

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




class        A_ (				unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self :     List[Any]     ,			lowercase_ :     int     ,			lowercase_ :     Tuple=7     ,			lowercase_ :     List[str]=3     ,			lowercase_ :     Optional[Any]=30     ,			lowercase_ :     Optional[Any]=400     ,			lowercase_ :     List[str]=None     ,			lowercase_ :     Optional[int]=True     ,			lowercase_ :     List[Any]=True     ,			lowercase_ :     List[str]=[0.5, 0.5, 0.5]     ,			lowercase_ :     Dict=[0.5, 0.5, 0.5]     ,			lowercase_ :     Optional[Any]=10     ,			lowercase_ :     Optional[int]=False     ,			lowercase_ :     Optional[Any]=255     ,			lowercase_ :     List[str]="shi-labs/oneformer_demo"     ,			lowercase_ :     Tuple="ade20k_panoptic.json"     ,			lowercase_ :     List[Any]=10     ,			)						->	Dict:
      UpperCAmelCase       :	Dict											= parent
      UpperCAmelCase       :	Dict											= batch_size
      UpperCAmelCase       :	Dict											= num_channels
      UpperCAmelCase       :	Optional[int]											= min_resolution
      UpperCAmelCase       :	Any											= max_resolution
      UpperCAmelCase       :	List[Any]											= do_resize
      UpperCAmelCase       :	List[Any]											= {'shortest_edge': 32, 'longest_edge': 1_333} if size is None else size
      UpperCAmelCase       :	str											= do_normalize
      UpperCAmelCase       :	Any											= image_mean
      UpperCAmelCase       :	Optional[Any]											= image_std
      UpperCAmelCase       :	Optional[Any]											= class_info_file
      UpperCAmelCase       :	Any											= prepare_metadata(lowerCAmelCase_     ,			lowerCAmelCase_       )
      UpperCAmelCase       :	List[Any]											= num_text
      UpperCAmelCase       :	str											= repo_path

      # for the post_process_functions
      UpperCAmelCase       :	List[str]											= 2
      UpperCAmelCase       :	str											= 10
      UpperCAmelCase       :	List[Any]											= 10
      UpperCAmelCase       :	Union[str, Any]											= 3
      UpperCAmelCase       :	Any											= 4
      UpperCAmelCase       :	Dict											= num_labels
      UpperCAmelCase       :	Tuple											= do_reduce_labels
      UpperCAmelCase       :	Union[str, Any]											= ignore_index


     def    UpperCAmelCase_       (		self :     Dict       )						->	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    UpperCAmelCase_       (		self :     Optional[Any]     ,			lowercase_ :     str     ,			lowercase_ :     Optional[Any]=False       )						->	List[str]:
      if not batched:
       UpperCAmelCase       :	Optional[int]											= image_inputs[0]
       if isinstance(lowerCAmelCase_     ,			Image.Image       ):
        UpperCAmelCase					, UpperCAmelCase       :	int											= image.size
       else:
        UpperCAmelCase					, UpperCAmelCase       :	List[str]											= image.shape[1], image.shape[2]
       if w < h:
        UpperCAmelCase       :	Any											= int(self.size['shortest_edge'] * h / w       )
        UpperCAmelCase       :	List[str]											= self.size['shortest_edge']
       elif w > h:
        UpperCAmelCase       :	List[str]											= self.size['shortest_edge']
        UpperCAmelCase       :	Optional[Any]											= int(self.size['shortest_edge'] * w / h       )
       else:
        UpperCAmelCase       :	List[str]											= self.size['shortest_edge']
        UpperCAmelCase       :	Dict											= self.size['shortest_edge']

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

      return expected_height, expected_width




     def    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	Dict:
      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_ (				_UpperCamelCase       ,   unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	List[Any]  		=  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     UpperCAmelCase_						:	int  		=  image_processing_class


     def    UpperCAmelCase_       (		self :     int       )						->	List[Any]:
      UpperCAmelCase       :	Tuple											= OneFormerImageProcessorTester(self       )


     @property
     def    UpperCAmelCase_       (		self :     Any       )						->	Union[str, Any]:
      return self.image_processing_tester.prepare_image_processor_dict()


     def    UpperCAmelCase_       (		self :     Any       )						->	Dict:
      UpperCAmelCase       :	int											= self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'image_mean'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'image_std'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'do_normalize'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'do_resize'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'size'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'ignore_index'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'class_info_file'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'num_text'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'repo_path'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'metadata'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'do_reduce_labels'       )       )


     def    UpperCAmelCase_       (		self :     str       )						->	Tuple:
      pass


     def    UpperCAmelCase_       (		self :     Dict       )						->	List[Any]:
      # Initialize image_processor
      UpperCAmelCase       :	List[Any]											= self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      UpperCAmelCase       :	Optional[Any]											= prepare_image_inputs(self.image_processing_tester     ,			equal_resolution=lowerCAmelCase_       )
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase_     ,			Image.Image       )

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

      UpperCAmelCase					, UpperCAmelCase       :	Dict											= self.image_processing_tester.get_expected_values(lowerCAmelCase_       )

      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(lowerCAmelCase_     ,			batched=lowerCAmelCase_       )

      UpperCAmelCase       :	Dict											= image_processor(
          lowerCAmelCase_     ,			['semantic'] * len(lowerCAmelCase_       )     ,			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    UpperCAmelCase_       (		self :     Optional[Any]       )						->	Optional[Any]:
      # Initialize image_processor
      UpperCAmelCase       :	List[Any]											= self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      UpperCAmelCase       :	List[str]											= prepare_image_inputs(self.image_processing_tester     ,			equal_resolution=lowerCAmelCase_     ,			numpify=lowerCAmelCase_       )
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase_     ,			np.ndarray       )

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

      UpperCAmelCase					, UpperCAmelCase       :	Any											= self.image_processing_tester.get_expected_values(lowerCAmelCase_       )

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

      # Test batched
      UpperCAmelCase					, UpperCAmelCase       :	Any											= self.image_processing_tester.get_expected_values(lowerCAmelCase_     ,			batched=lowerCAmelCase_       )

      UpperCAmelCase       :	Optional[Any]											= image_processor(
          lowerCAmelCase_     ,			['semantic'] * len(lowerCAmelCase_       )     ,			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    UpperCAmelCase_       (		self :     Any       )						->	Dict:
      # Initialize image_processor
      UpperCAmelCase       :	int											= self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      UpperCAmelCase       :	int											= prepare_image_inputs(self.image_processing_tester     ,			equal_resolution=lowerCAmelCase_     ,			torchify=lowerCAmelCase_       )
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase_     ,			torch.Tensor       )

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

      UpperCAmelCase					, UpperCAmelCase       :	Tuple											= self.image_processing_tester.get_expected_values(lowerCAmelCase_       )

      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(lowerCAmelCase_     ,			batched=lowerCAmelCase_       )

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

      UpperCAmelCase       :	int											= image_processor(
          lowerCAmelCase_     ,			['semantic'] * len(lowerCAmelCase_       )     ,			lowerCAmelCase_     ,			return_tensors='pt'     ,			instance_id_to_semantic_id=lowerCAmelCase_     ,			pad_and_return_pixel_mask=lowerCAmelCase_     ,			)

      return inputs


     def    UpperCAmelCase_       (		self :     str       )						->	Tuple:
      pass


     def    UpperCAmelCase_       (		self :     Optional[int]       )						->	Any:
      def common(lowercase_ :     List[Any]=False     ,			lowercase_ :     Union[str, Any]=None       ):
       UpperCAmelCase       :	Optional[Any]											= self.comm_get_image_processor_inputs(
           with_segmentation_maps=lowerCAmelCase_     ,			is_instance_map=lowerCAmelCase_     ,			segmentation_type=lowerCAmelCase_       )

       UpperCAmelCase       :	Union[str, Any]											= inputs['mask_labels']
       UpperCAmelCase       :	List[Any]											= inputs['class_labels']
       UpperCAmelCase       :	Tuple											= inputs['pixel_values']
       UpperCAmelCase       :	List[Any]											= inputs['text_inputs']

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

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


     def    UpperCAmelCase_       (		self :     List[str]       )						->	Any:
      UpperCAmelCase       :	List[str]											= np.zeros((20, 50)       )
      UpperCAmelCase       :	Dict											= 1
      UpperCAmelCase       :	Optional[int]											= 1
      UpperCAmelCase       :	int											= 1

      UpperCAmelCase       :	Any											= binary_mask_to_rle(lowerCAmelCase_       )
      self.assertEqual(len(lowerCAmelCase_       )     ,			4       )
      self.assertEqual(rle[0]     ,			21       )
      self.assertEqual(rle[1]     ,			45       )


     def    UpperCAmelCase_       (		self :     Any       )						->	str:
      UpperCAmelCase       :	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'     ,			)
      UpperCAmelCase       :	Optional[int]											= self.image_processing_tester.get_fake_oneformer_outputs()

      UpperCAmelCase       :	Dict											= fature_extractor.post_process_semantic_segmentation(lowerCAmelCase_       )

      self.assertEqual(len(lowerCAmelCase_       )     ,			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       :	int											= fature_extractor.post_process_semantic_segmentation(lowerCAmelCase_     ,			target_sizes=lowerCAmelCase_       )

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


     def    UpperCAmelCase_       (		self :     Dict       )						->	Union[str, Any]:
      UpperCAmelCase       :	Optional[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'     ,			)
      UpperCAmelCase       :	int											= self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCAmelCase       :	Optional[int]											= image_processor.post_process_instance_segmentation(lowerCAmelCase_     ,			threshold=0       )

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




     def    UpperCAmelCase_       (		self :     Optional[Any]       )						->	Tuple:
      UpperCAmelCase       :	Optional[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'     ,			)
      UpperCAmelCase       :	List[str]											= self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCAmelCase       :	Any											= image_processor.post_process_panoptic_segmentation(lowerCAmelCase_     ,			threshold=0       )

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











import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_snake_case  :    Tuple						    =  importlib.util.find_spec('s3fs') is not None

if _has_safs:
     from .safilesystem import SaFileSystem  # noqa: F401

_snake_case  :    List[compression.BaseCompressedFileFileSystem]						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
     if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
          warnings.warn(F"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
     fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def   a_    ( lowerCAmelCase_      :			str	):
   if "://" in dataset_path:
      __lowerCAmelCase								=	dataset_path.split('://'	)[1]
   return dataset_path

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem	):
   if fs is not None and fs.protocol != "file":
      return True
   else:
      return False

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	not is_remote_filesystem(lowerCAmelCase_	)
   if is_local:
      # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
      shutil.move(fs._strip_protocol(lowerCAmelCase_	),	fs._strip_protocol(lowerCAmelCase_	)	)
   else:
      fs.mv(lowerCAmelCase_,	lowerCAmelCase_,	recursive=lowerCAmelCase_	)




def   a_    ( ):
   if hasattr(fsspec.asyn,	'reset_lock'	):
      # for future fsspec>2022.05.0
      fsspec.asyn.reset_lock()
   else:
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	threading.Lock()

