


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 	__lowerCamelCase			:







       def __init__(			self: Tuple,A_: List[str],A_: List[Any]=2,A_: Union[str, Any]=True,A_: Dict=False,A_: Union[str, Any]=10,A_: Any=3,A_: Any=32 * 4,A_: int=32 * 6,A_: Optional[int]=4,A_: List[str]=32,):






            '''simple docstring'''




            __UpperCamelCase =			parent
            __UpperCamelCase =			batch_size
            __UpperCamelCase =			is_training
            __UpperCamelCase =			use_auxiliary_loss
            __UpperCamelCase =			num_queries
            __UpperCamelCase =			num_channels
            __UpperCamelCase =			min_size
            __UpperCamelCase =			max_size
            __UpperCamelCase =			num_labels
            __UpperCamelCase =			mask_feature_size







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
                A_					)

            __UpperCamelCase =			torch.ones([self.batch_size, self.min_size, self.max_size],device=A_					)

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

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







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            return MaskFormerConfig.from_backbone_and_decoder_configs(
                backbone_config=SwinConfig(
                    depths=[1, 1, 1, 1],),decoder_config=DetrConfig(
                    decoder_ffn_dim=128,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 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			self.prepare_config_and_inputs()
            __UpperCamelCase =			{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
            return config, inputs_dict







       def 		snake_case_	(			self: int,A_: str,A_: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			output.encoder_hidden_states
            __UpperCamelCase =			output.pixel_decoder_hidden_states
            __UpperCamelCase =			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 		snake_case_	(			self: List[Any],A_: Dict,A_: Any,A_: Optional[int],A_: List[Any]=False					):






            '''simple docstring'''




            with torch.no_grad():
                 __UpperCamelCase =			MaskFormerModel(config=A_					)
                 model.to(A_					)
                 model.eval()

                 __UpperCamelCase =			model(pixel_values=A_,pixel_mask=A_					)
                 __UpperCamelCase =			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 		snake_case_	(			self: List[str],A_: str,A_: List[Any],A_: List[str],A_: str,A_: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			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():
                 __UpperCamelCase =			model(pixel_values=A_,pixel_mask=A_					)
                 __UpperCamelCase =			model(A_					)

                 comm_check_on_output(A_					)

                 __UpperCamelCase =			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 	__lowerCamelCase			(_a				,      _a				,      unittest.TestCase						):
       _lowercase											=     (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
       _lowercase											=     (
           {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
           if is_torch_available()
           else {}
       )

       _lowercase											=     False
       _lowercase											=     False
       _lowercase											=     False
       _lowercase											=     False







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			MaskFormerModelTester(self					)
            __UpperCamelCase =			ConfigTester(self,config_class=A_,has_text_modality=A_					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskformer_model(A_,**A_,output_hidden_states=A_					)







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






            '''simple docstring'''




            __UpperCamelCase =			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 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'					)
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='MaskFormer is not a generative model'					)
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='MaskFormer does not use token embeddings'					)
       def 		snake_case_	(			self: Tuple					):






            '''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 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            pass







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






            '''simple docstring'''




            pass







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

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

                 __UpperCamelCase =			['pixel_values']
                 self.assertListEqual(arg_names[:1],A_					)







       @slow
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			(self.model_tester.min_size,) * 2
            __UpperCamelCase =			{
                'pixel_values': torch.randn((2, 3, *size),device=A_					),
                'mask_labels': torch.randn((2, 10, *size),device=A_					),
                'class_labels': torch.zeros(2,10,device=A_					).long(),
            }

            __UpperCamelCase =			MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(A_					)
            __UpperCamelCase =			model(**A_					)
            self.assertTrue(outputs.loss is not None					)







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskformer_model(A_,**A_,output_hidden_states=A_					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




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

            __UpperCamelCase =			model_class(A_					)
            model.to(A_					)
            model.train()

            __UpperCamelCase =			model(A_,mask_labels=A_,class_labels=A_					).loss
            loss.backward()







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




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

            __UpperCamelCase =			model_class(A_					)
            model.to(A_					)
            model.train()

            __UpperCamelCase =			model(A_,mask_labels=A_,class_labels=A_					)

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

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

            __UpperCamelCase =			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					)



__snake_case   							=						1e-4






def 		_A		(		)					-> str:


     """simple docstring"""


     __UpperCamelCase =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
     return image



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







       @cached_property
       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'					).to(A_					)
            __UpperCamelCase =			self.default_image_processor
            __UpperCamelCase =			prepare_img()
            __UpperCamelCase =			image_processor(A_,return_tensors='pt'					).to(A_					)
            __UpperCamelCase =			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(A_,(1, 3, 800, 1088)					)

            with torch.no_grad():
                 __UpperCamelCase =			model(**A_					)

            __UpperCamelCase =			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_					)					)

            __UpperCamelCase =			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_					)					)

            __UpperCamelCase =			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 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			(
                MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'					)
                .to(A_					)
                .eval()
            )
            __UpperCamelCase =			self.default_image_processor
            __UpperCamelCase =			prepare_img()
            __UpperCamelCase =			image_processor(A_,return_tensors='pt'					).to(A_					)
            __UpperCamelCase =			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(A_,(1, 3, 800, 1088)					)

            with torch.no_grad():
                 __UpperCamelCase =			model(**A_					)
            # masks_queries_logits
            __UpperCamelCase =			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),)
            __UpperCamelCase =			[
                [-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],
            ]
            __UpperCamelCase =			torch.tensor(A_					).to(A_					)
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],A_,atol=A_					)					)
            # class_queries_logits
            __UpperCamelCase =			outputs.class_queries_logits
            self.assertEqual(
                class_queries_logits.shape,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
            __UpperCamelCase =			torch.tensor(
                [
                    [1.6_512E00, -5.2_572E00, -3.3_519E00],
                    [3.6_169E-02, -5.9_025E00, -2.9_313E00],
                    [1.0_766E-04, -7.7_630E00, -5.1_263E00],
                ]					).to(A_					)
            self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],A_,atol=A_					)					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			(
                MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'					)
                .to(A_					)
                .eval()
            )
            __UpperCamelCase =			self.default_image_processor
            __UpperCamelCase =			prepare_img()
            __UpperCamelCase =			image_processor(A_,return_tensors='pt'					).to(A_					)
            __UpperCamelCase =			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(A_,(1, 3, 800, 1088)					)

            with torch.no_grad():
                 __UpperCamelCase =			model(**A_					)
            # masks_queries_logits
            __UpperCamelCase =			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),)
            __UpperCamelCase =			[[-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]]
            __UpperCamelCase =			torch.tensor(A_					).to(A_					)
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],A_,atol=A_					)					)
            # class_queries_logits
            __UpperCamelCase =			outputs.class_queries_logits
            self.assertEqual(
                class_queries_logits.shape,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
            __UpperCamelCase =			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 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			(
                MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'					)
                .to(A_					)
                .eval()
            )
            __UpperCamelCase =			self.default_image_processor

            __UpperCamelCase =			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',)

            __UpperCamelCase =			inputs['pixel_values'].to(A_					)
            __UpperCamelCase =			[el.to(A_					) for el in inputs['mask_labels']]
            __UpperCamelCase =			[el.to(A_					) for el in inputs['class_labels']]

            with torch.no_grad():
                 __UpperCamelCase =			model(**A_					)

            self.assertTrue(outputs.loss is not None					)




"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_			,					A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      List[str]   			=  params

         _lowercase:      str   			=  np.array(A_							)
         _lowercase:      Optional[int]   			=  np.array([len(A_							) for t in data]							)

         self.check()
         self.remove_long_sequences()
         self.remove_empty_sequences()
         self.remove_unknown_sequences()
         self.check()
         self.print_statistics()
       def __getitem__(  self			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         return (self.token_ids[index], self.lengths[index])
       def __len__(  self							)  ->						List[str]:





         """simple docstring"""




         return len(self.lengths							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         assert len(self.token_ids							) == len(self.lengths							)
         assert all(self.lengths[i] == len(self.token_ids[i]							) for i in range(len(self.lengths							)							)							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.params.max_model_input_size
         _lowercase:      Tuple   			=  self.lengths > max_len
         logger.info(f'''Splitting {sum(A_							)} too long sequences.'''							)

         def divide_chunks(A_			,					A_							):
           return [l[i : i + n] for i in range(0			,					len(A_							)			,					A_							)]

         _lowercase:      Dict   			=  []
         _lowercase:      Union[str, Any]   			=  []
         if self.params.mlm:
           _lowercase			,					_lowercase:      int   			=  self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
         else:
           _lowercase			,					_lowercase:      Dict   			=  self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

         for seq_, len_ in zip(self.token_ids			,					self.lengths							):
           assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
           if len_ <= max_len:
             new_tok_ids.append(seq_							)
             new_lengths.append(len_							)
           else:
             _lowercase:      Optional[Any]   			=  []
             for sub_s in divide_chunks(seq_			,					max_len - 2							):
               if sub_s[0] != cls_id:
                 _lowercase:      Any   			=  np.insert(A_			,					0			,					A_							)
               if sub_s[-1] != sep_id:
                 _lowercase:      Optional[int]   			=  np.insert(A_			,					len(A_							)			,					A_							)
               assert len(A_							) <= max_len
               assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
               sub_seqs.append(A_							)

             new_tok_ids.extend(A_							)
             new_lengths.extend([len(A_							) for l in sub_seqs]							)

         _lowercase:      Optional[Any]   			=  np.array(A_							)
         _lowercase:      List[str]   			=  np.array(A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  len(self							)
         _lowercase:      Optional[Any]   			=  self.lengths > 11
         _lowercase:      int   			=  self.token_ids[indices]
         _lowercase:      List[str]   			=  self.lengths[indices]
         _lowercase:      Optional[int]   			=  len(self							)
         logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         if "unk_token" not in self.params.special_tok_ids:
           return
         else:
           _lowercase:      Dict   			=  self.params.special_tok_ids['''unk_token''']
         _lowercase:      int   			=  len(self							)
         _lowercase:      Tuple   			=  np.array([np.count_nonzero(a == unk_token_id							) for a in self.token_ids]							)
         _lowercase:      Dict   			=  (unk_occs / self.lengths) < 0.5
         _lowercase:      str   			=  self.token_ids[indices]
         _lowercase:      Dict   			=  self.lengths[indices]
         _lowercase:      int   			=  len(self							)
         logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         if not self.params.is_master:
           return
         logger.info(f'''{len(self							)} sequences'''							)
         # data_len = sum(self.lengths)
         # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
         # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

         # unk_idx = self.params.special_tok_ids['unk_token']
         # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
         # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')



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





         """simple docstring"""




         _lowercase:      List[str]   			=  [t[0] for t in batch]
         _lowercase:      Dict   			=  [t[1] for t in batch]
         assert len(A_							) == len(A_							)

         # Max for paddings
         _lowercase:      Tuple   			=  max(A_							)

         # Pad token ids
         if self.params.mlm:
           _lowercase:      str   			=  self.params.special_tok_ids['''pad_token''']
         else:
           _lowercase:      Union[str, Any]   			=  self.params.special_tok_ids['''unk_token''']
         _lowercase:      int   			=  [list(t.astype(A_							)							) + [pad_idx] * (max_seq_len_ - len(A_							)) for t in token_ids]
         assert len(tk_							) == len(A_							)
         assert all(len(A_							) == max_seq_len_ for t in tk_							)

         _lowercase:      str   			=  torch.tensor(tk_							)  # (bs, max_seq_len_)
         _lowercase:      int   			=  torch.tensor(A_							)  # (bs)
         return tk_t, lg_t
