




import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class  UpperCAmelCase(	nn.Module	):



	"""simple docstring"""






	def __init__(	self							)   ->     List[Any]:





								"""simple docstring"""


								super().__init__()
								lowercase__    :			Tuple     =				nn.Linear(3			, 4							)
								lowercase__    :			List[str]     =				nn.BatchNormad(4							)
								lowercase__    :			Optional[int]     =				nn.Linear(4			, 5							)







	def        __a      (	self			, lowerCamelCase							)   ->     Union[str, Any]:





								"""simple docstring"""


								return self.lineara(self.batchnorm(self.lineara(lowerCamelCase							)							)							)







class  UpperCAmelCase(	unittest.TestCase	):



	"""simple docstring"""






	def        __a      (	self							)   ->     Tuple:





								"""simple docstring"""


								lowercase__    :			Union[str, Any]     =				ModelForTest()
								with TemporaryDirectory() as tmp_dir:
															offload_state_dict(lowerCamelCase			, model.state_dict()							)
															lowercase__    :			List[Any]     =				os.path.join(lowerCamelCase			, "index.json"							)
															self.assertTrue(os.path.isfile(lowerCamelCase							)							)
															# TODO: add tests on what is inside the index

															for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
																						lowercase__    :			List[str]     =				os.path.join(lowerCamelCase			, f"""{key}.dat"""							)
																						self.assertTrue(os.path.isfile(lowerCamelCase							)							)
																						# TODO: add tests on the fact weights are properly loaded

	def        __a      (	self							)   ->     int:





								"""simple docstring"""


								lowercase__    :			int     =				[torch.floataa, torch.floataa, torch.bfloataa]

								for dtype in dtypes:
															lowercase__    :			Optional[int]     =				torch.randn(2			, 3			, dtype=lowerCamelCase							)
															with TemporaryDirectory() as tmp_dir:
																						lowercase__    :			Any     =				offload_weight(lowerCamelCase			, "weight"			, lowerCamelCase			, {}							)
																						lowercase__    :			str     =				os.path.join(lowerCamelCase			, "weight.dat"							)
																						self.assertTrue(os.path.isfile(lowerCamelCase							)							)
																						self.assertDictEqual(lowerCamelCase			, {"weight": {"shape": [2, 3], "dtype": str(lowerCamelCase							).split("."							)[1]}}							)

																						lowercase__    :			Optional[Any]     =				load_offloaded_weight(lowerCamelCase			, index["weight"]							)
																						self.assertTrue(torch.equal(lowerCamelCase			, lowerCamelCase							)							)

	def        __a      (	self							)   ->     Any:





								"""simple docstring"""


								lowercase__    :			List[Any]     =				ModelForTest()
								lowercase__    :			Union[str, Any]     =				model.state_dict()
								lowercase__    :			int     =				{k: v for k, v in state_dict.items() if "linear2" not in k}
								lowercase__    :			Optional[Any]     =				{k: v for k, v in state_dict.items() if "linear2" in k}

								with TemporaryDirectory() as tmp_dir:
															offload_state_dict(lowerCamelCase			, lowerCamelCase							)
															lowercase__    :			List[Any]     =				OffloadedWeightsLoader(state_dict=lowerCamelCase			, save_folder=lowerCamelCase							)

															# Every key is there with the right value
															self.assertEqual(sorted(lowerCamelCase							)			, sorted(state_dict.keys()							)							)
															for key, param in state_dict.items():
																						self.assertTrue(torch.allclose(lowerCamelCase			, weight_map[key]							)							)

								lowercase__    :			List[str]     =				{k: v for k, v in state_dict.items() if "weight" in k}
								lowercase__    :			str     =				{k: v for k, v in state_dict.items() if "weight" not in k}

								with TemporaryDirectory() as tmp_dir:
															offload_state_dict(lowerCamelCase			, lowerCamelCase							)
															lowercase__    :			Union[str, Any]     =				OffloadedWeightsLoader(state_dict=lowerCamelCase			, save_folder=lowerCamelCase							)

															# Every key is there with the right value
															self.assertEqual(sorted(lowerCamelCase							)			, sorted(state_dict.keys()							)							)
															for key, param in state_dict.items():
																						self.assertTrue(torch.allclose(lowerCamelCase			, weight_map[key]							)							)

								with TemporaryDirectory() as tmp_dir:
															offload_state_dict(lowerCamelCase			, lowerCamelCase							)
															# Duplicates are removed
															lowercase__    :			Dict     =				OffloadedWeightsLoader(state_dict=lowerCamelCase			, save_folder=lowerCamelCase							)

															# Every key is there with the right value
															self.assertEqual(sorted(lowerCamelCase							)			, sorted(state_dict.keys()							)							)
															for key, param in state_dict.items():
																						self.assertTrue(torch.allclose(lowerCamelCase			, weight_map[key]							)							)







	def        __a      (	self							)   ->     List[str]:





								"""simple docstring"""


								lowercase__    :			Any     =				{"a.1": 0, "a.10": 1, "a.2": 2}
								lowercase__    :			Optional[Any]     =				extract_submodules_state_dict(lowerCamelCase			, ["a.1", "a.2"]							)
								self.assertDictEqual(lowerCamelCase			, {"a.1": 0, "a.2": 2}							)

								lowercase__    :			Optional[Any]     =				{"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
								lowercase__    :			Any     =				extract_submodules_state_dict(lowerCamelCase			, ["a.1", "a.2"]							)
								self.assertDictEqual(lowerCamelCase			, {"a.1.a": 0, "a.2.a": 2}							)







'''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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     self.config_class(**self.inputs_dict   )
         self.parent.assertTrue(hasattr(lowerCamelCase			, 'width_multiplier'   )   )







class 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=13			, lowerCamelCase=64			, lowerCamelCase=2			, lowerCamelCase=3			, lowerCamelCase="swish"			, lowerCamelCase=3			, lowerCamelCase=32			, lowerCamelCase=0.1			, lowerCamelCase=0.02			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=10			, lowerCamelCase=None			, lowerCamelCase=0.25			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, )				->List[str]:



         '''simple docstring'''
         __a					  =     parent
         __a					  =     batch_size
         __a					  =     image_size
         __a					  =     patch_size
         __a					  =     num_channels
         __a					  =     make_divisible(512 * width_multiplier			, divisor=8   )
         __a					  =     hidden_act
         __a					  =     conv_kernel_size
         __a					  =     output_stride
         __a					  =     classifier_dropout_prob
         __a					  =     use_labels
         __a					  =     is_training
         __a					  =     num_labels
         __a					  =     initializer_range
         __a					  =     scope
         __a					  =     width_multiplier
         __a					  =     ffn_dropout
         __a					  =     attn_dropout




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

         __a					  =     self.get_config()

         return config, pixel_values, labels, pixel_labels




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->str:



         '''simple docstring'''
         __a					  =     MobileViTVaModel(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Any:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     MobileViTVaForImageClassification(lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase			, labels=lowerCamelCase   )
         self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     MobileViTVaForSemanticSegmentation(lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     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,
             )			, )
         __a					  =     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 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.prepare_config_and_inputs()
         __a  ,      __a  ,      __a  ,      __a					  =     config_and_inputs
         __a					  =     {'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   _lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =(
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

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

      __a	       =False
      __a	       =False
      __a	       =False
      __a	       =False




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     MobileViTVaModelTester(self   )
         __a					  =     MobileViTVaConfigTester(self			, config_class=lowerCamelCase			, has_text_modality=lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         self.config_tester.run_common_tests()




      @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'   )
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         pass




      @require_torch_multi_gpu
      @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'   )
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         pass




      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         pass




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

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

            __a					  =     ['pixel_values']
            self.assertListEqual(arg_names[:1]			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         def check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   ):
            __a					  =     model_class(lowerCamelCase   )
            model.to(lowerCamelCase   )
            model.eval()

            with torch.no_grad():
               __a					  =     model(**self._prepare_for_class(lowerCamelCase			, lowerCamelCase   )   )

            __a					  =     outputs.hidden_states

            __a					  =     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.
            __a					  =     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   )

         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __a					  =     True
            check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

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

            check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase   )






      @slow
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a					  =     MobileViTVaModel.from_pretrained(lowerCamelCase   )
            self.assertIsNotNone(lowerCamelCase   )







def  __UpperCAmelCase						(					)	->      List[Any]:

   """simple docstring"""
   __a					  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
   return image



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




      @cached_property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return (
             MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   )
             if is_vision_available()
             else None
         )




      @slow
      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   ).to(
             lowerCamelCase   )

         __a					  =     self.default_image_processor
         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )

         # verify the logits
         __a					  =     torch.Size((1, 1000)   )
         self.assertEqual(outputs.logits.shape			, lowerCamelCase   )

         __a					  =     torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]   ).to(lowerCamelCase   )

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




      @slow
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
         __a					  =     model.to(lowerCamelCase   )

         __a					  =     MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )
         __a					  =     outputs.logits

         # verify the logits
         __a					  =     torch.Size((1, 21, 32, 32)   )
         self.assertEqual(logits.shape			, lowerCamelCase   )

         __a					  =     torch.tensor(
             [
                 [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                 [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                 [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
             ]			, device=lowerCamelCase			, )

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






      @slow
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
         __a					  =     model.to(lowerCamelCase   )

         __a					  =     MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )

         __a					  =     outputs.logits.detach().cpu()

         __a					  =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase			, target_sizes=[(50, 60)]   )
         __a					  =     torch.Size((50, 60)   )
         self.assertEqual(segmentation[0].shape			, lowerCamelCase   )

         __a					  =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase   )
         __a					  =     torch.Size((32, 32)   )
         self.assertEqual(segmentation[0].shape			, lowerCamelCase   )