

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





class  __UpperCAmelCase					(	unittest.TestCase						):



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

     # for the post_process_functions
     UpperCAmelCase	:							Dict						=			2
     UpperCAmelCase	:							List[Any]						=			1_0
     UpperCAmelCase	:							Optional[int]						=			1_0
     UpperCAmelCase	:							Optional[Any]						=			3
     UpperCAmelCase	:							Optional[Any]						=			4
     UpperCAmelCase	:							int						=			num_labels
     UpperCAmelCase	:							Optional[Any]						=			do_reduce_labels
     UpperCAmelCase	:							Union[str, Any]						=			ignore_index



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

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

     return expected_height, expected_width





   def 	__magic_name__   (							self							:					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					(	lowerCamelCase__      ,  unittest.TestCase						):
   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 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							List[Any]						=			OneFormerImageProcessorTester(self						)



   @property
   def 	__magic_name__   (							self							:					Any						):
     return self.image_processing_tester.prepare_image_processor_dict()



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							int						=			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 	__magic_name__   (							self							:					List[Any]						):
     pass



   def 	__magic_name__   (							self							:					Optional[Any]						):
     # Initialize image_processor
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     UpperCAmelCase	:							Dict						=			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	:							List[str]						=			image_processor(image_inputs[0],		['''semantic'''],		return_tensors='''pt'''						).pixel_values

     UpperCAmelCase			,						UpperCAmelCase	:							int						=			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	:							Tuple						=			self.image_processing_tester.get_expected_values(__A,		batched=__A						)

     UpperCAmelCase	:							List[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 	__magic_name__   (							self							:					List[str]						):
     # Initialize image_processor
     UpperCAmelCase	:							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=__A,		numpify=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		np.ndarray						)

     # Test not batched input
     UpperCAmelCase	:							List[str]						=			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[Any]						=			self.image_processing_tester.get_expected_values(__A,		batched=__A						)

     UpperCAmelCase	:							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 	__magic_name__   (							self							:					Any						):
     # Initialize image_processor
     UpperCAmelCase	:							Optional[int]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     UpperCAmelCase	:							Dict						=			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	:							Dict						=			image_processor(image_inputs[0],		['''semantic'''],		return_tensors='''pt'''						).pixel_values

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

     UpperCAmelCase	:							Tuple						=			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 	__magic_name__   (							self							:					Tuple						):
     pass



   def 	__magic_name__   (							self							:					Optional[Any]						):
     def common(__A							:					Any=False,		__A							:					int=None						):
       UpperCAmelCase	:							List[str]						=			self.comm_get_image_processor_inputs(
           with_segmentation_maps=__A,		is_instance_map=__A,		segmentation_type=__A						)

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

     UpperCAmelCase	:							List[Any]						=			binary_mask_to_rle(__A						)
     self.assertEqual(len(__A						),		4						)
     self.assertEqual(rle[0],		2_1						)
     self.assertEqual(rle[1],		4_5						)



   def 	__magic_name__   (							self							:					List[Any]						):
     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	:							str						=			self.image_processing_tester.get_fake_oneformer_outputs()

     UpperCAmelCase	:							Optional[int]						=			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	:							Union[str, Any]						=			[(1, 4) for i in range(self.image_processing_tester.batch_size						)]
     UpperCAmelCase	:							int						=			fature_extractor.post_process_semantic_segmentation(__A,		target_sizes=__A						)

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



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							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''',		)
     UpperCAmelCase	:							Optional[int]						=			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 	__magic_name__   (							self							:					Any						):
     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	:							Dict						=			self.image_processing_tester.get_fake_oneformer_outputs()
     UpperCAmelCase	:							Optional[Any]						=			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)						)








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

def       a__					(		UpperCAmelCase							:      int      )  ->						Dict:

  # A local function to see if a dot lands in the circle.
  def is_in_circle(UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float      ) -> bool:
    UpperCAmelCase	:							Dict						=			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
  UpperCAmelCase	:							List[str]						=			mean(
      int(is_in_circle(uniform(-1.0    ,			1.0      )    ,			uniform(-1.0    ,			1.0      )      )      )
      for _ in range(UpperCAmelCase      )      )
  # The ratio of the area for circle to square is pi/4.
  UpperCAmelCase	:							int						=			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__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      Callable[[float], float]    ,			UpperCAmelCase							:      float = 0.0    ,			UpperCAmelCase							:      float = 1.0    ,			)  ->						float:
  return mean(
      function_to_integrate(uniform(UpperCAmelCase    ,			UpperCAmelCase      )      ) for _ in range(UpperCAmelCase      )      ) * (max_value - min_value)

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      float = 0.0    ,			UpperCAmelCase							:      float = 1.0      )  ->						None:

  def identity_function(UpperCAmelCase							:      float      ) -> float:
    return x

  UpperCAmelCase	:							int						=			area_under_curve_estimator(
      UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			(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__					(		UpperCAmelCase							:      int      )  ->						None:

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

  UpperCAmelCase	:							Optional[int]						=			area_under_curve_estimator(
      UpperCAmelCase    ,			UpperCAmelCase    ,			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()





