


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''		,							[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''		,							num_bytes=13_37		,							num_examples=42		,							dataset_name='''my_dataset'''	)}	),
        SplitDict({'''train''': SplitInfo(name='''train'''		,							num_bytes=13_37		,							num_examples=42	)}	),
        SplitDict({'''train''': SplitInfo()}	),
    ]		,							)
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= split_dict._to_yaml_list()
		assert len(SCREAMING_SNAKE_CASE	) == len(SCREAMING_SNAKE_CASE	)
		lowercase__				= SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE	)
		for split_name, split_info in split_dict.items():
				# dataset_name field is deprecated, and is therefore not part of the YAML dump
				lowercase__				= None
				# the split name of split_dict takes over the name of the split info object
				lowercase__				= split_name
		assert split_dict == reloaded
@pytest.mark.parametrize(
    '''split_info'''		,							[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE	), SplitInfo(dataset_name='''my_dataset'''	)]	)
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= asdict(SplitDict({'''train''': split_info}	)	)
		assert "dataset_name" in split_dict_asdict["train"]
		assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name







"""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		( SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		List[Any]="shi-labs/oneformer_demo"							)    ->  Tuple:





     '''simple docstring'''

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






class       UpperCAmelCase_    (							unittest.TestCase		):



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

           # for the post_process_functions
           _UpperCAmelCase     :      str														=       2
           _UpperCAmelCase     :      Any														=       1_0
           _UpperCAmelCase     :      Optional[int]														=       1_0
           _UpperCAmelCase     :      Tuple														=       3
           _UpperCAmelCase     :      List[str]														=       4
           _UpperCAmelCase     :      int														=       num_labels
           _UpperCAmelCase     :      Optional[Any]														=       do_reduce_labels
           _UpperCAmelCase     :      Any														=       ignore_index



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

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

           return expected_height, expected_width



      def 		snake_case_   (	self	:      List[str]		):
           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_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  str						=					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		:  Tuple						=					image_processing_class



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Optional[Any]														=       OneFormerImageProcessorTester(self		)



      @property
      def 		snake_case_   (	self	:      Optional[Any]		):
           return self.image_processing_tester.prepare_image_processor_dict()



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      str														=       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 		snake_case_   (	self	:      List[Any]		):
           pass



      def 		snake_case_   (	self	:      Union[str, Any]		):
           # Initialize image_processor
           _UpperCAmelCase     :      List[str]														=       self.image_processing_class(**self.image_processor_dict		)
           # create random PIL images
           _UpperCAmelCase     :      Optional[int]														=       prepare_image_inputs(self.image_processing_tester	,      equal_resolution=A		)
           for image in image_inputs:
                self.assertIsInstance(A	,      Image.Image		)

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

           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[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
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      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 		snake_case_   (	self	:      str		):
           # Initialize image_processor
           _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_class(**self.image_processor_dict		)
           # create random numpy tensors
           _UpperCAmelCase     :      Any														=       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
           _UpperCAmelCase     :      int														=       image_processor(image_inputs[0]	,      ["semantic"]	,      return_tensors="pt"		).pixel_values

           _UpperCAmelCase							,			_UpperCAmelCase     :      List[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
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      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 		snake_case_   (	self	:      Optional[int]		):
           # Initialize image_processor
           _UpperCAmelCase     :      Dict														=       self.image_processing_class(**self.image_processor_dict		)
           # create random PyTorch tensors
           _UpperCAmelCase     :      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
           _UpperCAmelCase     :      Tuple														=       image_processor(image_inputs[0]	,      ["semantic"]	,      return_tensors="pt"		).pixel_values

           _UpperCAmelCase							,			_UpperCAmelCase     :      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
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[str]														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

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

           _UpperCAmelCase     :      int														=       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 		snake_case_   (	self	:      Any		):
           pass



      def 		snake_case_   (	self	:      Dict		):
           def common(A	:      List[Any]=False	,      A	:      List[str]=None		):
                _UpperCAmelCase     :      str														=       self.comm_get_image_processor_inputs(
                    with_segmentation_maps=A	,      is_instance_map=A	,      segmentation_type=A		)

                _UpperCAmelCase     :      Optional[int]														=       inputs["mask_labels"]
                _UpperCAmelCase     :      str														=       inputs["class_labels"]
                _UpperCAmelCase     :      List[str]														=       inputs["pixel_values"]
                _UpperCAmelCase     :      Any														=       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 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Optional[Any]														=       np.zeros((2_0, 5_0)		)
           _UpperCAmelCase     :      Dict														=       1
           _UpperCAmelCase     :      Optional[int]														=       1
           _UpperCAmelCase     :      Optional[int]														=       1

           _UpperCAmelCase     :      List[str]														=       binary_mask_to_rle(A		)
           self.assertEqual(len(A		)	,      4		)
           self.assertEqual(rle[0]	,      2_1		)
           self.assertEqual(rle[1]	,      4_5		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Optional[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"	,      )
           _UpperCAmelCase     :      int														=       self.image_processing_tester.get_fake_oneformer_outputs()

           _UpperCAmelCase     :      List[str]														=       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,
               )	,      )

           _UpperCAmelCase     :      Tuple														=       [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
           _UpperCAmelCase     :      Optional[Any]														=       fature_extractor.post_process_semantic_segmentation(A	,      target_sizes=A		)

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



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Tuple														=       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"	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       self.image_processing_tester.get_fake_oneformer_outputs()
           _UpperCAmelCase     :      Tuple														=       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 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[int]														=       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"	,      )
           _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_tester.get_fake_oneformer_outputs()
           _UpperCAmelCase     :      Tuple														=       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)		)
