





'''simple docstring'''







import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image


class     _a						:
       def __init__(			self		:			Tuple     ,       lowercase		:			List[str]     ,       lowercase		:			Optional[int]=2     ,       lowercase		:			Any=True     ,       lowercase		:			Optional[Any]=False     ,       lowercase		:			List[str]=10     ,       lowercase		:			Any=3     ,       lowercase		:			str=32 * 8     ,       lowercase		:			Optional[Any]=32 * 8     ,       lowercase		:			Tuple=4     ,       lowercase		:			List[Any]=64     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_auxiliary_loss
           UpperCAmelCase     =	num_queries
           UpperCAmelCase     =	num_channels
           UpperCAmelCase     =	min_size
           UpperCAmelCase     =	max_size
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	hidden_dim
           UpperCAmelCase     =	hidden_dim
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
               lowercase       )

           UpperCAmelCase     =	torch.ones([self.batch_size, self.min_size, self.max_size]     ,       device=lowercase       )

           UpperCAmelCase     =	(
               torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,       device=lowercase       ) > 0.5
           ).float()
           UpperCAmelCase     =	(torch.rand((self.batch_size, self.num_labels)     ,       device=lowercase       ) > 0.5).long()

           UpperCAmelCase     =	self.get_config()
           return config, pixel_values, pixel_mask, mask_labels, class_labels
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MaskaFormerConfig(
               hidden_size=self.hidden_dim     ,       )
           UpperCAmelCase     =	self.num_queries
           UpperCAmelCase     =	self.num_labels

           UpperCAmelCase     =	[1, 1, 1, 1]
           UpperCAmelCase     =	self.num_channels

           UpperCAmelCase     =	64
           UpperCAmelCase     =	128
           UpperCAmelCase     =	self.hidden_dim
           UpperCAmelCase     =	self.hidden_dim
           UpperCAmelCase     =	self.hidden_dim
           return config
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase     =	{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
           return config, inputs_dict
       def 					A   (			self		:			str     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	output.encoder_hidden_states
           UpperCAmelCase     =	output.pixel_decoder_hidden_states
           UpperCAmelCase     =	output.transformer_decoder_hidden_states

           self.parent.assertTrue(len(lowercase       )     ,       len(config.backbone_config.depths       )       )
           self.parent.assertTrue(len(lowercase       )     ,       len(config.backbone_config.depths       )       )
           self.parent.assertTrue(len(lowercase       )     ,       config.decoder_layers       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			Any     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           with torch.no_grad():
               UpperCAmelCase     =	MaskaFormerModel(config=lowercase       )
               model.to(lowercase       )
               model.eval()

               UpperCAmelCase     =	model(pixel_values=lowercase     ,       pixel_mask=lowercase       )
               UpperCAmelCase     =	model(lowercase     ,       output_hidden_states=lowercase       )

           self.parent.assertEqual(
               output.transformer_decoder_last_hidden_state.shape     ,       (self.batch_size, self.num_queries, self.hidden_dim)     ,       )
           # let's ensure the other two hidden state exists
           self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None       )
           self.parent.assertTrue(output.encoder_last_hidden_state is not None       )

           if output_hidden_states:
               self.check_output_hidden_state(lowercase     ,       lowercase       )







       def 					A   (			self		:			str     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			Dict     ,       lowercase		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MaskaFormerForUniversalSegmentation(config=lowercase       )
           model.to(lowercase       )
           model.eval()

           def comm_check_on_output(lowercase		:			Optional[Any]       ):
               # let's still check that all the required stuff is there
               self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None       )
               self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None       )
               self.parent.assertTrue(result.encoder_last_hidden_state is not None       )
               # okay, now we need to check the logits shape
               # due to the encoder compression, masks have a //4 spatial size
               self.parent.assertEqual(
                   result.masks_queries_logits.shape     ,       (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)     ,       )
               # + 1 for null class
               self.parent.assertEqual(
                   result.class_queries_logits.shape     ,       (self.batch_size, self.num_queries, self.num_labels + 1)       )

           with torch.no_grad():
               UpperCAmelCase     =	model(pixel_values=lowercase     ,       pixel_mask=lowercase       )
               UpperCAmelCase     =	model(lowercase       )

               comm_check_on_output(lowercase       )

               UpperCAmelCase     =	model(
                   pixel_values=lowercase     ,       pixel_mask=lowercase     ,       mask_labels=lowercase     ,       class_labels=lowercase       )

           comm_check_on_output(lowercase       )

           self.parent.assertTrue(result.loss is not None       )
           self.parent.assertEqual(result.loss.shape     ,       torch.Size([1]       )       )






@require_torch
class     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  List[str]            =      (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       __a       :  Tuple            =      {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

       __a       :  Tuple            =      False
       __a       :  Optional[Any]            =      False
       __a       :  Union[str, Any]            =      False
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MaskaFormerModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       has_text_modality=lowercase       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(lowercase     ,       **lowercase     ,       output_hidden_states=lowercase       )
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowercase       )
       @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''Mask2Former is not a generative model'''       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''Mask2Former does not use token embeddings'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass
       @require_torch_multi_gpu
       @unittest.skip(
           reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

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



           '''simple docstring'''

           pass
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase     =	['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,       lowercase       )
       @slow
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               UpperCAmelCase     =	MaskaFormerModel.from_pretrained(lowercase       )
               self.assertIsNotNone(lowercase       )
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	(self.model_tester.min_size,) * 2
           UpperCAmelCase     =	{
               '''pixel_values''': torch.randn((2, 3, *size)     ,       device=lowercase       ),
               '''mask_labels''': torch.randn((2, 10, *size)     ,       device=lowercase       ),
               '''class_labels''': torch.zeros(2     ,       10     ,       device=lowercase       ).long(),
           }
           UpperCAmelCase     =	self.model_tester.get_config()

           UpperCAmelCase     =	MaskaFormerForUniversalSegmentation(lowercase       ).to(lowercase       )
           UpperCAmelCase     =	model(**lowercase       )
           self.assertTrue(outputs.loss is not None       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(lowercase     ,       **lowercase     ,       output_hidden_states=lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     =	model_class(lowercase       ).to(lowercase       )
               UpperCAmelCase     =	model(**lowercase     ,       output_attentions=lowercase       )
               self.assertTrue(outputs.attentions is not None       )
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           if not self.model_tester.is_training:
               return

           UpperCAmelCase     =	self.all_model_classes[1]
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()

           UpperCAmelCase     =	model_class(lowercase       )
           model.to(lowercase       )
           model.train()

           UpperCAmelCase     =	model(lowercase     ,       mask_labels=lowercase     ,       class_labels=lowercase       ).loss
           loss.backward()







       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.all_model_classes[1]
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           UpperCAmelCase     =	True
           UpperCAmelCase     =	True

           UpperCAmelCase     =	model_class(lowercase       ).to(lowercase       )
           model.train()

           UpperCAmelCase     =	model(lowercase     ,       mask_labels=lowercase     ,       class_labels=lowercase       )

           UpperCAmelCase     =	outputs.encoder_hidden_states[0]
           encoder_hidden_states.retain_grad()

           UpperCAmelCase     =	outputs.pixel_decoder_hidden_states[0]
           pixel_decoder_hidden_states.retain_grad()

           UpperCAmelCase     =	outputs.transformer_decoder_hidden_states[0]
           transformer_decoder_hidden_states.retain_grad()

           UpperCAmelCase     =	outputs.attentions[0]
           attentions.retain_grad()

           outputs.loss.backward(retain_graph=lowercase       )

           self.assertIsNotNone(encoder_hidden_states.grad       )
           self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
           self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
           self.assertIsNotNone(attentions.grad       )






A									=1E-4





def 		snake_case_							():
    UpperCAmelCase     =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
    return image







@require_vision
@slow
class     _a						(			unittest.TestCase				):
       @cached_property
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           return "facebook/mask2former-swin-small-coco-instance"
       @cached_property
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(lowercase       )
           UpperCAmelCase     =	self.default_image_processor
           UpperCAmelCase     =	prepare_img()
           UpperCAmelCase     =	image_processor(lowercase     ,       return_tensors='''pt'''       ).to(lowercase       )
           UpperCAmelCase     =	inputs['''pixel_values'''].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
           # check size
           self.assertEqual(lowercase     ,       (1, 3, 384, 384)       )

           with torch.no_grad():
               UpperCAmelCase     =	model(**lowercase       )

           UpperCAmelCase     =	torch.tensor(
               [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]       ).to(lowercase       )
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,       lowercase     ,       atol=lowercase       )       )

           UpperCAmelCase     =	torch.tensor(
               [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]       ).to(lowercase       )
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,       lowercase     ,       atol=lowercase       )       )

           UpperCAmelCase     =	torch.tensor(
               [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]       ).to(lowercase       )
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,       lowercase     ,       atol=lowercase       )       )
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(lowercase       ).eval()
           UpperCAmelCase     =	self.default_image_processor
           UpperCAmelCase     =	prepare_img()
           UpperCAmelCase     =	image_processor(lowercase     ,       return_tensors='''pt'''       ).to(lowercase       )
           UpperCAmelCase     =	inputs['''pixel_values'''].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
           # check size
           self.assertEqual(lowercase     ,       (1, 3, 384, 384)       )

           with torch.no_grad():
               UpperCAmelCase     =	model(**lowercase       )
           # masks_queries_logits
           UpperCAmelCase     =	outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape     ,       (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
           UpperCAmelCase     =	[
               [-8.7839, -9.0056, -8.8121],
               [-7.4104, -7.0313, -6.5401],
               [-6.6105, -6.3427, -6.4675],
           ]
           UpperCAmelCase     =	torch.tensor(lowercase       ).to(lowercase       )
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,       lowercase     ,       atol=lowercase       )       )
           # class_queries_logits
           UpperCAmelCase     =	outputs.class_queries_logits
           self.assertEqual(class_queries_logits.shape     ,       (1, model.config.num_queries, model.config.num_labels + 1)       )
           UpperCAmelCase     =	torch.tensor(
               [
                   [1.8324, -8.0835, -4.1922],
                   [0.8450, -9.0050, -3.6053],
                   [0.3045, -7.7293, -3.0275],
               ]       ).to(lowercase       )
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,       lowercase     ,       atol=lowercase       )       )







       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(lowercase       ).eval()
           UpperCAmelCase     =	self.default_image_processor

           UpperCAmelCase     =	image_processor(
               [np.zeros((3, 800, 1_333)       ), np.zeros((3, 800, 1_333)       )]     ,       segmentation_maps=[np.zeros((384, 384)       ).astype(np.floataa       ), np.zeros((384, 384)       ).astype(np.floataa       )]     ,       return_tensors='''pt'''     ,       )

           UpperCAmelCase     =	inputs['''pixel_values'''].to(lowercase       )
           UpperCAmelCase     =	[el.to(lowercase       ) for el in inputs['''mask_labels''']]
           UpperCAmelCase     =	[el.to(lowercase       ) for el in inputs['''class_labels''']]

           with torch.no_grad():
               UpperCAmelCase     =	model(**lowercase       )

           self.assertTrue(outputs.loss is not None       )










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




