






'''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    _UpperCAmelCase			:




   """simple docstring"""

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





         '''simple docstring'''





         _A			     =	parent
         _A			     =	batch_size
         _A			     =	is_training
         _A			     =	use_auxiliary_loss
         _A			     =	num_queries
         _A			     =	num_channels
         _A			     =	min_size
         _A			     =	max_size
         _A			     =	num_labels
         _A			     =	hidden_dim
         _A			     =	hidden_dim

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         _A			     =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
             __UpperCAmelCase		)

         _A			     =	torch.ones([self.batch_size, self.min_size, self.max_size]      ,      device=__UpperCAmelCase		)

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

         _A			     =	self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels

   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





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

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

         _A			     =	64
         _A			     =	128
         _A			     =	self.hidden_dim
         _A			     =	self.hidden_dim
         _A			     =	self.hidden_dim
         return config

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





         _A     ,	_A     ,	_A     ,	_A     ,	_A			     =	self.prepare_config_and_inputs()
         _A			     =	{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
         return config, inputs_dict

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	Optional[int]		):





         '''simple docstring'''





         _A			     =	output.encoder_hidden_states
         _A			     =	output.pixel_decoder_hidden_states
         _A			     =	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 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	List[str]=False		):





         '''simple docstring'''





         with torch.no_grad():
               _A			     =	MaskaFormerModel(config=__UpperCAmelCase		)
               model.to(__UpperCAmelCase		)
               model.eval()

               _A			     =	model(pixel_values=__UpperCAmelCase      ,      pixel_mask=__UpperCAmelCase		)
               _A			     =	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 						lowerCAmelCase (		self :	Union[str, Any]      ,      __UpperCAmelCase :	List[Any]      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	MaskaFormerForUniversalSegmentation(config=__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()

         def comm_check_on_output(__UpperCAmelCase :	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():
               _A			     =	model(pixel_values=__UpperCAmelCase      ,      pixel_mask=__UpperCAmelCase		)
               _A			     =	model(__UpperCAmelCase		)

               comm_check_on_output(__UpperCAmelCase		)

               _A			     =	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    _UpperCAmelCase			(     snake_case_							,    snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   snake_case    				=						{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

   snake_case    				=						False
   snake_case    				=						False
   snake_case    				=						False
   snake_case    				=						False

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A			     =	MaskaFormerModelTester(self		)
         _A			     =	ConfigTester(self      ,      config_class=__UpperCAmelCase      ,      has_text_modality=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Any		):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A     ,	_A			     =	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase      ,      **__UpperCAmelCase      ,      output_hidden_states=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A			     =	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 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         pass

   @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"		)
   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         pass

   @unittest.skip(reason="Mask2Former is not a generative model"		)
   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         pass

   @unittest.skip(reason="Mask2Former does not use token embeddings"		)
   def 						lowerCAmelCase (		self :	Union[str, 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 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         pass

   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         pass

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         _A     ,	_A			     =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               _A			     =	model_class(__UpperCAmelCase		)
               _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]      ,      __UpperCAmelCase		)

   @slow
   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               _A			     =	MaskaFormerModel.from_pretrained(__UpperCAmelCase		)
               self.assertIsNotNone(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





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

         _A			     =	MaskaFormerForUniversalSegmentation(__UpperCAmelCase		).to(__UpperCAmelCase		)
         _A			     =	model(**__UpperCAmelCase		)
         self.assertTrue(outputs.loss is not None		)

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A     ,	_A			     =	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase      ,      **__UpperCAmelCase      ,      output_hidden_states=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         _A     ,	_A			     =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               _A			     =	model_class(__UpperCAmelCase		).to(__UpperCAmelCase		)
               _A			     =	model(**__UpperCAmelCase      ,      output_attentions=__UpperCAmelCase		)
               self.assertTrue(outputs.attentions is not None		)

   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         if not self.model_tester.is_training:
               return

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

         _A			     =	model_class(__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.train()

         _A			     =	model(__UpperCAmelCase      ,      mask_labels=__UpperCAmelCase      ,      class_labels=__UpperCAmelCase		).loss
         loss.backward()

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





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

         _A			     =	model_class(__UpperCAmelCase		).to(__UpperCAmelCase		)
         model.train()

         _A			     =	model(__UpperCAmelCase      ,      mask_labels=__UpperCAmelCase      ,      class_labels=__UpperCAmelCase		)

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

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

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

         _A			     =	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 					__lowercase						(			)       ->						Optional[int]:

      '''simple docstring'''


      _A			     =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
      return image


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




   """simple docstring"""

   @cached_property
   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         return "facebook/mask2former-swin-small-coco-instance"

   @cached_property
   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None

   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         _A			     =	MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(__UpperCAmelCase		)
         _A			     =	self.default_image_processor
         _A			     =	prepare_img()
         _A			     =	image_processor(__UpperCAmelCase      ,      return_tensors="pt"		).to(__UpperCAmelCase		)
         _A			     =	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(__UpperCAmelCase      ,      (1, 3, 384, 384)		)

         with torch.no_grad():
               _A			     =	model(**__UpperCAmelCase		)

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

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

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

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         _A			     =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(__UpperCAmelCase		).eval()
         _A			     =	self.default_image_processor
         _A			     =	prepare_img()
         _A			     =	image_processor(__UpperCAmelCase      ,      return_tensors="pt"		).to(__UpperCAmelCase		)
         _A			     =	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(__UpperCAmelCase      ,      (1, 3, 384, 384)		)

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

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





         _A			     =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(__UpperCAmelCase		).eval()
         _A			     =	self.default_image_processor

         _A			     =	image_processor(
             [np.zeros((3, 800, 1333)		), np.zeros((3, 800, 1333)		)]      ,      segmentation_maps=[np.zeros((384, 384)		).astype(np.floataa		), np.zeros((384, 384)		).astype(np.floataa		)]      ,      return_tensors="pt"      ,      )

         _A			     =	inputs["pixel_values"].to(__UpperCAmelCase		)
         _A			     =	[el.to(__UpperCAmelCase		) for el in inputs["mask_labels"]]
         _A			     =	[el.to(__UpperCAmelCase		) for el in inputs["class_labels"]]

         with torch.no_grad():
               _A			     =	model(**__UpperCAmelCase		)

         self.assertTrue(outputs.loss is not None		)








"""simple docstring"""





from collections.abc import Sequence




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   return sum(c * (x**i) for i, c in enumerate(UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   __lowercase								=						0.0
   for coeff in reversed(UpperCamelCase_):
      __lowercase								=						result * x + coeff
   return result


if __name__ == "__main__":
   _a       =							(0.0, 0.0, 5.0, 9.3, 7.0)
   _a       =							10.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))




