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




class   lowercase_    (    unittest.TestCase		):



	"""simple docstring"""





	def __init__(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE=7						,     __SCREAMING_SNAKE_CASE=3						,     __SCREAMING_SNAKE_CASE=30						,     __SCREAMING_SNAKE_CASE=400						,     __SCREAMING_SNAKE_CASE=None						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]						,     __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]						,     __SCREAMING_SNAKE_CASE=10						,     __SCREAMING_SNAKE_CASE=False						,     __SCREAMING_SNAKE_CASE=255						,     __SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"						,     __SCREAMING_SNAKE_CASE="ade20k_panoptic.json"						,     __SCREAMING_SNAKE_CASE=10						,     )		->Tuple:
								lowerCAmelCase      						=						parent
								lowerCAmelCase      						=						batch_size
								lowerCAmelCase      						=						num_channels
								lowerCAmelCase      						=						min_resolution
								lowerCAmelCase      						=						max_resolution
								lowerCAmelCase      						=						do_resize
								lowerCAmelCase      						=						{"shortest_edge": 32, "longest_edge": 1333} if size is None else size
								lowerCAmelCase      						=						do_normalize
								lowerCAmelCase      						=						image_mean
								lowerCAmelCase      						=						image_std
								lowerCAmelCase      						=						class_info_file
								lowerCAmelCase      						=						prepare_metadata(lowerCamelCase__						,     lowerCamelCase__							)
								lowerCAmelCase      						=						num_text
								lowerCAmelCase      						=						repo_path

								# for the post_process_functions
								lowerCAmelCase      						=						2
								lowerCAmelCase      						=						10
								lowerCAmelCase      						=						10
								lowerCAmelCase      						=						3
								lowerCAmelCase      						=						4
								lowerCAmelCase      						=						num_labels
								lowerCAmelCase      						=						do_reduce_labels
								lowerCAmelCase      						=						ignore_index


	def      SCREAMING_SNAKE_CASE_							(				self							)		->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      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE=False							)		->List[Any]:
								if not batched:
															lowerCAmelCase      						=						image_inputs[0]
															if isinstance(lowerCamelCase__						,     Image.Image							):
																						lowerCAmelCase      						=						image.size
															else:
																						lowerCAmelCase      						=						image.shape[1], image.shape[2]
															if w < h:
																						lowerCAmelCase      						=						int(self.size['''shortest_edge'''] * h / w							)
																						lowerCAmelCase      						=						self.size["shortest_edge"]
															elif w > h:
																						lowerCAmelCase      						=						self.size["shortest_edge"]
																						lowerCAmelCase      						=						int(self.size['''shortest_edge'''] * w / h							)
															else:
																						lowerCAmelCase      						=						self.size["shortest_edge"]
																						lowerCAmelCase      						=						self.size["shortest_edge"]

								else:
															lowerCAmelCase      						=						[]
															for image in image_inputs:
																						lowerCAmelCase      						=						self.get_expected_values([image]							)
																						expected_values.append((expected_height, expected_width)							)
															lowerCAmelCase      						=						max(lowerCamelCase__						,     key=lambda __SCREAMING_SNAKE_CASE							: item[0]							)[0]
															lowerCAmelCase      						=						max(lowerCamelCase__						,     key=lambda __SCREAMING_SNAKE_CASE							: item[1]							)[1]

								return expected_height, expected_width




	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								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"""





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


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Dict:
								lowerCAmelCase      						=						OneFormerImageProcessorTester(self							)


	@property
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								return self.image_processing_tester.prepare_image_processor_dict()


	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								lowerCAmelCase      						=						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      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								pass


	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								# Initialize image_processor
								lowerCAmelCase      						=						self.image_processing_class(**self.image_processor_dict							)
								# create random PIL images
								lowerCAmelCase      						=						prepare_image_inputs(self.image_processing_tester						,     equal_resolution=lowerCamelCase__							)
								for image in image_inputs:
															self.assertIsInstance(lowerCamelCase__						,     Image.Image							)

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

								lowerCAmelCase      						=						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
								lowerCAmelCase      						=						self.image_processing_tester.get_expected_values(lowerCamelCase__						,     batched=lowerCamelCase__							)

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

								lowerCAmelCase      						=						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
								lowerCAmelCase      						=						self.image_processing_tester.get_expected_values(lowerCamelCase__						,     batched=lowerCamelCase__							)

								lowerCAmelCase      						=						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      SCREAMING_SNAKE_CASE_							(				self							)		->Any:
								# Initialize image_processor
								lowerCAmelCase      						=						self.image_processing_class(**self.image_processor_dict							)
								# create random PyTorch tensors
								lowerCAmelCase      						=						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
								lowerCAmelCase      						=						image_processor(image_inputs[0]						,     ['''semantic''']						,     return_tensors='''pt'''							).pixel_values

								lowerCAmelCase      						=						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
								lowerCAmelCase      						=						self.image_processing_tester.get_expected_values(lowerCamelCase__						,     batched=lowerCamelCase__							)

								lowerCAmelCase      						=						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      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE=False						,     __SCREAMING_SNAKE_CASE=False						,     __SCREAMING_SNAKE_CASE="np"							)		->Union[str, Any]:
								lowerCAmelCase      						=						self.image_processing_class(**self.image_processor_dict							)
								# prepare image and target
								lowerCAmelCase      						=						self.image_processing_tester.num_labels
								lowerCAmelCase      						=						None
								lowerCAmelCase      						=						None
								lowerCAmelCase      						=						prepare_image_inputs(self.image_processing_tester						,     equal_resolution=lowerCamelCase__							)
								if with_segmentation_maps:
															lowerCAmelCase      						=						num_labels
															if is_instance_map:
																						lowerCAmelCase      						=						list(range(lowerCamelCase__							)							) * 2
																						lowerCAmelCase      						=						dict(enumerate(lowerCamelCase__							)							)
															lowerCAmelCase      						=						[
															    np.random.randint(0						,     high * 2						,     (img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
															]
															if segmentation_type == "pil":
																						lowerCAmelCase      						=						[Image.fromarray(lowerCamelCase__							) for annotation in annotations]

								lowerCAmelCase      						=						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      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								pass


	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								def common(__SCREAMING_SNAKE_CASE=False						,     __SCREAMING_SNAKE_CASE=None							):
															lowerCAmelCase      						=						self.comm_get_image_processor_inputs(
															    with_segmentation_maps=lowerCamelCase__						,     is_instance_map=lowerCamelCase__						,     segmentation_type=lowerCamelCase__							)

															lowerCAmelCase      						=						inputs["mask_labels"]
															lowerCAmelCase      						=						inputs["class_labels"]
															lowerCAmelCase      						=						inputs["pixel_values"]
															lowerCAmelCase      						=						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      SCREAMING_SNAKE_CASE_							(				self							)		->Any:
								lowerCAmelCase      						=						np.zeros((20, 50)							)
								lowerCAmelCase      						=						1
								lowerCAmelCase      						=						1
								lowerCAmelCase      						=						1

								lowerCAmelCase      						=						binary_mask_to_rle(lowerCamelCase__							)
								self.assertEqual(len(lowerCamelCase__							)						,     4							)
								self.assertEqual(rle[0]						,     21							)
								self.assertEqual(rle[1]						,     45							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								lowerCAmelCase      						=						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'''						,     )
								lowerCAmelCase      						=						self.image_processing_tester.get_fake_oneformer_outputs()

								lowerCAmelCase      						=						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,
								    )						,     )

								lowerCAmelCase      						=						[(1, 4) for i in range(self.image_processing_tester.batch_size							)]
								lowerCAmelCase      						=						fature_extractor.post_process_semantic_segmentation(lowerCamelCase__						,     target_sizes=lowerCamelCase__							)

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


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Any:
								lowerCAmelCase      						=						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'''						,     )
								lowerCAmelCase      						=						self.image_processing_tester.get_fake_oneformer_outputs()
								lowerCAmelCase      						=						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      SCREAMING_SNAKE_CASE_							(				self							)		->str:
								lowerCAmelCase      						=						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'''						,     )
								lowerCAmelCase      						=						self.image_processing_tester.get_fake_oneformer_outputs()
								lowerCAmelCase      						=						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)							)




from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :      Optional[int]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    'efficientnet'




    def __init__(    self       ,						lowerCamelCase__ = 3       ,						lowerCamelCase__ = 600       ,						lowerCamelCase__ = 2.0       ,						lowerCamelCase__ = 3.1       ,						lowerCamelCase__ = 8       ,						lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3]       ,						lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192]       ,						lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320]       ,						lowerCamelCase__ = []       ,						lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1]       ,						lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1]       ,						lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6]       ,						lowerCamelCase__ = 0.2_5       ,						lowerCamelCase__ = "swish"       ,						lowerCamelCase__ = 2_560       ,						lowerCamelCase__ = "mean"       ,						lowerCamelCase__ = 0.0_2       ,						lowerCamelCase__ = 0.0_0_1       ,						lowerCamelCase__ = 0.9_9       ,						lowerCamelCase__ = 0.5       ,						lowerCamelCase__ = 0.2       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )

       lowerCAmelCase_:							str       =	num_channels
       lowerCAmelCase_:							str       =	image_size
       lowerCAmelCase_:							int       =	width_coefficient
       lowerCAmelCase_:							Union[str, Any]       =	depth_coefficient
       lowerCAmelCase_:							int       =	depth_divisor
       lowerCAmelCase_:							List[str]       =	kernel_sizes
       lowerCAmelCase_:							Tuple       =	in_channels
       lowerCAmelCase_:							List[str]       =	out_channels
       lowerCAmelCase_:							List[str]       =	depthwise_padding
       lowerCAmelCase_:							Optional[int]       =	strides
       lowerCAmelCase_:							List[str]       =	num_block_repeats
       lowerCAmelCase_:							Any       =	expand_ratios
       lowerCAmelCase_:							List[Any]       =	squeeze_expansion_ratio
       lowerCAmelCase_:							Optional[int]       =	hidden_act
       lowerCAmelCase_:							Optional[int]       =	hidden_dim
       lowerCAmelCase_:							Dict       =	pooling_type
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	batch_norm_eps
       lowerCAmelCase_:							List[str]       =	batch_norm_momentum
       lowerCAmelCase_:							List[Any]       =	dropout_rate
       lowerCAmelCase_:							Union[str, Any]       =	drop_connect_rate
       lowerCAmelCase_:							Tuple       =	sum(lowerCamelCase__ ) * 4





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    version.parse('1.11'			)




    @property
    def      _a    (    self ):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ] )




    @property
    def      _a    (    self ):
       return 1E-5