


'''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__							,      SCREAMING_SNAKE_CASE__="shi-labs/oneformer_demo"     ):







				"""simple docstring"""





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





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=None    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=[0.5, 0.5, 0.5]    ,					__snake_case=[0.5, 0.5, 0.5]    ,					__snake_case=10    ,					__snake_case=False    ,					__snake_case=255    ,					__snake_case="shi-labs/oneformer_demo"    ,					__snake_case="ade20k_panoptic.json"    ,					__snake_case=10    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      parent
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_channels
											_SCREAMING_SNAKE_CASE :       Any     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Any     		=      {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      do_normalize
											_SCREAMING_SNAKE_CASE :       Tuple     		=      image_mean
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      image_std
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      class_info_file
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      prepare_metadata(__snake_case    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      num_text
											_SCREAMING_SNAKE_CASE :       Dict     		=      repo_path

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






							def UpperCAmelCase_	(    self  ):
											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    ,					__snake_case    ,					__snake_case=False  ):

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

											else:
															_SCREAMING_SNAKE_CASE :       str     		=      []
															for image in image_inputs:
																			_SCREAMING_SNAKE_CASE :       Dict     		=      self.get_expected_values([image]  )
																			expected_values.append((expected_height, expected_width)  )
															_SCREAMING_SNAKE_CASE :       Any     		=      max(__snake_case    ,					key=lambda __snake_case  : item[0]  )[0]
															_SCREAMING_SNAKE_CASE :       Any     		=      max(__snake_case    ,					key=lambda __snake_case  : item[1]  )[1]

											return expected_height, expected_width






							def UpperCAmelCase_	(    self  ):
											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     lowercase__  (						__UpperCAmelCase     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Dict      	=  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
							# only for test_image_processing_common.test_image_proc_to_json_string
							A_							:      int      	=  image_processing_class






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      OneFormerImageProcessorTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processing_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      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 UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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
											_SCREAMING_SNAKE_CASE :       Tuple     		=      image_processor(image_inputs[0]    ,					["""semantic"""]    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      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
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processing_tester.get_expected_values(__snake_case    ,					batched=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[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 UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE :       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
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      image_processor(image_inputs[0]    ,					["""semantic"""]    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      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
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_tester.get_expected_values(__snake_case    ,					batched=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[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 UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       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
											_SCREAMING_SNAKE_CASE :       str     		=      image_processor(image_inputs[0]    ,					["""semantic"""]    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE :       Optional[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
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.image_processing_tester.get_expected_values(__snake_case    ,					batched=__snake_case  )

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

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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 UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):

											def common(__snake_case=False    ,					__snake_case=None  ):
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.comm_get_image_processor_inputs(
															    with_segmentation_maps=__snake_case    ,					is_instance_map=__snake_case    ,					segmentation_type=__snake_case  )

															_SCREAMING_SNAKE_CASE :       str     		=      inputs['mask_labels']
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      inputs['class_labels']
															_SCREAMING_SNAKE_CASE :       Tuple     		=      inputs['pixel_values']
															_SCREAMING_SNAKE_CASE :       Dict     		=      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 UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      np.zeros((20, 50)  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      1
											_SCREAMING_SNAKE_CASE :       int     		=      1
											_SCREAMING_SNAKE_CASE :       int     		=      1

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      binary_mask_to_rle(__snake_case  )
											self.assertEqual(len(__snake_case  )    ,					4  )
											self.assertEqual(rle[0]    ,					21  )
											self.assertEqual(rle[1]    ,					45  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[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"""    ,					)
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processing_tester.get_fake_oneformer_outputs()

											_SCREAMING_SNAKE_CASE :       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,
											    )    ,					)

											_SCREAMING_SNAKE_CASE :       Tuple     		=      [(1, 4) for i in range(self.image_processing_tester.batch_size  )]
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      fature_extractor.post_process_semantic_segmentation(__snake_case    ,					target_sizes=__snake_case  )

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






							def UpperCAmelCase_	(    self  ):
											_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 :       Any     		=      self.image_processing_tester.get_fake_oneformer_outputs()
											_SCREAMING_SNAKE_CASE :       Optional[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 UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[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"""    ,					)
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_tester.get_fake_oneformer_outputs()
											_SCREAMING_SNAKE_CASE :       str     		=      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)  )











from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def A_	(		A__				)  ->		Tuple:

       # A local function to see if a dot lands in the circle.
       def is_in_circle(A__		,      A__				) -> bool:
              a__						:	List[str] =				sqrt((x**2) + (y**2)				)
              # Our circle has a radius of 1, so a distance
              # greater than 1 would land outside the circle.
              return distance_from_centre <= 1

       # The proportion of guesses that landed in the circle
       a__						:	List[str] =				mean(
           int(is_in_circle(uniform(-1.0		,      1.0				)		,      uniform(-1.0		,      1.0				)				)				)
           for _ in range(A__				)				)
       # The ratio of the area for circle to square is pi/4.
       a__						:	Optional[Any] =				proportion * 4
       print(F'The estimated value of pi is {pi_estimate}'				)
       print(F'The numpy value of pi is {pi}'				)
       print(F'The total error is {abs(pi - pi_estimate				)}'				)



def A_	(		A__		,      A__		,      A__ = 0.0		,      A__ = 1.0		,      )  ->		float:
       return mean(
           function_to_integrate(uniform(A__		,      A__				)				) for _ in range(A__				)				) * (max_value - min_value)



def A_	(		A__		,      A__ = 0.0		,      A__ = 1.0				)  ->		None:

       def identity_function(A__				) -> float:
              return x

       a__						:	List[Any] =				area_under_curve_estimator(
           A__		,      A__		,      A__		,      A__				)
       a__						:	Union[str, Any] =				(max_value * max_value - min_value * min_value) / 2

       print('******************'				)
       print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {expected_value}'				)
       print(F'Total error is {abs(estimated_value - expected_value				)}'				)
       print('******************'				)



def A_	(		A__				)  ->		None:

       def function_to_integrate(A__				) -> float:
              return sqrt(4.0 - x * x				)

       a__						:	Dict =				area_under_curve_estimator(
           A__		,      A__		,      0.0		,      2.0				)

       print('******************'				)
       print('Estimating pi using area_under_curve_estimator'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {pi}'				)
       print(F'Total error is {abs(estimated_value - pi				)}'				)
       print('******************'				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


