






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 				SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(   self	:    Tuple				,						__A	:    List[str]				,						__A	:    List[Any]=2				,						__A	:    Union[str, Any]=True				,						__A	:    Dict=False				,						__A	:    Union[str, Any]=1_0				,						__A	:    Any=3				,						__A	:    Any=3_2 * 4				,						__A	:    int=3_2 * 6				,						__A	:    Optional[int]=4				,						__A	:    List[str]=3_2				,						):
						snake_case__					: List[str]  =    parent
						snake_case__					: Optional[int]  =    batch_size
						snake_case__					: Any  =    is_training
						snake_case__					: List[str]  =    use_auxiliary_loss
						snake_case__					: List[str]  =    num_queries
						snake_case__					: int  =    num_channels
						snake_case__					: Union[str, Any]  =    min_size
						snake_case__					: Dict  =    max_size
						snake_case__					: Optional[Any]  =    num_labels
						snake_case__					: Any  =    mask_feature_size



					def 			_lowercase	(   self	:    str			):
						snake_case__					: List[str]  =    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
						    A_			)

						snake_case__					: Optional[int]  =    torch.ones([self.batch_size, self.min_size, self.max_size]				,						device=A_			)

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

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



					def 			_lowercase	(   self	:    Optional[Any]			):
						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	:    Union[str, Any]			):
						snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__					: Tuple  =    self.prepare_config_and_inputs()
						snake_case__					: Dict  =    {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
						return config, inputs_dict



					def 			_lowercase	(   self	:    int				,						__A	:    str				,						__A	:    Union[str, Any]			):
						snake_case__					: Union[str, Any]  =    output.encoder_hidden_states
						snake_case__					: List[str]  =    output.pixel_decoder_hidden_states
						snake_case__					: Tuple  =    output.transformer_decoder_hidden_states

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



					def 			_lowercase	(   self	:    List[Any]				,						__A	:    Dict				,						__A	:    Any				,						__A	:    Optional[int]				,						__A	:    List[Any]=False			):




						with torch.no_grad():
							snake_case__					: Union[str, Any]  =    MaskFormerModel(config=A_			)
							model.to(A_			)
							model.eval()

							snake_case__					: Optional[int]  =    model(pixel_values=A_				,						pixel_mask=A_			)
							snake_case__					: Tuple  =    model(A_				,						output_hidden_states=A_			)
						# 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(A_				,						A_			)






					def 			_lowercase	(   self	:    List[str]				,						__A	:    str				,						__A	:    List[Any]				,						__A	:    List[str]				,						__A	:    str				,						__A	:    Union[str, Any]			):
						snake_case__					: Union[str, Any]  =    MaskFormerForInstanceSegmentation(config=A_			)
						model.to(A_			)
						model.eval()

						def comm_check_on_output(__A	:    Optional[int]			):
							# 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():
							snake_case__					: List[Any]  =    model(pixel_values=A_				,						pixel_mask=A_			)
							snake_case__					: Optional[Any]  =    model(A_			)

							comm_check_on_output(A_			)

							snake_case__					: Any  =    model(
							    pixel_values=A_				,						pixel_mask=A_				,						mask_labels=A_				,						class_labels=A_			)

						comm_check_on_output(A_			)

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



@require_torch
class 				SCREAMING_SNAKE_CASE__		(							_a					,  _a					,  unittest.TestCase		):





					"""simple docstring"""






					a_      =						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
					a_      =						(
					    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
					    if is_torch_available()
					    else {}
					)

					a_      =						False
					a_      =						False
					a_      =						False
					a_      =						False



					def 			_lowercase	(   self	:    Dict			):
						snake_case__					: Any  =    MaskFormerModelTester(self			)
						snake_case__					: int  =    ConfigTester(self				,						config_class=A_				,						has_text_modality=A_			)



					def 			_lowercase	(   self	:    int			):
						self.config_tester.run_common_tests()



					def 			_lowercase	(   self	:    List[str]			):
						snake_case__,		snake_case__					: List[str]  =    self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskformer_model(A_				,						**A_				,						output_hidden_states=A_			)



					def 			_lowercase	(   self	:    Union[str, Any]			):
						snake_case__					: str  =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*A_			)



					@unittest.skip(reason="MaskFormer does not use inputs_embeds"			)
					def 			_lowercase	(   self	:    Tuple			):
						pass



					@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"			)
					def 			_lowercase	(   self	:    Tuple			):
						pass



					@unittest.skip(reason="MaskFormer is not a generative model"			)
					def 			_lowercase	(   self	:    Any			):
						pass



					@unittest.skip(reason="MaskFormer does not use token embeddings"			)
					def 			_lowercase	(   self	:    Tuple			):
						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]			):
						pass



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



					def 			_lowercase	(   self	:    Optional[Any]			):
						snake_case__,		snake_case__					: Any  =    self.model_tester.prepare_config_and_inputs_for_common()

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

							snake_case__					: str  =    ["pixel_values"]
							self.assertListEqual(arg_names[:1]				,						A_			)



					@slow
					def 			_lowercase	(   self	:    Any			):




						for model_name in ["facebook/maskformer-swin-small-coco"]:
							snake_case__					: Union[str, Any]  =    MaskFormerModel.from_pretrained(A_			)
							self.assertIsNotNone(A_			)



					def 			_lowercase	(   self	:    int			):
						snake_case__					: int  =    (self.model_tester.min_size,) * 2
						snake_case__					: List[Any]  =    {
						    "pixel_values": torch.randn((2, 3, *size)				,						device=A_			),
						    "mask_labels": torch.randn((2, 1_0, *size)				,						device=A_			),
						    "class_labels": torch.zeros(2				,						1_0				,						device=A_			).long(),
						}

						snake_case__					: List[str]  =    MaskFormerForInstanceSegmentation(MaskFormerConfig()			).to(A_			)
						snake_case__					: Optional[Any]  =    model(**A_			)
						self.assertTrue(outputs.loss is not None			)



					def 			_lowercase	(   self	:    Tuple			):
						snake_case__,		snake_case__					: Union[str, Any]  =    self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskformer_model(A_				,						**A_				,						output_hidden_states=A_			)



					def 			_lowercase	(   self	:    Any			):
						snake_case__,		snake_case__					: Union[str, Any]  =    self.model_tester.prepare_config_and_inputs_for_common()

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



					def 			_lowercase	(   self	:    Any			):




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

						snake_case__					: str  =    model_class(A_			)
						model.to(A_			)
						model.train()

						snake_case__					: Union[str, Any]  =    model(A_				,						mask_labels=A_				,						class_labels=A_			).loss
						loss.backward()






					def 			_lowercase	(   self	:    List[Any]			):
						snake_case__					: Union[str, Any]  =    self.all_model_classes[1]
						snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__					: List[str]  =    self.model_tester.prepare_config_and_inputs()
						snake_case__					: List[str]  =    True
						snake_case__					: Optional[Any]  =    True

						snake_case__					: str  =    model_class(A_			)
						model.to(A_			)
						model.train()

						snake_case__					: Any  =    model(A_				,						mask_labels=A_				,						class_labels=A_			)

						snake_case__					: Union[str, Any]  =    outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						snake_case__					: Optional[int]  =    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
						snake_case__					: Optional[int]  =    outputs.transformer_decoder_hidden_states[0]
						transformer_decoder_hidden_states.retain_grad()

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

						outputs.loss.backward(retain_graph=A_			)

						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   :     Dict 		= 1e-4







def 	SCREAMING_SNAKE_CASE							(					):
	snake_case__					: Union[str, Any]  =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
	return image





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





					"""simple docstring"""






					@cached_property
					def 			_lowercase	(   self	:    Dict			):
						return (
						    MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"			)
						    if is_vision_available()
						    else None
						)



					def 			_lowercase	(   self	:    Dict			):
						snake_case__					: int  =    MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"			).to(A_			)
						snake_case__					: List[Any]  =    self.default_image_processor
						snake_case__					: List[Any]  =    prepare_img()
						snake_case__					: str  =    image_processor(A_				,						return_tensors="pt"			).to(A_			)
						snake_case__					: List[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(A_				,						(1, 3, 8_0_0, 1_0_8_8)			)

						with torch.no_grad():
							snake_case__					: List[str]  =    model(**A_			)

						snake_case__					: Dict  =    torch.tensor(
						    [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]			).to(A_			)
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]				,						A_				,						atol=A_			)			)

						snake_case__					: Union[str, Any]  =    torch.tensor(
						    [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]			).to(A_			)
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,						A_				,						atol=A_			)			)

						snake_case__					: Dict  =    torch.tensor(
						    [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]			).to(A_			)
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,						A_				,						atol=A_			)			)



					def 			_lowercase	(   self	:    Optional[Any]			):
						snake_case__					: Optional[Any]  =    (
						    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"			)
						    .to(A_			)
						    .eval()
						)
						snake_case__					: List[str]  =    self.default_image_processor
						snake_case__					: List[Any]  =    prepare_img()
						snake_case__					: Dict  =    image_processor(A_				,						return_tensors="pt"			).to(A_			)
						snake_case__					: 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(A_				,						(1, 3, 8_0_0, 1_0_8_8)			)

						with torch.no_grad():
							snake_case__					: List[Any]  =    model(**A_			)
						# masks_queries_logits
						snake_case__					: Optional[Any]  =    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)				,						)
						snake_case__					: Optional[int]  =    [
						    [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
						    [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
						    [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
						]
						snake_case__					: Optional[Any]  =    torch.tensor(A_			).to(A_			)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,						A_				,						atol=A_			)			)
						# class_queries_logits
						snake_case__					: Dict  =    outputs.class_queries_logits
						self.assertEqual(
						    class_queries_logits.shape				,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
						snake_case__					: List[str]  =    torch.tensor(
						    [
						        [1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
						        [3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
						        [1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
						    ]			).to(A_			)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,						A_				,						atol=A_			)			)



					def 			_lowercase	(   self	:    List[str]			):
						snake_case__					: Dict  =    (
						    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"			)
						    .to(A_			)
						    .eval()
						)
						snake_case__					: Dict  =    self.default_image_processor
						snake_case__					: Optional[int]  =    prepare_img()
						snake_case__					: Tuple  =    image_processor(A_				,						return_tensors="pt"			).to(A_			)
						snake_case__					: 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(A_				,						(1, 3, 8_0_0, 1_0_8_8)			)

						with torch.no_grad():
							snake_case__					: Union[str, Any]  =    model(**A_			)
						# masks_queries_logits
						snake_case__					: 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)				,						)
						snake_case__					: str  =    [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
						snake_case__					: Tuple  =    torch.tensor(A_			).to(A_			)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,						A_				,						atol=A_			)			)
						# class_queries_logits
						snake_case__					: List[str]  =    outputs.class_queries_logits
						self.assertEqual(
						    class_queries_logits.shape				,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
						snake_case__					: int  =    torch.tensor(
						    [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]			).to(A_			)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,						A_				,						atol=A_			)			)






					def 			_lowercase	(   self	:    str			):
						snake_case__					: Optional[int]  =    (
						    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"			)
						    .to(A_			)
						    .eval()
						)
						snake_case__					: int  =    self.default_image_processor

						snake_case__					: int  =    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"				,						)

						snake_case__					: Union[str, Any]  =    inputs["pixel_values"].to(A_			)
						snake_case__					: Dict  =    [el.to(A_			) for el in inputs["mask_labels"]]
						snake_case__					: List[Any]  =    [el.to(A_			) for el in inputs["class_labels"]]

						with torch.no_grad():
							snake_case__					: Tuple  =    model(**A_			)

						self.assertTrue(outputs.loss is not None			)







from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets



class 	__lowerCamelCase			(_a						):







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






            '''simple docstring'''




            __UpperCamelCase =			EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny','prajjwal1/bert-tiny'					)
            __UpperCamelCase =			BertTokenizer.from_pretrained('bert-base-uncased'					)

            __UpperCamelCase =			bertabert.config.encoder.vocab_size
            __UpperCamelCase =			tokenizer.sep_token_id
            __UpperCamelCase =			tokenizer.cls_token_id
            __UpperCamelCase =			128

            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='train[:1%]'					)
            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='validation[:1%]'					)

            __UpperCamelCase =			train_dataset.select(range(32					)					)
            __UpperCamelCase =			val_dataset.select(range(16					)					)

            __UpperCamelCase =			4

            def _map_to_encoder_decoder_inputs(A_: Dict					):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 __UpperCamelCase =			tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512					)
                 __UpperCamelCase =			tokenizer(batch['highlights'],padding='max_length',truncation=A_,max_length=128					)
                 __UpperCamelCase =			inputs.input_ids
                 __UpperCamelCase =			inputs.attention_mask

                 __UpperCamelCase =			outputs.input_ids
                 __UpperCamelCase =			outputs.input_ids.copy()
                 __UpperCamelCase =			[
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 __UpperCamelCase =			outputs.attention_mask

                 assert all(len(A_					) == 512 for x in inputs.input_ids					)
                 assert all(len(A_					) == 128 for x in outputs.input_ids					)

                 return batch

            def _compute_metrics(A_: str					):
                 __UpperCamelCase =			pred.label_ids
                 __UpperCamelCase =			pred.predictions

                 # all unnecessary tokens are removed
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)

                 __UpperCamelCase =			sum([int(pred_str[i] == label_str[i]					) for i in range(len(A_					)					)]					) / len(A_					)

                 return {"accuracy": accuracy}

            # map train dataset
            __UpperCamelCase =			train_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            train_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            # same for validation dataset
            __UpperCamelCase =			val_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            val_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            __UpperCamelCase =			self.get_auto_remove_tmp_dir()

            __UpperCamelCase =			SeqaSeqTrainingArguments(
                output_dir=A_,per_device_train_batch_size=A_,per_device_eval_batch_size=A_,predict_with_generate=A_,evaluation_strategy='steps',do_train=A_,do_eval=A_,warmup_steps=0,eval_steps=2,logging_steps=2,)

            # instantiate trainer
            __UpperCamelCase =			SeqaSeqTrainer(
                model=A_,args=A_,compute_metrics=_compute_metrics,train_dataset=A_,eval_dataset=A_,tokenizer=A_,)

            # start training
            trainer.train()


