'''simple docstring'''




import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=13       ,				_a=30       ,				_a=2       ,				_a=3       ,				_a=True       ,				_a=True       ,				_a=32       ,				_a=5       ,				_a=4       ,				_a=37       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=10       ,				_a=0.02       ,				_a=None       ,				)    ->     List[str]:
        lowercase_					:       List[Any]     				=					parent
        lowercase_					:       Union[str, Any]     				=					batch_size
        lowercase_					:       str     				=					image_size
        lowercase_					:       Union[str, Any]     				=					patch_size
        lowercase_					:       str     				=					num_channels
        lowercase_					:       str     				=					is_training
        lowercase_					:       int     				=					use_labels
        lowercase_					:       int     				=					hidden_size
        lowercase_					:       Tuple     				=					num_hidden_layers
        lowercase_					:       str     				=					num_attention_heads
        lowercase_					:       List[Any]     				=					intermediate_size
        lowercase_					:       int     				=					hidden_act
        lowercase_					:       List[Any]     				=					hidden_dropout_prob
        lowercase_					:       Union[str, Any]     				=					attention_probs_dropout_prob
        lowercase_					:       Any     				=					type_sequence_label_size
        lowercase_					:       Optional[int]     				=					initializer_range
        lowercase_					:       int     				=					scope

        # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase_					:       int     				=					(image_size // patch_size) ** 2
        lowercase_					:       Any     				=					num_patches + 1
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       int     				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        lowercase_					:       Optional[int]     				=					None
        if self.use_labels:
             lowercase_					:       Union[str, Any]     				=					ids_tensor([self.batch_size]       ,				self.type_sequence_label_size				)

        lowercase_					:       Optional[Any]     				=					self.get_config()

        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        return ViTMSNConfig(
            image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				initializer_range=self.initializer_range       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     str:
        lowercase_					:       str     				=					ViTMSNModel(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)
        self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     str:
        lowercase_					:       int     				=					self.type_sequence_label_size
        lowercase_					:       Tuple     				=					ViTMSNForImageClassification(_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Dict     				=					model(_a       ,				labels=_a				)
        print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'				)
        print('Labels: {labels}'				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)				)

        # test greyscale images
        lowercase_					:       Tuple     				=					1
        lowercase_					:       Optional[int]     				=					ViTMSNForImageClassification(_a				)
        model.to(_a				)
        model.eval()

        lowercase_					:       Union[str, Any]     				=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
        lowercase_					:       Union[str, Any]     				=					model(_a				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)				)





   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       int     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Tuple     				=					config_and_inputs
        lowercase_					:       Dict     				=					{'pixel_values': pixel_values}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Dict     =    (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   A							:       Tuple     =    (
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   A							:       Any     =    False
   A							:       Union[str, Any]     =    False
   A							:       Dict     =    False
   A							:       str     =    False
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Optional[Any]     				=					ViTMSNModelTester(self				)
        lowercase_					:       List[Any]     				=					ConfigTester(self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        self.config_tester.run_common_tests()
   @unittest.skip(reason='ViTMSN does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        pass
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_				,lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       List[str]     				=					model_class(_a				)
             self.assertIsInstance(model.get_input_embeddings()       ,				(nn.Module)				)
             lowercase_					:       Any     				=					model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_a       ,				nn.Linear				)				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_					:       Any     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Tuple     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_a				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       Optional[Any]     				=					ViTMSNModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)



def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					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				)    ->     Optional[int]:
        return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'				) if is_vision_available() else None





   @slow
   def 		_lowerCamelCase (self				)    ->     Dict:
        torch.manual_seed(2				)
        lowercase_					:       Optional[Any]     				=					ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'				).to(_a				)

        lowercase_					:       Tuple     				=					self.default_image_processor
        lowercase_					:       Any     				=					prepare_img()
        lowercase_					:       Optional[int]     				=					image_processor(images=_a       ,				return_tensors='pt'				).to(_a				)

        # forward pass
        with torch.no_grad():
             lowercase_					:       str     				=					model(**_a				)

        # verify the logits
        lowercase_					:       Optional[Any]     				=					torch.Size((1, 1_000)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)

        lowercase_					:       List[Any]     				=					torch.tensor([-0.08_03, -0.44_54, -0.23_75]				).to(_a				)

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





'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_A								=   {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_A								=   logging.get_logger(__name__)


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    '''maskformer'''
   A							:       List[str]     =    {'''hidden_size''': '''mask_feature_size'''}
   A							:       Dict     =    ['''resnet''', '''swin''']
   A							:       Optional[Any]     =    ['''detr''']
   def __init__(self       ,				_a = 256       ,				_a = 256       ,				_a = 0.1       ,				_a = False       ,				_a = None       ,				_a = None       ,				_a = 0.02       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 20.0       ,				_a = None       ,				**_a       ,				)    ->     int:
        if backbone_config is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             lowercase_					:       List[Any]     				=					SwinConfig(
                 image_size=384       ,				in_channels=3       ,				patch_size=4       ,				embed_dim=128       ,				depths=[2, 2, 18, 2]       ,				num_heads=[4, 8, 16, 32]       ,				window_size=12       ,				drop_path_rate=0.3       ,				out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,				)

        if isinstance(_a       ,				_a				):
             lowercase_					:       Dict     				=					backbone_config.pop('model_type'				)
             lowercase_					:       Tuple     				=					CONFIG_MAPPING[backbone_model_type]
             lowercase_					:       Tuple     				=					config_class.from_dict(_a				)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(
                 f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
                 f'''Supported model types: {",".join(self.backbones_supported				)}'''				)

        if decoder_config is None:
             # fall back to https://huggingface.co/facebook/detr-resnet-50
             lowercase_					:       str     				=					DetrConfig()
        else:
             # verify that the decoder is supported
             lowercase_					:       List[Any]     				=					(
                 decoder_config.pop('model_type'				) if isinstance(_a       ,				_a				) else decoder_config.model_type
             )
             if decoder_type not in self.decoders_supported:
                  raise ValueError(
                      f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                      f''' {",".join(self.decoders_supported				)}'''				)
             if isinstance(_a       ,				_a				):
                  lowercase_					:       List[Any]     				=					CONFIG_MAPPING[decoder_type]
                  lowercase_					:       Tuple     				=					config_class.from_dict(_a				)

        lowercase_					:       Optional[Any]     				=					backbone_config
        lowercase_					:       int     				=					decoder_config
        # main feature dimension for the model
        lowercase_					:       List[Any]     				=					fpn_feature_size
        lowercase_					:       Tuple     				=					mask_feature_size
        # initializer
        lowercase_					:       Optional[int]     				=					init_std
        lowercase_					:       List[str]     				=					init_xavier_std
        # Hungarian matcher && loss
        lowercase_					:       int     				=					cross_entropy_weight
        lowercase_					:       Optional[int]     				=					dice_weight
        lowercase_					:       Dict     				=					mask_weight
        lowercase_					:       Tuple     				=					use_auxiliary_loss
        lowercase_					:       int     				=					no_object_weight
        lowercase_					:       Union[str, Any]     				=					output_auxiliary_logits

        lowercase_					:       List[Any]     				=					self.decoder_config.encoder_attention_heads
        lowercase_					:       Tuple     				=					self.decoder_config.num_hidden_layers
        super().__init__(**_a				)
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				_a       ,				**_a				)    ->     Union[str, Any]:
        return cls(
            backbone_config=_a       ,				decoder_config=_a       ,				**_a       ,				)





   def 		_lowerCamelCase (self				)    ->     Dict[str, any]:
        lowercase_					:       Optional[int]     				=					copy.deepcopy(self.__dict__				)
        lowercase_					:       Union[str, Any]     				=					self.backbone_config.to_dict()
        lowercase_					:       Optional[Any]     				=					self.decoder_config.to_dict()
        lowercase_					:       Dict     				=					self.__class__.model_type
        return output




