



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       _SCREAMING_SNAKE_CASE	:





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							List[Any]=2     ,  UpperCamelCase :							List[Any]=True     ,  UpperCamelCase :							str=False     ,  UpperCamelCase :							Optional[int]=1_0     ,  UpperCamelCase :							Union[str, Any]=3     ,  UpperCamelCase :							List[Any]=3_2 * 8     ,  UpperCamelCase :							List[str]=3_2 * 8     ,  UpperCamelCase :							Dict=4     ,  UpperCamelCase :							Any=6_4     ,  )->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	parent
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	batch_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	is_training
      __SCREAMING_SNAKE_CASE       :    Tuple       =	use_auxiliary_loss
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_queries
      __SCREAMING_SNAKE_CASE       :    Any       =	num_channels
      __SCREAMING_SNAKE_CASE       :    Any       =	min_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	max_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_labels
      __SCREAMING_SNAKE_CASE       :    str       =	hidden_dim
      __SCREAMING_SNAKE_CASE       :    int       =	hidden_dim





     def 	__snake_case    (       self :							int				)->str:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
          UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	torch.ones([self.batch_size, self.min_size, self.max_size]     ,  device=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	(
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,  device=UpperCamelCase				) > 0.5
      ).float()
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	(torch.rand((self.batch_size, self.num_labels)     ,  device=UpperCamelCase				) > 0.5).long()

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels





     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    str       =	MaskaFormerConfig(
          hidden_size=self.hidden_dim     ,  )
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.num_queries
      __SCREAMING_SNAKE_CASE       :    int       =	self.num_labels

      __SCREAMING_SNAKE_CASE       :    Tuple       =	[1, 1, 1, 1]
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.num_channels

      __SCREAMING_SNAKE_CASE       :    Tuple       =	6_4
      __SCREAMING_SNAKE_CASE       :    List[str]       =	1_2_8
      __SCREAMING_SNAKE_CASE       :    str       =	self.hidden_dim
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.hidden_dim
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.hidden_dim
      return config





     def 	__snake_case    (       self :							List[str]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE       :    str       =	{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
      return config, inputs_dict





     def 	__snake_case    (       self :							Any     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							str				)->Any:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	output.encoder_hidden_states
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	output.pixel_decoder_hidden_states
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	output.transformer_decoder_hidden_states

      self.parent.assertTrue(len(UpperCamelCase				)     ,  len(config.backbone_config.depths				)				)
      self.parent.assertTrue(len(UpperCamelCase				)     ,  len(config.backbone_config.depths				)				)
      self.parent.assertTrue(len(UpperCamelCase				)     ,  config.decoder_layers				)





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Tuple=False				)->Dict:
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    Any       =	MaskaFormerModel(config=UpperCamelCase				)
       model.to(UpperCamelCase				)
       model.eval()

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model(pixel_values=UpperCamelCase     ,  pixel_mask=UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    List[str]       =	model(UpperCamelCase     ,  output_hidden_states=UpperCamelCase				)

      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(UpperCamelCase     ,  UpperCamelCase				)






     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[Any]				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    str       =	MaskaFormerForUniversalSegmentation(config=UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.eval()

      def comm_check_on_output(UpperCamelCase :							str				):
       # 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():
       __SCREAMING_SNAKE_CASE       :    str       =	model(pixel_values=UpperCamelCase     ,  pixel_mask=UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Any       =	model(UpperCamelCase				)

       comm_check_on_output(UpperCamelCase				)

       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	model(
           pixel_values=UpperCamelCase     ,  pixel_mask=UpperCamelCase     ,  mask_labels=UpperCamelCase     ,  class_labels=UpperCamelCase				)

      comm_check_on_output(UpperCamelCase				)

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






@require_torch
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
     lowerCAmelCase					=			{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False





     def 	__snake_case    (       self :							List[Any]				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	MaskaFormerModelTester(self				)
      __SCREAMING_SNAKE_CASE       :    Any       =	ConfigTester(self     ,  config_class=UpperCamelCase     ,  has_text_modality=UpperCamelCase				)





     def 	__snake_case    (       self :							Tuple				)->Optional[int]:
      self.config_tester.run_common_tests()





     def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(UpperCamelCase     ,  **UpperCamelCase     ,  output_hidden_states=UpperCamelCase				)





     def 	__snake_case    (       self :							Union[str, Any]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCamelCase				)





     @unittest.skip(reason="Mask2Former does not use inputs_embeds"				)
     def 	__snake_case    (       self :							Union[str, Any]				)->Optional[int]:
      pass





     @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"				)
     def 	__snake_case    (       self :							Tuple				)->List[str]:
      pass





     @unittest.skip(reason="Mask2Former is not a generative model"				)
     def 	__snake_case    (       self :							List[Any]				)->int:
      pass





     @unittest.skip(reason="Mask2Former does not use token embeddings"				)
     def 	__snake_case    (       self :							Any				)->Tuple:
      pass





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





     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      pass





     def 	__snake_case    (       self :							Dict				)->Optional[int]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

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

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	["pixel_values"]
       self.assertListEqual(arg_names[:1]     ,  UpperCamelCase				)





     @slow
     def 	__snake_case    (       self :							List[Any]				)->List[Any]:
      for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	MaskaFormerModel.from_pretrained(UpperCamelCase				)
       self.assertIsNotNone(UpperCamelCase				)





     def 	__snake_case    (       self :							int				)->Dict:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	(self.model_tester.min_size,) * 2
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{
          "pixel_values": torch.randn((2, 3, *size)     ,  device=UpperCamelCase				),
          "mask_labels": torch.randn((2, 1_0, *size)     ,  device=UpperCamelCase				),
          "class_labels": torch.zeros(2     ,  1_0     ,  device=UpperCamelCase				).long(),
      }
      __SCREAMING_SNAKE_CASE       :    str       =	self.model_tester.get_config()

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	MaskaFormerForUniversalSegmentation(UpperCamelCase				).to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	model(**UpperCamelCase				)
      self.assertTrue(outputs.loss is not None				)





     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(UpperCamelCase     ,  **UpperCamelCase     ,  output_hidden_states=UpperCamelCase				)





     def 	__snake_case    (       self :							Tuple				)->int:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	model_class(UpperCamelCase				).to(UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Dict       =	model(**UpperCamelCase     ,  output_attentions=UpperCamelCase				)
       self.assertTrue(outputs.attentions is not None				)





     def 	__snake_case    (       self :							int				)->str:
      if not self.model_tester.is_training:
       return

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.all_model_classes[1]
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.model_tester.prepare_config_and_inputs()

      __SCREAMING_SNAKE_CASE       :    int       =	model_class(UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.train()

      __SCREAMING_SNAKE_CASE       :    Any       =	model(UpperCamelCase     ,  mask_labels=UpperCamelCase     ,  class_labels=UpperCamelCase				).loss
      loss.backward()






     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    str       =	self.all_model_classes[1]
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	self.model_tester.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	True
      __SCREAMING_SNAKE_CASE       :    Tuple       =	True

      __SCREAMING_SNAKE_CASE       :    List[str]       =	model_class(UpperCamelCase				).to(UpperCamelCase				)
      model.train()

      __SCREAMING_SNAKE_CASE       :    int       =	model(UpperCamelCase     ,  mask_labels=UpperCamelCase     ,  class_labels=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	outputs.pixel_decoder_hidden_states[0]
      pixel_decoder_hidden_states.retain_grad()

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=UpperCamelCase				)

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






_lowerCamelCase						    =  1e-4






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
 return image






@require_vision
@slow
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





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





     @cached_property
     def 	__snake_case    (       self :							Any				)->Optional[Any]:
      return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None





     def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.default_image_processor
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	prepare_img()
      __SCREAMING_SNAKE_CASE       :    Dict       =	image_processor(UpperCamelCase     ,  return_tensors="pt"				).to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0				)
      # check size
      self.assertEqual(UpperCamelCase     ,  (1, 3, 3_8_4, 3_8_4)				)

      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    int       =	model(**UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.tensor(
          [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]				).to(UpperCamelCase				)
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,  UpperCamelCase     ,  atol=UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE       :    Any       =	torch.tensor(
          [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]				).to(UpperCamelCase				)
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,  UpperCamelCase     ,  atol=UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.tensor(
          [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]				).to(UpperCamelCase				)
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,  UpperCamelCase     ,  atol=UpperCamelCase				)				)





     def 	__snake_case    (       self :							Dict				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Any       =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(UpperCamelCase				).eval()
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.default_image_processor
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	prepare_img()
      __SCREAMING_SNAKE_CASE       :    str       =	image_processor(UpperCamelCase     ,  return_tensors="pt"				).to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0				)
      # check size
      self.assertEqual(UpperCamelCase     ,  (1, 3, 3_8_4, 3_8_4)				)

      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    List[str]       =	model(**UpperCamelCase				)
      # masks_queries_logits
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape     ,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	[
          [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
          [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
          [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
      ]
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.tensor(UpperCamelCase				).to(UpperCamelCase				)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,  UpperCamelCase     ,  atol=UpperCamelCase				)				)
      # class_queries_logits
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	outputs.class_queries_logits
      self.assertEqual(class_queries_logits.shape     ,  (1, model.config.num_queries, model.config.num_labels + 1)				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	torch.tensor(
          [
              [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
              [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
              [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
          ]				).to(UpperCamelCase				)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,  UpperCamelCase     ,  atol=UpperCamelCase				)				)






     def 	__snake_case    (       self :							Any				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(UpperCamelCase				).eval()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.default_image_processor

      __SCREAMING_SNAKE_CASE       :    str       =	image_processor(
          [np.zeros((3, 8_0_0, 1_3_3_3)				), np.zeros((3, 8_0_0, 1_3_3_3)				)]     ,  segmentation_maps=[np.zeros((3_8_4, 3_8_4)				).astype(np.floataa				), np.zeros((3_8_4, 3_8_4)				).astype(np.floataa				)]     ,  return_tensors="pt"     ,  )

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	inputs["pixel_values"].to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Any       =	[el.to(UpperCamelCase				) for el in inputs["mask_labels"]]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[el.to(UpperCamelCase				) for el in inputs["class_labels"]]

      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    Any       =	model(**UpperCamelCase				)

      self.assertTrue(outputs.loss is not None				)











from collections.abc import Generator
from math import sin






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 if len(__lowerCamelCase     ) != 32:
  raise ValueError("Input must be of length 32"     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	b""
 for i in [3, 2, 1, 0]:
  little_endian += string_aa[8 * i : 8 * i + 8]
 return little_endian






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )

 __SCREAMING_SNAKE_CASE       :    Any       =	format(__lowerCamelCase		,	"08x"     )[-8:]
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	b""
 for i in [3, 2, 1, 0]:
  little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"     )
 return little_endian_hex






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	b""
 for char in message:
  bit_string += format(__lowerCamelCase		,	"08b"     ).encode("utf-8"     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	format(len(__lowerCamelCase     )		,	"064b"     ).encode("utf-8"     )

 # Pad bit_string to a multiple of 512 chars
 bit_string += b"1"
 while len(__lowerCamelCase     ) % 512 != 448:
  bit_string += b"0"
 bit_string += to_little_endian(start_len[32:]     ) + to_little_endian(start_len[:32]     )

 return bit_string






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 if len(__lowerCamelCase     ) % 512 != 0:
  raise ValueError("Input must have length that's a multiple of 512"     )

 for pos in range(0		,	len(__lowerCamelCase     )		,	512     ):
  __SCREAMING_SNAKE_CASE       :    int       =	bit_string[pos : pos + 512]
  __SCREAMING_SNAKE_CASE       :    Tuple       =	[]
  for i in range(0		,	512		,	32     ):
   block_words.append(int(to_little_endian(block[i : i + 32]     )		,	2     )     )
  yield block_words






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	format(__lowerCamelCase		,	"032b"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	""
 for c in i_str:
  new_str += "1" if c == "0" else "0"
 return int(__lowerCamelCase		,	2     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 return (a + b) % 2**32






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )
 if shift < 0:
  raise ValueError("Shift must be non-negative"     )
 return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	preprocess(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[int(2**32 * abs(sin(i + 1     )     )     ) for i in range(64     )]

 # Starting states
 __SCREAMING_SNAKE_CASE       :    Tuple       =	0X67452301
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0Xefcdab89
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0X98badcfe
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0X10325476

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
 ]

 # Process bit string in chunks, each with 16 32-char words
 for block_words in get_block_words(__lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    Any       =	aa
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	ba
  __SCREAMING_SNAKE_CASE       :    str       =	ca
  __SCREAMING_SNAKE_CASE       :    Dict       =	da

  # Hash current chunk
  for i in range(64     ):
   if i <= 15:
    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	d ^ (b & (c ^ d))
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	i
   elif i <= 31:
    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
    __SCREAMING_SNAKE_CASE       :    int       =	c ^ (d & (b ^ c))
    __SCREAMING_SNAKE_CASE       :    int       =	(5 * i + 1) % 16
   elif i <= 47:
    __SCREAMING_SNAKE_CASE       :    List[str]       =	b ^ c ^ d
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(3 * i + 5) % 16
   else:
    __SCREAMING_SNAKE_CASE       :    Any       =	c ^ (b | not_aa(__lowerCamelCase     ))
    __SCREAMING_SNAKE_CASE       :    str       =	(7 * i) % 16
   __SCREAMING_SNAKE_CASE       :    List[str]       =	(f + a + added_consts[i] + block_words[g]) % 2**32
   __SCREAMING_SNAKE_CASE       :    Dict       =	d
   __SCREAMING_SNAKE_CASE       :    str       =	c
   __SCREAMING_SNAKE_CASE       :    Tuple       =	b
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum_aa(__lowerCamelCase		,	left_rotate_aa(__lowerCamelCase		,	shift_amounts[i]     )     )

  # Add hashed chunk to running total
  __SCREAMING_SNAKE_CASE       :    Dict       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    str       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[str]       =	reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     )
 return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()






