




"""simple docstring"""




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A_							:			Optional[Any]											= pytest.mark.integration


A_							:			Union[str, Any]											= {"comet"}
A_							:			str											= importlib.util.find_spec("fairseq") is not None

A_							:			Any											= {"code_eval"}
A_							:			Tuple											= os.name == "nt"

A_							:			List[Any]											= {"bertscore", "frugalscore", "perplexity"}
A_							:			Any											= importlib.util.find_spec("transformers") is not None

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("""\"test requires Fairseq\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("""\"test requires transformers\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("""\"test not supported on Windows\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [metric_dir.split(os.sep    )[-2] for metric_dir in glob.glob("""./metrics/*/"""    )]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()			)
@for_all_test_methods(
    A__			,A__			,A__			)
@local
class    lowerCamelCase						(parameterized.TestCase			):
			lowerCamelCase__   :		int							=							{}
			lowerCamelCase__   :		Dict							=							None

			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""       )
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						SCREAMING_SNAKE_CASE__								=      datasets.load.import_main_class(metric_module.__name__    ,				dataset=__UpperCAmelCase       )
						# check parameters
						SCREAMING_SNAKE_CASE__								=      inspect.signature(metric._compute       ).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(__UpperCAmelCase    ,				metric_module.__name__       ):
									with self.use_local_metrics():
												try:
															SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
												except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						# run doctest
						with self.use_local_metrics():
									SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCAmelCase       ):
												yield
						else:
									yield

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						def load_local_metric(__UpperCAmelCase				:						int    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[int]       ):
									return load_metric(os.path.join("""metrics"""    ,				__UpperCAmelCase       )    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase       )

						with patch("""datasets.load_metric"""       ) as mock_load_metric:
									SCREAMING_SNAKE_CASE__								=      load_local_metric
									yield





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[Any]:
						def wrapper(__UpperCAmelCase				:						List[str]       ):
									SCREAMING_SNAKE_CASE__								=      contextmanager(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      patcher
									return patcher

						return wrapper

@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("""sv"""		,				""""""		,				""""""    )  # handle pytest cli flags





			class    lowerCamelCase						(A__			):

						def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    List[str]:
									assert len(input_dict["""input_ids"""]       ) == 2
									return np.array([1.03, 1.04]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("""bleurt.score._create_predictor"""    ) as mock_create_predictor:
						SCREAMING_SNAKE_CASE__								=      MockedPredictor()
						yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import torch

			def bert_cos_score_idf(snake_case__		,				snake_case__		,				*snake_case__		,				**snake_case__    ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case__    )    )

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("""bert_score.scorer.get_model"""    ), patch(
			    """bert_score.scorer.bert_cos_score_idf"""    ) as mock_bert_cos_score_idf:
						SCREAMING_SNAKE_CASE__								=      bert_cos_score_idf
						yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			def load_from_checkpoint(snake_case__    ):





						class    lowerCamelCase						:

									def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Any    ,				*__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    Optional[int]:
												assert len(__UpperCAmelCase       ) == 2
												SCREAMING_SNAKE_CASE__								=      [0.19, 0.92]
												return scores, sum(__UpperCAmelCase       ) / len(__UpperCAmelCase       )
						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("""comet.download_model"""    ) as mock_download_model:
						SCREAMING_SNAKE_CASE__								=      None
						with patch("""comet.load_from_checkpoint"""    ) as mock_load_from_checkpoint:
									SCREAMING_SNAKE_CASE__								=      load_from_checkpoint
									yield




def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      load_metric(os.path.join("""metrics"""		,				"""seqeval"""    )    )
			SCREAMING_SNAKE_CASE__								=      """ERROR"""
			SCREAMING_SNAKE_CASE__								=      f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
			with pytest.raises(snake_case__		,				match=re.escape(snake_case__    )    ):
						metric.compute(predictions=[]		,				references=[]		,				scheme=snake_case__    )





"""simple docstring"""




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    lowerCamelCase						(A__			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.config_class(**self.inputs_dict       )
						self.parent.assertTrue(hasattr(__UpperCAmelCase    ,				"""tf_padding"""       )       )
						self.parent.assertTrue(hasattr(__UpperCAmelCase    ,				"""depth_multiplier"""       )       )





class    lowerCamelCase						:

			def __init__(					self				:						Any    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Any=1_3    ,				__UpperCAmelCase				:						Any=3    ,				__UpperCAmelCase				:						int=3_2    ,				__UpperCAmelCase				:						Tuple=0.25    ,				__UpperCAmelCase				:						Optional[int]=8    ,				__UpperCAmelCase				:						Any=8    ,				__UpperCAmelCase				:						str=6    ,				__UpperCAmelCase				:						Optional[int]=3_2    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						Any="relu6"    ,				__UpperCAmelCase				:						str=1_2_8_0    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Any=0.02    ,				__UpperCAmelCase				:						Any=True    ,				__UpperCAmelCase				:						Optional[int]=True    ,				__UpperCAmelCase				:						Dict=1_0    ,				__UpperCAmelCase				:						Optional[int]=None    ,				)   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      depth_multiplier
						SCREAMING_SNAKE_CASE__								=      depth_divisible_by
						SCREAMING_SNAKE_CASE__								=      min_depth
						SCREAMING_SNAKE_CASE__								=      expand_ratio
						SCREAMING_SNAKE_CASE__								=      tf_padding
						SCREAMING_SNAKE_CASE__								=      output_stride
						SCREAMING_SNAKE_CASE__								=      first_layer_is_expansion
						SCREAMING_SNAKE_CASE__								=      finegrained_output
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier       )
						SCREAMING_SNAKE_CASE__								=      classifier_dropout_prob
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      num_labels
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      scope

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels, pixel_labels

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.num_labels
						SCREAMING_SNAKE_CASE__								=      MobileNetVaForSemanticSegmentation(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.logits.shape    ,				(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )    ,				)
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						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				:						str       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		str							=							(
			    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__   :		str							=							(
			    {
			        'feature-extraction': MobileNetVaModel,
			        'image-classification': MobileNetVaForImageClassification,
			        'image-segmentation': MobileNetVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		List[Any]							=							False
			lowerCamelCase__   :		Union[str, Any]							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      MobileNetVaModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      MobileNetVaConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    int:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Union[str, Any]:
						pass

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[str]:
						def check_hidden_states_output(__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]       ):
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									model.to(__UpperCAmelCase       )
									model.eval()

									with torch.no_grad():
												SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase       )       )

									SCREAMING_SNAKE_CASE__								=      outputs.hidden_states

									SCREAMING_SNAKE_CASE__								=      1_6
									self.assertEqual(len(__UpperCAmelCase       )    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

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

									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

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





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      MobileNetVaModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


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





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

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

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

						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_1)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([0.2_445, -1.1_993, 0.1_905]       ).to(__UpperCAmelCase       )

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





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""       )
						SCREAMING_SNAKE_CASE__								=      model.to(__UpperCAmelCase       )

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

						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      outputs.logits

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 2_1, 6_5, 6_5)       )
						self.assertEqual(logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [
						        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
						        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
						        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
						    ]    ,				device=__UpperCAmelCase    ,				)

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


