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					(   lowercase__	:      Any  ,	lowercase__	:      Any="shi-labs/oneformer_demo"			):







     '''simple docstring'''


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

class    snake_case       (   unittest.TestCase    ):




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




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

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




  def 						__lowercase(   self  :						Dict					)->							Dict:




       """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  :						Optional[int]  ,			a_  :						Optional[int]  ,			a_  :						Dict=False					)->							str:




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

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

       return expected_height, expected_width




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




       """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    snake_case       (   UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  lowercase_								=  image_processing_class




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		OneFormerImageProcessorTester(self					)




  @property
  def 						__lowercase(   self  :						Tuple					)->							Optional[Any]:




       """simple docstring"""
       return self.image_processing_tester.prepare_image_processor_dict()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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  :						Dict					)->							List[Any]:




       """simple docstring"""
       pass




  def 						__lowercase(   self  :						str					)->							Tuple:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE__					:					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
       SCREAMING_SNAKE_CASE__					:					Dict      =		image_processor(image_inputs[0]  ,			['semantic']  ,			return_tensors='pt'					).pixel_values

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					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
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					str      =		self.image_processing_tester.get_expected_values(a_  ,			batched=a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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  :						int					)->							Union[str, Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__					:					List[str]      =		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
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		image_processor(image_inputs[0]  ,			['semantic']  ,			return_tensors='pt'					).pixel_values

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Dict      =		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
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.image_processing_tester.get_expected_values(a_  ,			batched=a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		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  :						Union[str, Any]					)->							Optional[Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__					:					str      =		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
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image_processor(image_inputs[0]  ,			['semantic']  ,			return_tensors='pt'					).pixel_values

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		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
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		self.image_processing_tester.get_expected_values(a_  ,			batched=a_					)

       SCREAMING_SNAKE_CASE__					:					List[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_  :						str="np"					)->							Dict:




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

       SCREAMING_SNAKE_CASE__					:					Dict      =		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  :						Any					)->							List[str]:




       """simple docstring"""
       pass




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




       """simple docstring"""
       def common(a_  :						Optional[Any]=False  ,			a_  :						Tuple=None					):
            SCREAMING_SNAKE_CASE__					:					int      =		self.comm_get_image_processor_inputs(
                with_segmentation_maps=a_  ,			is_instance_map=a_  ,			segmentation_type=a_					)

            SCREAMING_SNAKE_CASE__					:					Tuple      =		inputs['mask_labels']
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		inputs['class_labels']
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		inputs['pixel_values']
            SCREAMING_SNAKE_CASE__					:					List[str]      =		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]					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		np.zeros((20, 50)					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		1
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		1
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		1

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					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'  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.image_processing_tester.get_fake_oneformer_outputs()

       SCREAMING_SNAKE_CASE__					:					int      =		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,
           )  ,			)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[(1, 4) for i in range(self.image_processing_tester.batch_size					)]
       SCREAMING_SNAKE_CASE__					:					List[str]      =		fature_extractor.post_process_semantic_segmentation(a_  ,			target_sizes=a_					)

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					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'  ,			)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.image_processing_tester.get_fake_oneformer_outputs()
       SCREAMING_SNAKE_CASE__					:					Optional[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  :						Union[str, Any]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					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'  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.image_processing_tester.get_fake_oneformer_outputs()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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)					)










from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)