'''simple docstring'''

import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Optional[Any]  =					FileLock(str(tmpdir / '''foo.lock'''     )     )
 lowercase__				:    int  =					FileLock(str(tmpdir / '''foo.lock'''     )     )
 lowercase__				:    Union[str, Any]  =					0.0_1
 with locka.acquire():
  with pytest.raises(UpperCAmelCase     ):
   lowercase__				:    Optional[int]  =					time.time()
   locka.acquire(UpperCAmelCase     )
   assert time.time() - _start > timeout





def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    str  =					'''a''' * 1000 + '''.lock'''
 lowercase__				:    Dict  =					FileLock(str(tmpdir / filename     )     )
 assert locka._lock_file.endswith('''.lock'''     )
 assert not locka._lock_file.endswith(UpperCAmelCase     )
 assert len(os.path.basename(locka._lock_file     )     ) <= 255
 lowercase__				:    int  =					FileLock(tmpdir / filename     )
 with locka.acquire():
  with pytest.raises(UpperCAmelCase     ):
   locka.acquire(0     )


'''simple docstring'''

import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
  from transformers.models.mobilevitva.modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      make_divisible,
  )


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Union[str, Any]  =					self.config_class(**self.inputs_dict )
        self.parent.assertTrue(hasattr(__lowerCAmelCase	,   '''width_multiplier''' ) )



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=13	,   __lowerCAmelCase=64	,   __lowerCAmelCase=2	,   __lowerCAmelCase=3	,   __lowerCAmelCase="swish"	,   __lowerCAmelCase=3	,   __lowerCAmelCase=32	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.0_2	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=10	,   __lowerCAmelCase=None	,   __lowerCAmelCase=0.2_5	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=0.0	,   )  ->  List[Any]:
        lowercase__				:    List[str]  =					parent
        lowercase__				:    List[Any]  =					batch_size
        lowercase__				:    List[str]  =					image_size
        lowercase__				:    Optional[int]  =					patch_size
        lowercase__				:    Tuple  =					num_channels
        lowercase__				:    List[str]  =					make_divisible(512 * width_multiplier	,   divisor=8 )
        lowercase__				:    Optional[int]  =					hidden_act
        lowercase__				:    List[Any]  =					conv_kernel_size
        lowercase__				:    Dict  =					output_stride
        lowercase__				:    List[Any]  =					classifier_dropout_prob
        lowercase__				:    str  =					use_labels
        lowercase__				:    List[Any]  =					is_training
        lowercase__				:    Tuple  =					num_labels
        lowercase__				:    Optional[int]  =					initializer_range
        lowercase__				:    Tuple  =					scope
        lowercase__				:    List[Any]  =					width_multiplier
        lowercase__				:    Optional[int]  =					ffn_dropout
        lowercase__				:    int  =					attn_dropout

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__				:    Any  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        lowercase__				:    Any  =					None
        lowercase__				:    Tuple  =					None
        if self.use_labels:
         lowercase__				:    str  =					ids_tensor([self.batch_size]	,   self.num_labels )
         lowercase__				:    int  =					ids_tensor([self.batch_size, self.image_size, self.image_size]	,   self.num_labels )

        lowercase__				:    Dict  =					self.get_config()

        return config, pixel_values, labels, pixel_labels

       def _lowerCAmelCase(						self )  ->  Tuple:
        return MobileViTVaConfig(
            image_size=self.image_size	,   patch_size=self.patch_size	,   num_channels=self.num_channels	,   hidden_act=self.hidden_act	,   conv_kernel_size=self.conv_kernel_size	,   output_stride=self.output_stride	,   classifier_dropout_prob=self.classifier_dropout_prob	,   initializer_range=self.initializer_range	,   width_multiplier=self.width_multiplier	,   ffn_dropout=self.ffn_dropout_prob	,   attn_dropout=self.attn_dropout_prob	,   )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Any:
        lowercase__				:    Optional[int]  =					MobileViTVaModel(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    str  =					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,
            )	,   )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					self.num_labels
        lowercase__				:    Dict  =					MobileViTVaForImageClassification(__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    Optional[Any]  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    str  =					self.num_labels
        lowercase__				:    List[Any]  =					MobileViTVaForSemanticSegmentation(__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    int  =					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__				:    Union[str, Any]  =					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 _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Union[str, Any]  =					self.prepare_config_and_inputs()
        lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    List[str]  =					config_and_inputs
        lowercase__				:    List[Any]  =					{'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class 			UpperCAmelCase  (						a__    ,						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    (
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       SCREAMING_SNAKE_CASE							=    (
           {
               "feature-extraction": MobileViTVaModel,
               "image-classification": MobileViTVaForImageClassification,
               "image-segmentation": MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Tuple  =					MobileViTVaModelTester(self )
        lowercase__				:    Any  =					MobileViTVaConfigTester(self	,   config_class=__lowerCAmelCase	,   has_text_modality=__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[Any]:
        self.config_tester.run_common_tests()

       @unittest.skip(reason='''MobileViTV2 does not use inputs_embeds''' )
       def _lowerCAmelCase(						self )  ->  str:
        pass

       @unittest.skip(reason='''MobileViTV2 does not support input and output embeddings''' )
       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        pass

       @unittest.skip(reason='''MobileViTV2 does not output attentions''' )
       def _lowerCAmelCase(						self )  ->  Optional[int]:
        pass

       @require_torch_multi_gpu
       @unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.''' )
       def _lowerCAmelCase(						self )  ->  Any:
        pass

       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
       def _lowerCAmelCase(						self )  ->  str:
        pass

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__ ,							lowercase__				:    List[str]  =					self.model_tester.prepare_config_and_inputs_for_common()

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

         lowercase__				:    Dict  =					['''pixel_values''']
         self.assertListEqual(arg_names[:1]	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        def check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase ):
         lowercase__				:    Optional[int]  =					model_class(__lowerCAmelCase )
         model.to(__lowerCAmelCase )
         model.eval()

         with torch.no_grad():
          lowercase__				:    List[str]  =					model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase ) )

         lowercase__				:    List[Any]  =					outputs.hidden_states

         lowercase__				:    Optional[int]  =					5
         self.assertEqual(len(__lowerCAmelCase )	,   __lowerCAmelCase )

         # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
         # with the width and height being successively divided by 2.
         lowercase__				:    str  =					2
         for i in range(len(__lowerCAmelCase ) ):
          self.assertListEqual(
              list(hidden_states[i].shape[-2:] )	,   [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,   )
          divisor *= 2

         self.assertEqual(self.model_tester.output_stride	,   divisor // 2 )

        lowercase__ ,							lowercase__				:    Optional[Any]  =					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__				:    List[str]  =					True
         check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowercase__				:    Tuple  =					True

         check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Tuple  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase )


       @slow
       def _lowerCAmelCase(						self )  ->  List[str]:
        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__				:    Dict  =					MobileViTVaModel.from_pretrained(__lowerCAmelCase )
         self.assertIsNotNone(__lowerCAmelCase )


def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image





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





       '''simple docstring'''



       @cached_property
       def _lowerCAmelCase(						self )  ->  int:
        return (
            MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' )
            if is_vision_available()
            else None
        )

       @slow
       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Dict  =					MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' ).to(
            __lowerCAmelCase )

        lowercase__				:    List[Any]  =					self.default_image_processor
        lowercase__				:    Optional[int]  =					prepare_img()
        lowercase__				:    int  =					image_processor(images=__lowerCAmelCase	,   return_tensors='''pt''' ).to(__lowerCAmelCase )

        # forward pass
        with torch.no_grad():
         lowercase__				:    List[str]  =					model(**__lowerCAmelCase )

        # verify the logits
        lowercase__				:    Optional[Any]  =					torch.Size((1, 1000) )
        self.assertEqual(outputs.logits.shape	,   __lowerCAmelCase )

        lowercase__				:    int  =					torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] ).to(__lowerCAmelCase )

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

       @slow
       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__				:    Dict  =					MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
        lowercase__				:    int  =					model.to(__lowerCAmelCase )

        lowercase__				:    Any  =					MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )

        lowercase__				:    str  =					prepare_img()
        lowercase__				:    Optional[int]  =					image_processor(images=__lowerCAmelCase	,   return_tensors='''pt''' ).to(__lowerCAmelCase )

        # forward pass
        with torch.no_grad():
         lowercase__				:    str  =					model(**__lowerCAmelCase )
        lowercase__				:    Tuple  =					outputs.logits

        # verify the logits
        lowercase__				:    List[Any]  =					torch.Size((1, 21, 32, 32) )
        self.assertEqual(logits.shape	,   __lowerCAmelCase )

        lowercase__				:    Union[str, Any]  =					torch.tensor(
            [
                [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
                [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
                [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
            ]	,   device=__lowerCAmelCase	,   )

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


       @slow
       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Optional[int]  =					MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
        lowercase__				:    List[str]  =					model.to(__lowerCAmelCase )

        lowercase__				:    Optional[int]  =					MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )

        lowercase__				:    int  =					prepare_img()
        lowercase__				:    List[str]  =					image_processor(images=__lowerCAmelCase	,   return_tensors='''pt''' ).to(__lowerCAmelCase )

        # forward pass
        with torch.no_grad():
         lowercase__				:    Optional[Any]  =					model(**__lowerCAmelCase )

        lowercase__				:    Optional[int]  =					outputs.logits.detach().cpu()

        lowercase__				:    Any  =					image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase	,   target_sizes=[(50, 60)] )
        lowercase__				:    Optional[int]  =					torch.Size((50, 60) )
        self.assertEqual(segmentation[0].shape	,   __lowerCAmelCase )

        lowercase__				:    Optional[Any]  =					image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					torch.Size((32, 32) )
        self.assertEqual(segmentation[0].shape	,   __lowerCAmelCase )

