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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="shi-labs/oneformer_demo"     ):
       with open(hf_hub_download(lowerCAmelCase__     ,  lowerCAmelCase__     ,  repo_type='dataset'     )     ,  'r'     ) as f:
              lowerCAmelCase__					     = json.load(lowerCAmelCase__     )
       lowerCAmelCase__					     = {}
       lowerCAmelCase__					     = []
       lowerCAmelCase__					     = []
       for key, info in class_info.items():
              lowerCAmelCase__					     = info['name']
              class_names.append(info['name']     )
              if info["isthing"]:
                     thing_ids.append(int(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = thing_ids
       lowerCAmelCase__					     = class_names
       return metadata
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






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







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = num_channels
        lowerCAmelCase__					     = min_resolution
        lowerCAmelCase__					     = max_resolution
        lowerCAmelCase__					     = do_resize
        lowerCAmelCase__					     = {'shortest_edge': 32, 'longest_edge': 1_333} if size is None else size
        lowerCAmelCase__					     = do_normalize
        lowerCAmelCase__					     = image_mean
        lowerCAmelCase__					     = image_std
        lowerCAmelCase__					     = class_info_file
        lowerCAmelCase__					     = prepare_metadata(lowercase__      ,   lowercase__)
        lowerCAmelCase__					     = num_text
        lowerCAmelCase__					     = repo_path

        # for the post_process_functions
        lowerCAmelCase__					     = 2
        lowerCAmelCase__					     = 10
        lowerCAmelCase__					     = 10
        lowerCAmelCase__					     = 3
        lowerCAmelCase__					     = 4
        lowerCAmelCase__					     = num_labels
        lowerCAmelCase__					     = do_reduce_labels
        lowerCAmelCase__					     = ignore_index




 def 		__snake_case						(      self :  List[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 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  List[str]=False):







        '''simple docstring'''



        if not batched:
               lowerCAmelCase__					     = image_inputs[0]
               if isinstance(lowercase__      ,   Image.Image):
                      lowerCAmelCase__     , lowerCAmelCase__					     = image.size
               else:
                      lowerCAmelCase__     , lowerCAmelCase__					     = image.shape[1], image.shape[2]
               if w < h:
                      lowerCAmelCase__					     = int(self.size['shortest_edge'] * h / w)
                      lowerCAmelCase__					     = self.size['shortest_edge']
               elif w > h:
                      lowerCAmelCase__					     = self.size['shortest_edge']
                      lowerCAmelCase__					     = int(self.size['shortest_edge'] * w / h)
               else:
                      lowerCAmelCase__					     = self.size['shortest_edge']
                      lowerCAmelCase__					     = self.size['shortest_edge']

        else:
               lowerCAmelCase__					     = []
               for image in image_inputs:
                      lowerCAmelCase__     , lowerCAmelCase__					     = self.get_expected_values([image])
                      expected_values.append((expected_height, expected_width))
               lowerCAmelCase__					     = max(lowercase__      ,   key=lambda lowercase__: item[0])[0]
               lowerCAmelCase__					     = max(lowercase__      ,   key=lambda lowercase__: item[1])[1]

        return expected_height, expected_width




 def 		__snake_case						(      self :  Optional[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     a_			(						SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 UpperCAmelCase_     			= image_processing_class




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = OneFormerImageProcessorTester(self)




 @property
 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        return self.image_processing_tester.prepare_image_processor_dict()




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = 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 		__snake_case						(      self :  Any):







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__					     = prepare_image_inputs(self.image_processing_tester      ,   equal_resolution=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__      ,   Image.Image)

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

        lowerCAmelCase__     , lowerCAmelCase__					     = 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
        lowerCAmelCase__     , lowerCAmelCase__					     = self.image_processing_tester.get_expected_values(lowercase__      ,   batched=lowercase__)

        lowerCAmelCase__					     = 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 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        lowerCAmelCase__					     = 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
        lowerCAmelCase__					     = image_processor(image_inputs[0]      ,   ['semantic']      ,   return_tensors='pt').pixel_values

        lowerCAmelCase__     , lowerCAmelCase__					     = 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
        lowerCAmelCase__     , lowerCAmelCase__					     = self.image_processing_tester.get_expected_values(lowercase__      ,   batched=lowercase__)

        lowerCAmelCase__					     = 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 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        lowerCAmelCase__					     = 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
        lowerCAmelCase__					     = image_processor(image_inputs[0]      ,   ['semantic']      ,   return_tensors='pt').pixel_values

        lowerCAmelCase__     , lowerCAmelCase__					     = 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
        lowerCAmelCase__     , lowerCAmelCase__					     = self.image_processing_tester.get_expected_values(lowercase__      ,   batched=lowercase__)

        lowerCAmelCase__					     = 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 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  int=False      ,   lowercase__ :  Dict=False      ,   lowercase__ :  List[str]="np"):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processing_class(**self.image_processor_dict)
        # prepare image and target
        lowerCAmelCase__					     = self.image_processing_tester.num_labels
        lowerCAmelCase__					     = None
        lowerCAmelCase__					     = None
        lowerCAmelCase__					     = prepare_image_inputs(self.image_processing_tester      ,   equal_resolution=lowercase__)
        if with_segmentation_maps:
               lowerCAmelCase__					     = num_labels
               if is_instance_map:
                      lowerCAmelCase__					     = list(range(lowercase__)) * 2
                      lowerCAmelCase__					     = dict(enumerate(lowercase__))
               lowerCAmelCase__					     = [
                   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__					     = [Image.fromarray(lowercase__) for annotation in annotations]

        lowerCAmelCase__					     = 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 		__snake_case						(      self :  int):







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        def common(lowercase__ :  Tuple=False      ,   lowercase__ :  int=None):
               lowerCAmelCase__					     = self.comm_get_image_processor_inputs(
                   with_segmentation_maps=lowercase__      ,   is_instance_map=lowercase__      ,   segmentation_type=lowercase__)

               lowerCAmelCase__					     = inputs['mask_labels']
               lowerCAmelCase__					     = inputs['class_labels']
               lowerCAmelCase__					     = inputs['pixel_values']
               lowerCAmelCase__					     = 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 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = np.zeros((20, 50))
        lowerCAmelCase__					     = 1
        lowerCAmelCase__					     = 1
        lowerCAmelCase__					     = 1

        lowerCAmelCase__					     = binary_mask_to_rle(lowercase__)
        self.assertEqual(len(lowercase__)      ,   4)
        self.assertEqual(rle[0]      ,   21)
        self.assertEqual(rle[1]      ,   45)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = 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__					     = self.image_processing_tester.get_fake_oneformer_outputs()

        lowerCAmelCase__					     = 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,
            )      ,   )

        lowerCAmelCase__					     = [(1, 4) for i in range(self.image_processing_tester.batch_size)]
        lowerCAmelCase__					     = fature_extractor.post_process_semantic_segmentation(lowercase__      ,   target_sizes=lowercase__)

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




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = 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__					     = self.image_processing_tester.get_fake_oneformer_outputs()
        lowerCAmelCase__					     = 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 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = 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__					     = self.image_processing_tester.get_fake_oneformer_outputs()
        lowerCAmelCase__					     = 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))

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'xlm-roberta-xl'




 def __init__(      self :  int      ,   lowercase__ :  Optional[int]=250_880      ,   lowercase__ :  Union[str, Any]=2_560      ,   lowercase__ :  int=36      ,   lowercase__ :  int=32      ,   lowercase__ :  List[Any]=10_240      ,   lowercase__ :  Union[str, Any]="gelu"      ,   lowercase__ :  Dict=0.1      ,   lowercase__ :  Any=0.1      ,   lowercase__ :  Any=514      ,   lowercase__ :  Optional[int]=1      ,   lowercase__ :  Any=0.02      ,   lowercase__ :  List[str]=1e-05      ,   lowercase__ :  Dict=1      ,   lowercase__ :  Union[str, Any]=0      ,   lowercase__ :  Dict=2      ,   lowercase__ :  str="absolute"      ,   lowercase__ :  Dict=True      ,   lowercase__ :  List[Any]=None      ,   **lowercase__ :  Optional[Any]      ,   ):







        '''simple docstring'''



        super().__init__(pad_token_id=lowercase__      ,   bos_token_id=lowercase__      ,   eos_token_id=lowercase__      ,   **lowercase__)
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = hidden_act
        lowerCAmelCase__					     = intermediate_size
        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = type_vocab_size
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = layer_norm_eps
        lowerCAmelCase__					     = position_embedding_type
        lowerCAmelCase__					     = use_cache
        lowerCAmelCase__					     = classifier_dropout






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 @property
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        if self.task == "multiple-choice":
               lowerCAmelCase__					     = {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
               lowerCAmelCase__					     = {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ])
