

"""simple docstring"""


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

			if is_vision_available():
						from transformers import MaskFormerImageProcessor

if is_vision_available():
			from PIL import Image





class     snake_case	:

				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[str]=2     ,  UpperCamelCase__							:				Tuple=True     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				Tuple=1_0     ,  UpperCamelCase__							:				Tuple=3     ,  UpperCamelCase__							:				Tuple=3_2 * 4     ,  UpperCamelCase__							:				Union[str, Any]=3_2 * 6     ,  UpperCamelCase__							:				Optional[int]=4     ,  UpperCamelCase__							:				List[str]=3_2     ,  )->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			parent
						__lowerCAmelCase:       Dict          =			batch_size
						__lowerCAmelCase:       Optional[Any]          =			is_training
						__lowerCAmelCase:       List[str]          =			use_auxiliary_loss
						__lowerCAmelCase:       Dict          =			num_queries
						__lowerCAmelCase:       int          =			num_channels
						__lowerCAmelCase:       Dict          =			min_size
						__lowerCAmelCase:       Dict          =			max_size
						__lowerCAmelCase:       str          =			num_labels
						__lowerCAmelCase:       Any          =			mask_feature_size

				def 							lowercase_     (	self							:				str)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
						    UpperCamelCase__)

						__lowerCAmelCase:       Any          =			torch.ones([self.batch_size, self.min_size, self.max_size]     ,  device=UpperCamelCase__)

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

						__lowerCAmelCase:       int          =			self.get_config()
						return config, pixel_values, pixel_mask, mask_labels, class_labels

				def 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						return MaskFormerConfig.from_backbone_and_decoder_configs(
						    backbone_config=SwinConfig(
						        depths=[1, 1, 1, 1]     ,  )     ,  decoder_config=DetrConfig(
						        decoder_ffn_dim=1_2_8     ,  num_queries=self.num_queries     ,  decoder_attention_heads=2     ,  d_model=self.mask_feature_size     ,  )     ,  mask_feature_size=self.mask_feature_size     ,  fpn_feature_size=self.mask_feature_size     ,  num_channels=self.num_channels     ,  num_labels=self.num_labels     ,  )

				def 							lowercase_     (	self							:				str)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			self.prepare_config_and_inputs()
						__lowerCAmelCase:       Union[str, Any]          =			{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
						return config, inputs_dict

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				str)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			output.encoder_hidden_states
						__lowerCAmelCase:       Tuple          =			output.pixel_decoder_hidden_states
						__lowerCAmelCase:       Tuple          =			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_config.decoder_layers)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				Any=False)->   str:



						'''simple docstring'''


						with torch.no_grad():
								__lowerCAmelCase:       Optional[int]          =			MaskFormerModel(config=UpperCamelCase__)
								model.to(UpperCamelCase__)
								model.eval()

								__lowerCAmelCase:       Dict          =			model(pixel_values=UpperCamelCase__     ,  pixel_mask=UpperCamelCase__)
								__lowerCAmelCase:       Union[str, Any]          =			model(UpperCamelCase__     ,  output_hidden_states=UpperCamelCase__)
						# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
						# encoder and pixel decoder
						self.parent.assertEqual(
						    output.transformer_decoder_last_hidden_state.shape     ,  (self.batch_size, self.num_queries, self.mask_feature_size)     ,  )
						# 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 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				int)->   Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			MaskFormerForInstanceSegmentation(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()

						def comm_check_on_output(UpperCamelCase__							:				List[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():
								__lowerCAmelCase:       int          =			model(pixel_values=UpperCamelCase__     ,  pixel_mask=UpperCamelCase__)
								__lowerCAmelCase:       List[Any]          =			model(UpperCamelCase__)

								comm_check_on_output(UpperCamelCase__)

								__lowerCAmelCase:       Tuple          =			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     snake_case	(				__snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      int         =  (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_   :      int         =  (
				    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  False
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  False
				SCREAMING_SNAKE_CASE_   :      str         =  False
				SCREAMING_SNAKE_CASE_   :      Dict         =  False

				def 							lowercase_     (	self							:				Optional[int])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			MaskFormerModelTester(self)
						__lowerCAmelCase:       Union[str, Any]          =			ConfigTester(self     ,  config_class=UpperCamelCase__     ,  has_text_modality=UpperCamelCase__)

				def 							lowercase_     (	self							:				Dict)->   List[str]:



						'''simple docstring'''


						self.config_tester.run_common_tests()

				def 							lowercase_     (	self							:				Tuple)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskformer_model(UpperCamelCase__     ,  **UpperCamelCase__     ,  output_hidden_states=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[int])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*UpperCamelCase__)

				@unittest.skip(reason="MaskFormer does not use inputs_embeds")
				def 							lowercase_     (	self							:				Optional[int])->   List[Any]:



						'''simple docstring'''


						pass

				@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method")
				def 							lowercase_     (	self							:				Tuple)->   Optional[int]:



						'''simple docstring'''


						pass

				@unittest.skip(reason="MaskFormer is not a generative model")
				def 							lowercase_     (	self							:				int)->   Union[str, Any]:



						'''simple docstring'''


						pass

				@unittest.skip(reason="MaskFormer does not use token embeddings")
				def 							lowercase_     (	self							:				Dict)->   List[str]:



						'''simple docstring'''


						pass

				@require_torch_multi_gpu
				@unittest.skip(
				    reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`")
				def 							lowercase_     (	self							:				List[str])->   Optional[Any]:



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				str)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       int          =			self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowerCAmelCase:       List[str]          =			["pixel_values"]
								self.assertListEqual(arg_names[:1]     ,  UpperCamelCase__)

				@slow
				def 							lowercase_     (	self							:				List[Any])->   Optional[Any]:



						'''simple docstring'''


						for model_name in ["facebook/maskformer-swin-small-coco"]:
								__lowerCAmelCase:       Dict          =			MaskFormerModel.from_pretrained(UpperCamelCase__)
								self.assertIsNotNone(UpperCamelCase__)

				def 							lowercase_     (	self							:				int)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			(self.model_tester.min_size,) * 2
						__lowerCAmelCase:       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(),
						}

						__lowerCAmelCase:       Optional[Any]          =			MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			model(**UpperCamelCase__)
						self.assertTrue(outputs.loss is not None)

				def 							lowercase_     (	self							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskformer_model(UpperCamelCase__     ,  **UpperCamelCase__     ,  output_hidden_states=UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any])->   Dict:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								__lowerCAmelCase:       Union[str, Any]          =			model_class(UpperCamelCase__).to(UpperCamelCase__)
								__lowerCAmelCase:       Tuple          =			model(**UpperCamelCase__     ,  output_attentions=UpperCamelCase__)
								self.assertTrue(outputs.attentions is not None)

				def 							lowercase_     (	self							:				int)->   Any:



						'''simple docstring'''


						if not self.model_tester.is_training:
								return
						# only MaskFormerForInstanceSegmentation has the loss
						__lowerCAmelCase:       Dict          =			self.all_model_classes[1]
						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			self.model_tester.prepare_config_and_inputs()

						__lowerCAmelCase:       str          =			model_class(UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.train()

						__lowerCAmelCase:       Optional[Any]          =			model(UpperCamelCase__     ,  mask_labels=UpperCamelCase__     ,  class_labels=UpperCamelCase__).loss
						loss.backward()




				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			self.all_model_classes[1]
						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Dict          =			self.model_tester.prepare_config_and_inputs()
						__lowerCAmelCase:       Any          =			True
						__lowerCAmelCase:       int          =			True

						__lowerCAmelCase:       Tuple          =			model_class(UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.train()

						__lowerCAmelCase:       int          =			model(UpperCamelCase__     ,  mask_labels=UpperCamelCase__     ,  class_labels=UpperCamelCase__)

						__lowerCAmelCase:       Optional[Any]          =			outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						__lowerCAmelCase:       List[Any]          =			outputs.pixel_decoder_hidden_states[0]
						pixel_decoder_hidden_states.retain_grad()
						# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
						__lowerCAmelCase:       List[Any]          =			outputs.transformer_decoder_hidden_states[0]
						transformer_decoder_hidden_states.retain_grad()

						__lowerCAmelCase:       List[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)


__A						   =			1E-4






def     a__    (    )	->	Any:
		__lowerCAmelCase:       Any          =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
		return image





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

				@cached_property
				def 							lowercase_     (	self							:				Any)->   Dict:



						'''simple docstring'''


						return (
						    MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco")
						    if is_vision_available()
						    else None
						)

				def 							lowercase_     (	self							:				Optional[Any])->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco").to(UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			self.default_image_processor
						__lowerCAmelCase:       Dict          =			prepare_img()
						__lowerCAmelCase:       Dict          =			image_processor(UpperCamelCase__     ,  return_tensors="pt").to(UpperCamelCase__)
						__lowerCAmelCase:       Union[str, Any]          =			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, 8_0_0, 1_0_8_8))

						with torch.no_grad():
								__lowerCAmelCase:       Union[str, Any]          =			model(**UpperCamelCase__)

						__lowerCAmelCase:       Optional[Any]          =			torch.tensor(
						    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]).to(UpperCamelCase__)
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,  UpperCamelCase__     ,  atol=UpperCamelCase__))

						__lowerCAmelCase:       List[Any]          =			torch.tensor(
						    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]).to(UpperCamelCase__)
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,  UpperCamelCase__     ,  atol=UpperCamelCase__))

						__lowerCAmelCase:       Tuple          =			torch.tensor(
						    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]).to(UpperCamelCase__)
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,  UpperCamelCase__     ,  atol=UpperCamelCase__))

				def 							lowercase_     (	self							:				int)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			(
						    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco")
						    .to(UpperCamelCase__)
						    .eval()
						)
						__lowerCAmelCase:       List[Any]          =			self.default_image_processor
						__lowerCAmelCase:       str          =			prepare_img()
						__lowerCAmelCase:       int          =			image_processor(UpperCamelCase__     ,  return_tensors="pt").to(UpperCamelCase__)
						__lowerCAmelCase:       Any          =			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, 8_0_0, 1_0_8_8))

						with torch.no_grad():
								__lowerCAmelCase:       Optional[int]          =			model(**UpperCamelCase__)
						# masks_queries_logits
						__lowerCAmelCase:       int          =			outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape     ,  (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     ,  )
						__lowerCAmelCase:       Optional[Any]          =			[
						    [-1.3737124, -1.7724937, -1.9364233],
						    [-1.5977281, -1.9867939, -2.1523695],
						    [-1.5795398, -1.9269832, -2.093942],
						]
						__lowerCAmelCase:       Optional[int]          =			torch.tensor(UpperCamelCase__).to(UpperCamelCase__)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,  UpperCamelCase__     ,  atol=UpperCamelCase__))
						# class_queries_logits
						__lowerCAmelCase:       Union[str, Any]          =			outputs.class_queries_logits
						self.assertEqual(
						    class_queries_logits.shape     ,  (1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
						__lowerCAmelCase:       Optional[int]          =			torch.tensor(
						    [
						        [1.6512e00, -5.2572e00, -3.3519e00],
						        [3.6169e-02, -5.9025e00, -2.9313e00],
						        [1.0766e-04, -7.7630e00, -5.1263e00],
						    ]).to(UpperCamelCase__)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,  UpperCamelCase__     ,  atol=UpperCamelCase__))

				def 							lowercase_     (	self							:				List[Any])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			(
						    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff")
						    .to(UpperCamelCase__)
						    .eval()
						)
						__lowerCAmelCase:       Dict          =			self.default_image_processor
						__lowerCAmelCase:       List[str]          =			prepare_img()
						__lowerCAmelCase:       Tuple          =			image_processor(UpperCamelCase__     ,  return_tensors="pt").to(UpperCamelCase__)
						__lowerCAmelCase:       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, 8_0_0, 1_0_8_8))

						with torch.no_grad():
								__lowerCAmelCase:       Dict          =			model(**UpperCamelCase__)
						# masks_queries_logits
						__lowerCAmelCase:       List[str]          =			outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape     ,  (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     ,  )
						__lowerCAmelCase:       Optional[int]          =			[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
						__lowerCAmelCase:       int          =			torch.tensor(UpperCamelCase__).to(UpperCamelCase__)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,  UpperCamelCase__     ,  atol=UpperCamelCase__))
						# class_queries_logits
						__lowerCAmelCase:       List[Any]          =			outputs.class_queries_logits
						self.assertEqual(
						    class_queries_logits.shape     ,  (1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
						__lowerCAmelCase:       Any          =			torch.tensor(
						    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]).to(UpperCamelCase__)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,  UpperCamelCase__     ,  atol=UpperCamelCase__))




				def 							lowercase_     (	self							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			(
						    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco")
						    .to(UpperCamelCase__)
						    .eval()
						)
						__lowerCAmelCase:       List[Any]          =			self.default_image_processor

						__lowerCAmelCase:       Optional[Any]          =			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"     ,  )

						__lowerCAmelCase:       Union[str, Any]          =			inputs["pixel_values"].to(UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			[el.to(UpperCamelCase__) for el in inputs["mask_labels"]]
						__lowerCAmelCase:       Optional[Any]          =			[el.to(UpperCamelCase__) for el in inputs["class_labels"]]

						with torch.no_grad():
								__lowerCAmelCase:       int          =			model(**UpperCamelCase__)

						self.assertTrue(outputs.loss is not None)





"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

__A						   =			logging.get_logger(__name__)


__A						   =			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__A						   =			{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__A						   =			{"facebook/blenderbot-3B": 128}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      str         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  BlenderbotTokenizer

				def __init__(	self							:				Any     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				Tuple=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				Optional[Any]="replace"     ,  UpperCamelCase__							:				int="<s>"     ,  UpperCamelCase__							:				str="</s>"     ,  UpperCamelCase__							:				List[Any]="</s>"     ,  UpperCamelCase__							:				Union[str, Any]="<s>"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="<pad>"     ,  UpperCamelCase__							:				List[Any]="<mask>"     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				int=True     ,  **UpperCamelCase__							:				Union[str, Any]     ,  )->   int:



						'''simple docstring'''


						super().__init__(
						    UpperCamelCase__     ,  UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  errors=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Any          =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  pre_tok_state.pop("type"))
								__lowerCAmelCase:       int          =			add_prefix_space
								__lowerCAmelCase:       str          =			pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			add_prefix_space

						__lowerCAmelCase:       Union[str, Any]          =			"post_processor"
						__lowerCAmelCase:       Tuple          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       List[Any]          =			json.loads(tokenizer_component_instance.__getstate__())

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
										__lowerCAmelCase:       str          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       Any          =			tuple(state["cls"])

								__lowerCAmelCase:       int          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Dict          =			add_prefix_space
										__lowerCAmelCase:       Optional[Any]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Dict          =			trim_offsets
										__lowerCAmelCase:       Tuple          =			True

								if changes_to_apply:
										__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       str          =			component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)

				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 							lowercase_     (	self							:				Optional[Any])->   str:



						'''simple docstring'''


						if self._mask_token is None:
								if self.verbose:
										logger.error("Using mask_token, but it is not set yet.")
								return None
						return str(self._mask_token)

				@mask_token.setter
				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       Any          =			value

				def 							lowercase_     (	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				List[str]     ,  **UpperCamelCase__							:				Optional[int])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)
						assert self.add_prefix_space or not is_split_into_words, (
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				Optional[int]     ,  **UpperCamelCase__							:				int)->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)

						assert self.add_prefix_space or not is_split_into_words, (
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[self.cls_token_id]

						if token_ids_a is None:
								return len(cls + token_ids_a + sep) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[Any]:



						'''simple docstring'''


						return token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				"Conversation")->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[]
						for is_user, text in conversation.iter_texts():
								if is_user:
										# We need to space prefix as it's being done within blenderbot
										inputs.append(" " + text)
								else:
										# Generated responses should contain them already.
										inputs.append(UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			"  ".join(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			self.encode(UpperCamelCase__)
						if len(UpperCamelCase__) > self.model_max_length:
								__lowerCAmelCase:       int          =			input_ids[-self.model_max_length :]
								logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.")
						return input_ids


