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



class 		UpperCamelCase__  (	unittest.TestCase   ):
   def __init__(self			: Optional[Any] ,    snake_case_			: Optional[int] ,    snake_case_			: List[Any]=7 ,    snake_case_			: int=3 ,    snake_case_			: List[Any]=3_0 ,    snake_case_			: List[Any]=4_0_0 ,    snake_case_			: Union[str, Any]=None ,    snake_case_			: Optional[Any]=True ,    snake_case_			: Tuple=True ,    snake_case_			: Dict=[0.5, 0.5, 0.5] ,    snake_case_			: Tuple=[0.5, 0.5, 0.5] ,    snake_case_			: List[Any]=1_0 ,    snake_case_			: List[Any]=False ,    snake_case_			: Dict=2_5_5 ,    snake_case_			: List[Any]="shi-labs/oneformer_demo" ,    snake_case_			: int="ade20k_panoptic.json" ,    snake_case_			: List[Any]=1_0 ,    ):
    __a			:  List[Any]		    =  parent
    __a			:  List[Any]		    =  batch_size
    __a			:  Any		    =  num_channels
    __a			:  Tuple		    =  min_resolution
    __a			:  int		    =  max_resolution
    __a			:  Optional[Any]		    =  do_resize
    __a			:  Tuple		    =  {'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
    __a			:  Dict		    =  do_normalize
    __a			:  Optional[int]		    =  image_mean
    __a			:  Dict		    =  image_std
    __a			:  Tuple		    =  class_info_file
    __a			:  int		    =  prepare_metadata(snake_case_ ,    snake_case_       )
    __a			:  Optional[Any]		    =  num_text
    __a			:  Any		    =  repo_path

    # for the post_process_functions
    __a			:  Any		    =  2
    __a			:  Union[str, Any]		    =  1_0
    __a			:  Optional[Any]		    =  1_0
    __a			:  str		    =  3
    __a			:  str		    =  4
    __a			:  Optional[Any]		    =  num_labels
    __a			:  Union[str, Any]		    =  do_reduce_labels
    __a			:  Optional[Any]		    =  ignore_index
   def 				lowerCAmelCase      (self			: List[str]       ):
    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 				lowerCAmelCase      (self			: Any ,    snake_case_			: Optional[Any] ,    snake_case_			: int=False       ):
    if not batched:
     __a			:  Optional[int]		    =  image_inputs[0]
     if isinstance(snake_case_ ,    Image.Image       ):
      __a    ,	__a			:  Dict		    =  image.size
     else:
      __a    ,	__a			:  int		    =  image.shape[1], image.shape[2]
     if w < h:
      __a			:  Dict		    =  int(self.size['''shortest_edge'''] * h / w       )
      __a			:  int		    =  self.size['''shortest_edge''']
     elif w > h:
      __a			:  int		    =  self.size['''shortest_edge''']
      __a			:  str		    =  int(self.size['''shortest_edge'''] * w / h       )
     else:
      __a			:  List[Any]		    =  self.size['''shortest_edge''']
      __a			:  str		    =  self.size['''shortest_edge''']

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

    return expected_height, expected_width







   def 				lowerCAmelCase      (self			: Union[str, 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 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Dict     	=				OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				image_processing_class
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Dict		    =  OneFormerImageProcessorTester(self       )
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    return self.image_processing_tester.prepare_image_processor_dict()
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  Dict		    =  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 				lowerCAmelCase      (self			: Optional[int]       ):
    pass
   def 				lowerCAmelCase      (self			: str       ):
    # Initialize image_processor
    __a			:  Optional[Any]		    =  self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    __a			:  List[str]		    =  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
    __a			:  Any		    =  image_processor(image_inputs[0] ,    ['''semantic'''] ,    return_tensors='''pt'''       ).pixel_values

    __a    ,	__a			:  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
    __a    ,	__a			:  Union[str, Any]		    =  self.image_processing_tester.get_expected_values(snake_case_ ,    batched=snake_case_       )

    __a			:  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 				lowerCAmelCase      (self			: List[Any]       ):
    # Initialize image_processor
    __a			:  List[Any]		    =  self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    __a			:  int		    =  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
    __a			:  Tuple		    =  image_processor(image_inputs[0] ,    ['''semantic'''] ,    return_tensors='''pt'''       ).pixel_values

    __a    ,	__a			:  str		    =  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
    __a    ,	__a			:  Optional[int]		    =  self.image_processing_tester.get_expected_values(snake_case_ ,    batched=snake_case_       )

    __a			:  List[Any]		    =  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 				lowerCAmelCase      (self			: List[Any]       ):
    # Initialize image_processor
    __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=snake_case_ ,    torchify=snake_case_       )
    for image in image_inputs:
     self.assertIsInstance(snake_case_ ,    torch.Tensor       )

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

    __a    ,	__a			:  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
    __a    ,	__a			:  str		    =  self.image_processing_tester.get_expected_values(snake_case_ ,    batched=snake_case_       )

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

    __a			:  Tuple		    =  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 				lowerCAmelCase      (self			: str       ):
    pass
   def 				lowerCAmelCase      (self			: int       ):
    def common(snake_case_			: Any=False ,    snake_case_			: Union[str, Any]=None       ):
     __a			:  int		    =  self.comm_get_image_processor_inputs(
         with_segmentation_maps=snake_case_ ,    is_instance_map=snake_case_ ,    segmentation_type=snake_case_       )

     __a			:  int		    =  inputs['''mask_labels''']
     __a			:  Optional[Any]		    =  inputs['''class_labels''']
     __a			:  List[Any]		    =  inputs['''pixel_values''']
     __a			:  Optional[Any]		    =  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 				lowerCAmelCase      (self			: Dict       ):
    __a			:  Dict		    =  np.zeros((2_0, 5_0)       )
    __a			:  Tuple		    =  1
    __a			:  int		    =  1
    __a			:  List[Any]		    =  1

    __a			:  Dict		    =  binary_mask_to_rle(snake_case_       )
    self.assertEqual(len(snake_case_       ) ,    4       )
    self.assertEqual(rle[0] ,    2_1       )
    self.assertEqual(rle[1] ,    4_5       )
   def 				lowerCAmelCase      (self			: Dict       ):
    __a			:  Dict		    =  self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,    max_seq_length=7_7 ,    task_seq_length=7_7 ,    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			:  Optional[int]		    =  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,
        ) ,    )

    __a			:  List[str]		    =  [(1, 4) for i in range(self.image_processing_tester.batch_size       )]
    __a			:  str		    =  fature_extractor.post_process_semantic_segmentation(snake_case_ ,    target_sizes=snake_case_       )

    self.assertEqual(segmentation[0].shape ,    target_sizes[0]       )
   def 				lowerCAmelCase      (self			: Any       ):
    __a			:  List[Any]		    =  self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,    max_seq_length=7_7 ,    task_seq_length=7_7 ,    class_info_file='''ade20k_panoptic.json''' ,    num_text=self.image_processing_tester.num_text ,    repo_path='''shi-labs/oneformer_demo''' ,    )
    __a			:  Optional[Any]		    =  self.image_processing_tester.get_fake_oneformer_outputs()
    __a			:  List[Any]		    =  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 				lowerCAmelCase      (self			: Any       ):
    __a			:  List[str]		    =  self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,    max_seq_length=7_7 ,    task_seq_length=7_7 ,    class_info_file='''ade20k_panoptic.json''' ,    num_text=self.image_processing_tester.num_text ,    repo_path='''shi-labs/oneformer_demo''' ,    )
    __a			:  List[str]		    =  self.image_processing_tester.get_fake_oneformer_outputs()
    __a			:  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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
