target_sizes=snake_case__     )

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



					def 		snake_case       (       self							:  Tuple     )->					List[str]:
								lowerCamelCase__		:		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''',  )
								lowerCamelCase__		:		int			=self.image_processing_tester.get_fake_oneformer_outputs()
								lowerCamelCase__		:		List[str]			=image_processor.post_process_instance_segmentation(snake_case__,  threshold=0     )

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




					def 		snake_case       (       self							:  str     )->					str:
								lowerCamelCase__		:		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''',  )
								lowerCamelCase__		:		Dict			=self.image_processing_tester.get_fake_oneformer_outputs()
								lowerCamelCase__		:		List[Any]			=image_processor.post_process_panoptic_segmentation(snake_case__,  threshold=0     )

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






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__lowerCamelCase							=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase							=       {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": 5_12,
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     VOCAB_FILES_NAMES
   A__					:		Optional[int]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Tuple						     =     PRETRAINED_INIT_CONFIGURATION
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		List[Any]						     =     LxmertTokenizer



   def __init__(self		:					Dict    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple="[UNK]"    ,						snake_case__		:					Optional[Any]="[SEP]"    ,						snake_case__		:					Optional[Any]="[PAD]"    ,						snake_case__		:					List[Any]="[CLS]"    ,						snake_case__		:					Tuple="[MASK]"    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Union[str, Any]=None    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(
              snake_case__    ,						tokenizer_file=snake_case__    ,						do_lower_case=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						pad_token=snake_case__    ,						cls_token=snake_case__    ,						mask_token=snake_case__    ,						tokenize_chinese_chars=snake_case__    ,						strip_accents=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   int							=      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get("lowercase"    ,						snake_case__ ) != do_lower_case
              or normalizer_state.get("strip_accents"    ,						snake_case__ ) != strip_accents
              or normalizer_state.get("handle_chinese_chars"    ,						snake_case__ ) != tokenize_chinese_chars
          ):
                 snake_case       :   Union[str, Any]							=      getattr(snake_case__    ,						normalizer_state.pop("type" ) )
                 snake_case       :   str							=      do_lower_case
                 snake_case       :   List[Any]							=      strip_accents
                 snake_case       :   Optional[int]							=      tokenize_chinese_chars
                 snake_case       :   int							=      normalizer_class(**snake_case__ )

          snake_case       :   Optional[Any]							=      do_lower_case



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict=None )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

          return output



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      [self.sep_token_id]
          snake_case       :   Optional[Any]							=      [self.cls_token_id]
          if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self._tokenizer.model.save(snake_case__    ,						name=snake_case__ )
          return tuple(snake_case__ )





