





'''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     :
	def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:Any=2						,	lowercase_				:Optional[int]=True						,	lowercase_				:Union[str, Any]=False						,	lowercase_				:Optional[Any]=10						,	lowercase_				:str=3						,	lowercase_				:List[str]=32 * 8						,	lowercase_				:Any=32 * 8						,	lowercase_				:Optional[int]=4						,	lowercase_				:Union[str, Any]=64						,	)->							Optional[Any]:
					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 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Any:
					A__    =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
					    lowerCAmelCase__							)

					A__    =      torch.ones([self.batch_size, self.min_size, self.max_size]						,	device=lowerCAmelCase__							)

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

					A__    =      self.get_config()
					return config, pixel_values, pixel_mask, mask_labels, class_labels
	def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
					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__    =      1_28
					A__    =      self.hidden_dim
					A__    =      self.hidden_dim
					A__    =      self.hidden_dim
					return config
	def 					UpperCAmelCase_							(    self				:List[Any]							)->							Union[str, Any]:
					A__    =      self.prepare_config_and_inputs()
					A__    =      {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
					return config, inputs_dict
	def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Tuple							)->							List[Any]:
					A__    =      output.encoder_hidden_states
					A__    =      output.pixel_decoder_hidden_states
					A__    =      output.transformer_decoder_hidden_states

					self.parent.assertTrue(len(lowerCAmelCase__							)						,	len(config.backbone_config.depths							)							)
					self.parent.assertTrue(len(lowerCAmelCase__							)						,	len(config.backbone_config.depths							)							)
					self.parent.assertTrue(len(lowerCAmelCase__							)						,	config.decoder_layers							)
	def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:int						,	lowercase_				:List[Any]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Dict=False							)->							Optional[int]:
					with torch.no_grad():
									A__    =      MaskaFormerModel(config=lowerCAmelCase__							)
									model.to(lowerCAmelCase__							)
									model.eval()

									A__    =      model(pixel_values=lowerCAmelCase__						,	pixel_mask=lowerCAmelCase__							)
									A__    =      model(lowerCAmelCase__						,	output_hidden_states=lowerCAmelCase__							)

					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(lowerCAmelCase__						,	lowerCAmelCase__							)



	def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Any							)->							Tuple:
					A__    =      MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__							)
					model.to(lowerCAmelCase__							)
					model.eval()

					def comm_check_on_output(lowercase_				:List[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=lowerCAmelCase__						,	pixel_mask=lowerCAmelCase__							)
									A__    =      model(lowerCAmelCase__							)

									comm_check_on_output(lowerCAmelCase__							)

									A__    =      model(
									    pixel_values=lowerCAmelCase__						,	pixel_mask=lowerCAmelCase__						,	mask_labels=lowerCAmelCase__						,	class_labels=lowerCAmelCase__							)

					comm_check_on_output(lowerCAmelCase__							)

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






@require_torch
class        UpperCAmelCase     (	a__      ,	a__      ,	unittest.TestCase    ):
	__lowercase       		=    (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
	__lowercase       		=    {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

	__lowercase       		=    False
	__lowercase       		=    False
	__lowercase       		=    False
	__lowercase       		=    False
	def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
					A__    =      MaskaFormerModelTester(self							)
					A__    =      ConfigTester(self						,	config_class=lowerCAmelCase__						,	has_text_modality=lowerCAmelCase__							)
	def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[str]:
					self.config_tester.run_common_tests()
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
					A__    =      self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__						,	**lowerCAmelCase__						,	output_hidden_states=lowerCAmelCase__							)
	def 					UpperCAmelCase_							(    self				:Tuple							)->							int:
					A__    =      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase__							)
	@unittest.skip(reason="Mask2Former does not use inputs_embeds"							)
	def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
					pass
	@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"							)
	def 					UpperCAmelCase_							(    self				:int							)->							List[str]:
					pass
	@unittest.skip(reason="Mask2Former is not a generative model"							)
	def 					UpperCAmelCase_							(    self				:str							)->							Optional[Any]:
					pass
	@unittest.skip(reason="Mask2Former does not use token embeddings"							)
	def 					UpperCAmelCase_							(    self				:Union[str, 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 					UpperCAmelCase_							(    self				:Any							)->							List[str]:
					pass
	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
	def 					UpperCAmelCase_							(    self				:Tuple							)->							List[Any]:
					pass
	def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[Any]:
					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__							)
	@slow
	def 					UpperCAmelCase_							(    self				:Dict							)->							str:
					for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
									A__    =      MaskaFormerModel.from_pretrained(lowerCAmelCase__							)
									self.assertIsNotNone(lowerCAmelCase__							)
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Union[str, Any]:
					A__    =      (self.model_tester.min_size,) * 2
					A__    =      {
					    "pixel_values": torch.randn((2, 3, *size)						,	device=lowerCAmelCase__							),
					    "mask_labels": torch.randn((2, 10, *size)						,	device=lowerCAmelCase__							),
					    "class_labels": torch.zeros(2						,	10						,	device=lowerCAmelCase__							).long(),
					}
					A__    =      self.model_tester.get_config()

					A__    =      MaskaFormerForUniversalSegmentation(lowerCAmelCase__							).to(lowerCAmelCase__							)
					A__    =      model(**lowerCAmelCase__							)
					self.assertTrue(outputs.loss is not None							)
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
					A__    =      self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__						,	**lowerCAmelCase__						,	output_hidden_states=lowerCAmelCase__							)
	def 					UpperCAmelCase_							(    self				:str							)->							List[str]:
					A__    =      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__    =      model_class(lowerCAmelCase__							).to(lowerCAmelCase__							)
									A__    =      model(**lowerCAmelCase__						,	output_attentions=lowerCAmelCase__							)
									self.assertTrue(outputs.attentions is not None							)
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Tuple:
					if not self.model_tester.is_training:
									return

					A__    =      self.all_model_classes[1]
					A__    =      self.model_tester.prepare_config_and_inputs()

					A__    =      model_class(lowerCAmelCase__							)
					model.to(lowerCAmelCase__							)
					model.train()

					A__    =      model(lowerCAmelCase__						,	mask_labels=lowerCAmelCase__						,	class_labels=lowerCAmelCase__							).loss
					loss.backward()



	def 					UpperCAmelCase_							(    self				:Dict							)->							int:
					A__    =      self.all_model_classes[1]
					A__    =      self.model_tester.prepare_config_and_inputs()
					A__    =      True
					A__    =      True

					A__    =      model_class(lowerCAmelCase__							).to(lowerCAmelCase__							)
					model.train()

					A__    =      model(lowerCAmelCase__						,	mask_labels=lowerCAmelCase__						,	class_labels=lowerCAmelCase__							)

					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=lowerCAmelCase__							)

					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							:     List[str]		     =1E-4





def 						UpperCamelCase    (							):
				A__    =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				return image






@require_vision
@slow
class        UpperCAmelCase     (	unittest.TestCase    ):
	@cached_property
	def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
					return "facebook/mask2former-swin-small-coco-instance"
	@cached_property
	def 					UpperCAmelCase_							(    self				:Dict							)->							Dict:
					return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints							) if is_vision_available() else None
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Tuple:
					A__    =      MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(lowerCAmelCase__							)
					A__    =      self.default_image_processor
					A__    =      prepare_img()
					A__    =      image_processor(lowerCAmelCase__						,	return_tensors="pt"							).to(lowerCAmelCase__							)
					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(lowerCAmelCase__						,	(1, 3, 3_84, 3_84)							)

					with torch.no_grad():
									A__    =      model(**lowerCAmelCase__							)

					A__    =      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(lowerCAmelCase__							)
					self.assertTrue(
					    torch.allclose(
					        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,	lowerCAmelCase__						,	atol=lowerCAmelCase__							)							)

					A__    =      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(lowerCAmelCase__							)
					self.assertTrue(
					    torch.allclose(
					        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,	lowerCAmelCase__						,	atol=lowerCAmelCase__							)							)

					A__    =      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(lowerCAmelCase__							)
					self.assertTrue(
					    torch.allclose(
					        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,	lowerCAmelCase__						,	atol=lowerCAmelCase__							)							)
	def 					UpperCAmelCase_							(    self				:Optional[int]							)->							List[Any]:
					A__    =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(lowerCAmelCase__							).eval()
					A__    =      self.default_image_processor
					A__    =      prepare_img()
					A__    =      image_processor(lowerCAmelCase__						,	return_tensors="pt"							).to(lowerCAmelCase__							)
					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(lowerCAmelCase__						,	(1, 3, 3_84, 3_84)							)

					with torch.no_grad():
									A__    =      model(**lowerCAmelCase__							)
					# 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__    =      [
					    [-8.7_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],
					]
					A__    =      torch.tensor(lowerCAmelCase__							).to(lowerCAmelCase__							)
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,	lowerCAmelCase__						,	atol=lowerCAmelCase__							)							)
					# 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(
					    [
					        [1.8_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(lowerCAmelCase__							)
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,	lowerCAmelCase__						,	atol=lowerCAmelCase__							)							)



	def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
					A__    =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(lowerCAmelCase__							).eval()
					A__    =      self.default_image_processor

					A__    =      image_processor(
					    [np.zeros((3, 8_00, 13_33)							), np.zeros((3, 8_00, 13_33)							)]						,	segmentation_maps=[np.zeros((3_84, 3_84)							).astype(np.floataa							), np.zeros((3_84, 3_84)							).astype(np.floataa							)]						,	return_tensors="pt"						,	)

					A__    =      inputs["pixel_values"].to(lowerCAmelCase__							)
					A__    =      [el.to(lowerCAmelCase__							) for el in inputs["mask_labels"]]
					A__    =      [el.to(lowerCAmelCase__							) for el in inputs["class_labels"]]

					with torch.no_grad():
									A__    =      model(**lowerCAmelCase__							)

					self.assertTrue(outputs.loss is not None							)










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : int = 1_00_00_00	):
    A__    =      set(range(3						,	_lowerCamelCase						,	2	)	)
    primes.add(2	)
    for p in range(3						,	_lowerCamelCase						,	2	):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p						,	_lowerCamelCase						,	_lowerCamelCase	)	)	)

    A__    =      [float(_lowerCamelCase	) for n in range(limit + 1	)]

    for p in primes:
        for n in range(_lowerCamelCase						,	limit + 1						,	_lowerCamelCase	):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]	)	)


if __name__ == "__main__":
  print(f"""{solution() = }""")



