
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							(	__magic_name__		,    __magic_name__="shi-labs/oneformer_demo"						):







				'''simple docstring'''


				with open(hf_hub_download(__magic_name__		,    __magic_name__		,    repo_type="dataset"						)		,    "r"						) as f:
								lowercase         =      json.load(__magic_name__						)
				lowercase         =      {}
				lowercase         =      []
				lowercase         =      []
				for key, info in class_info.items():
								lowercase         =      info["name"]
								class_names.append(info["name"]						)
								if info["isthing"]:
												thing_ids.append(int(__magic_name__						)						)
				lowercase         =      thing_ids
				lowercase         =      class_names
				return metadata





class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



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

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





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

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

										return expected_height, expected_width





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[Any]:
										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_      (      __a		,    unittest.TestCase		):







						'''simple docstring'''



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





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[int]:
										lowercase         =      OneFormerImageProcessorTester(self     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->List[str]:
										return self.image_processing_tester.prepare_image_processor_dict()





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Optional[Any]:
										lowercase         =      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							:Optional[Any]     )  ->Union[str, Any]:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Optional[Any]:
										# Initialize image_processor
										lowercase         =      self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										lowercase         =      prepare_image_inputs(self.image_processing_tester		,    equal_resolution=lowerCAmelCase__     )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase__		,    Image.Image     )

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

										lowercase     ,	lowercase         =      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
										lowercase     ,	lowercase         =      self.image_processing_tester.get_expected_values(lowerCAmelCase__		,    batched=lowerCAmelCase__     )

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

										lowercase     ,	lowercase         =      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
										lowercase     ,	lowercase         =      self.image_processing_tester.get_expected_values(lowerCAmelCase__		,    batched=lowerCAmelCase__     )

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

										lowercase     ,	lowercase         =      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
										lowercase     ,	lowercase         =      self.image_processing_tester.get_expected_values(lowerCAmelCase__		,    batched=lowerCAmelCase__     )

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

										lowercase         =      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							:Optional[Any]     )  ->List[str]:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[Any]:
										def common(lowerCAmelCase__							:str=False		,    lowerCAmelCase__							:Dict=None     ):
														lowercase         =      self.comm_get_image_processor_inputs(
														    with_segmentation_maps=lowerCAmelCase__		,    is_instance_map=lowerCAmelCase__		,    segmentation_type=lowerCAmelCase__     )

														lowercase         =      inputs["mask_labels"]
														lowercase         =      inputs["class_labels"]
														lowercase         =      inputs["pixel_values"]
														lowercase         =      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							:Dict     )  ->Union[str, Any]:
										lowercase         =      np.zeros((20, 50)     )
										lowercase         =      1
										lowercase         =      1
										lowercase         =      1

										lowercase         =      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							:Union[str, Any]     )  ->List[Any]:
										lowercase         =      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"		,    )
										lowercase         =      self.image_processing_tester.get_fake_oneformer_outputs()

										lowercase         =      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,
										    )		,    )

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

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





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Union[str, Any]:
										lowercase         =      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"		,    )
										lowercase         =      self.image_processing_tester.get_fake_oneformer_outputs()
										lowercase         =      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     )  ->Optional[Any]:
										lowercase         =      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"		,    )
										lowercase         =      self.image_processing_tester.get_fake_oneformer_outputs()
										lowercase         =      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 inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[int]:
										lowercase         =      self.config_class(**self.inputs_dict     )
										self.parent.assertTrue(hasattr(lowerCAmelCase__		,    "tf_padding"     )     )
										self.parent.assertTrue(hasattr(lowerCAmelCase__		,    "depth_multiplier"     )     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Dict		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Union[str, Any]=13		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:Optional[int]=32		,    lowerCAmelCase__							:Any=0.25		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Optional[int]=8		,    lowerCAmelCase__							:List[str]=6		,    lowerCAmelCase__							:List[Any]=32		,    lowerCAmelCase__							:Optional[Any]=True		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:Dict="relu6"		,    lowerCAmelCase__							:Tuple=1280		,    lowerCAmelCase__							:Dict=0.1		,    lowerCAmelCase__							:List[str]=0.02		,    lowerCAmelCase__							:Dict=True		,    lowerCAmelCase__							:List[Any]=True		,    lowerCAmelCase__							:Tuple=10		,    lowerCAmelCase__							:int=None		,    )  ->List[str]:
										lowercase         =      parent
										lowercase         =      batch_size
										lowercase         =      num_channels
										lowercase         =      image_size
										lowercase         =      depth_multiplier
										lowercase         =      depth_divisible_by
										lowercase         =      min_depth
										lowercase         =      expand_ratio
										lowercase         =      tf_padding
										lowercase         =      output_stride
										lowercase         =      first_layer_is_expansion
										lowercase         =      finegrained_output
										lowercase         =      hidden_act
										lowercase         =      last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier     )
										lowercase         =      classifier_dropout_prob
										lowercase         =      use_labels
										lowercase         =      is_training
										lowercase         =      num_labels
										lowercase         =      initializer_range
										lowercase         =      scope





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Dict:
										lowercase         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

										lowercase         =      None
										lowercase         =      None
										if self.use_labels:
														lowercase         =      ids_tensor([self.batch_size]		,    self.num_labels     )
														lowercase         =      ids_tensor([self.batch_size, self.image_size, self.image_size]		,    self.num_labels     )

										lowercase         =      self.get_config()

										return config, pixel_values, labels, pixel_labels





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->List[Any]:
										return MobileNetVaConfig(
										    num_channels=self.num_channels		,    image_size=self.image_size		,    depth_multiplier=self.depth_multiplier		,    depth_divisible_by=self.depth_divisible_by		,    min_depth=self.min_depth		,    expand_ratio=self.expand_ratio		,    output_stride=self.output_stride		,    first_layer_is_expansion=self.first_layer_is_expansion		,    finegrained_output=self.finegrained_output		,    hidden_act=self.hidden_act		,    tf_padding=self.tf_padding		,    classifier_dropout_prob=self.classifier_dropout_prob		,    initializer_range=self.initializer_range		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Tuple     )  ->Any:
										lowercase         =      MobileNetVaModel(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__     )
										self.parent.assertEqual(
										    result.last_hidden_state.shape		,    (
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )		,    )
										self.parent.assertEqual(
										    result.pooler_output.shape		,    (self.batch_size, self.last_hidden_size)		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]     )  ->Union[str, Any]:
										lowercase         =      self.num_labels
										lowercase         =      MobileNetVaForImageClassification(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__		,    labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[int]     )  ->Union[str, Any]:
										lowercase         =      self.num_labels
										lowercase         =      MobileNetVaForSemanticSegmentation(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__     )
										self.parent.assertEqual(
										    result.logits.shape		,    (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )		,    )
										lowercase         =      model(lowerCAmelCase__		,    labels=lowerCAmelCase__     )
										self.parent.assertEqual(
										    result.logits.shape		,    (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										lowercase         =      self.prepare_config_and_inputs()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      config_and_inputs
										lowercase         =      {"pixel_values": pixel_values}
										return config, inputs_dict





@require_torch
class   UpperCamelCase_      (      __a		,    __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[int]				   =					(
						    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase      :  List[Any]				   =					(
						    {
						        '''feature-extraction''': MobileNetVaModel,
						        '''image-classification''': MobileNetVaForImageClassification,
						        '''image-segmentation''': MobileNetVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase      :  Union[str, Any]				   =					False
						UpperCamelCase      :  int				   =					False
						UpperCamelCase      :  str				   =					False
						UpperCamelCase      :  Dict				   =					False





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase         =      MobileNetVaModelTester(self     )
										lowercase         =      MobileNetVaConfigTester(self		,    config_class=lowerCAmelCase__		,    has_text_modality=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Any:
										self.config_tester.run_common_tests()





						@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"     )
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[int]:
										pass





						@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"     )
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										pass





						@unittest.skip(reason="MobileNetV2 does not output attentions"     )
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->str:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Dict:
										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase         =      model_class(lowerCAmelCase__     )
														lowercase         =      inspect.signature(model.forward     )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowercase         =      [*signature.parameters.keys()]

														lowercase         =      ["pixel_values"]
														self.assertListEqual(arg_names[:1]		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Optional[int]:
										def check_hidden_states_output(lowerCAmelCase__							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:int     ):
														lowercase         =      model_class(lowerCAmelCase__     )
														model.to(lowerCAmelCase__     )
														model.eval()

														with torch.no_grad():
																		lowercase         =      model(**self._prepare_for_class(lowerCAmelCase__		,    lowerCAmelCase__     )     )

														lowercase         =      outputs.hidden_states

														lowercase         =      16
														self.assertEqual(len(lowerCAmelCase__     )		,    lowerCAmelCase__     )

										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase         =      True
														check_hidden_states_output(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														lowercase         =      True

														check_hidden_states_output(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Tuple:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->str:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__     )





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->str:
										for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowercase         =      MobileNetVaModel.from_pretrained(lowerCAmelCase__     )
														self.assertIsNotNone(lowerCAmelCase__     )




def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image





@require_torch
@require_vision
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						@cached_property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Any:
										return (
										    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"     ) if is_vision_available() else None
										)





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Union[str, Any]:
										lowercase         =      MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"     ).to(lowerCAmelCase__     )

										lowercase         =      self.default_image_processor
										lowercase         =      prepare_img()
										lowercase         =      image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     ).to(lowerCAmelCase__     )

										# forward pass
										with torch.no_grad():
														lowercase         =      model(**lowerCAmelCase__     )

										# verify the logits
										lowercase         =      torch.Size((1, 1001)     )
										self.assertEqual(outputs.logits.shape		,    lowerCAmelCase__     )

										lowercase         =      torch.tensor([0.24_45, -1.19_93, 0.19_05]     ).to(lowerCAmelCase__     )

										self.assertTrue(torch.allclose(outputs.logits[0, :3]		,    lowerCAmelCase__		,    atol=1E-4     )     )





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[Any]:
										lowercase         =      MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"     )
										lowercase         =      model.to(lowerCAmelCase__     )

										lowercase         =      MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"     )

										lowercase         =      prepare_img()
										lowercase         =      image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     ).to(lowerCAmelCase__     )

										# forward pass
										with torch.no_grad():
														lowercase         =      model(**lowerCAmelCase__     )
										lowercase         =      outputs.logits

										# verify the logits
										lowercase         =      torch.Size((1, 21, 65, 65)     )
										self.assertEqual(logits.shape		,    lowerCAmelCase__     )

										lowercase         =      torch.tensor(
										    [
										        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
										        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
										        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
										    ]		,    device=lowerCAmelCase__		,    )

										self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,    lowerCAmelCase__		,    atol=1E-4     )     )
