






'''simple docstring'''





import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      FocalNetBackbone,
      FocalNetForImageClassification,
      FocalNetForMaskedImageModeling,
      FocalNetModel,
  )
  from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     int				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     Optional[int]=13				,     __lowerCamelCase     :     Tuple=32				,     __lowerCamelCase     :     str=2				,     __lowerCamelCase     :     Tuple=3				,     __lowerCamelCase     :     str=16				,     __lowerCamelCase     :     Union[str, Any]=[32, 64, 128]				,     __lowerCamelCase     :     Dict=[1, 2, 1]				,     __lowerCamelCase     :     List[Any]=[2, 2, 4]				,     __lowerCamelCase     :     Dict=2				,     __lowerCamelCase     :     List[Any]=2.0				,     __lowerCamelCase     :     Dict=True				,     __lowerCamelCase     :     Any=0.0				,     __lowerCamelCase     :     int=0.0				,     __lowerCamelCase     :     Optional[Any]=0.1				,     __lowerCamelCase     :     List[Any]="gelu"				,     __lowerCamelCase     :     str=False				,     __lowerCamelCase     :     Optional[int]=True				,     __lowerCamelCase     :     str=0.0_2				,     __lowerCamelCase     :     Tuple=1E-5				,     __lowerCamelCase     :     Tuple=True				,     __lowerCamelCase     :     Any=None				,     __lowerCamelCase     :     Optional[int]=True				,     __lowerCamelCase     :     Optional[Any]=10				,     __lowerCamelCase     :     Tuple=8				,     __lowerCamelCase     :     str=["stage1", "stage2"]				,     __lowerCamelCase     :     Optional[int]=[1, 2]				,     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     parent
     lowerCamelCase__  =     batch_size
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     patch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     embed_dim
     lowerCamelCase__  =     hidden_sizes
     lowerCamelCase__  =     depths
     lowerCamelCase__  =     num_heads
     lowerCamelCase__  =     window_size
     lowerCamelCase__  =     mlp_ratio
     lowerCamelCase__  =     qkv_bias
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     drop_path_rate
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     use_absolute_embeddings
     lowerCamelCase__  =     patch_norm
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     is_training
     lowerCamelCase__  =     scope
     lowerCamelCase__  =     use_labels
     lowerCamelCase__  =     type_sequence_label_size
     lowerCamelCase__  =     encoder_stride
     lowerCamelCase__  =     out_features
     lowerCamelCase__  =     out_indices

    def    a__				(					self     :     Tuple     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

     lowerCamelCase__  =     None
     if self.use_labels:
      lowerCamelCase__  =     ids_tensor([self.batch_size]				,     self.type_sequence_label_size     )

     lowerCamelCase__  =     self.get_config()

     return config, pixel_values, labels

    def    a__				(					self     :     Dict     )							->							Optional[Any]:

     '''simple docstring'''




     return FocalNetConfig(
         image_size=self.image_size				,     patch_size=self.patch_size				,     num_channels=self.num_channels				,     embed_dim=self.embed_dim				,     hidden_sizes=self.hidden_sizes				,     depths=self.depths				,     num_heads=self.num_heads				,     window_size=self.window_size				,     mlp_ratio=self.mlp_ratio				,     qkv_bias=self.qkv_bias				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     drop_path_rate=self.drop_path_rate				,     hidden_act=self.hidden_act				,     use_absolute_embeddings=self.use_absolute_embeddings				,     path_norm=self.patch_norm				,     layer_norm_eps=self.layer_norm_eps				,     initializer_range=self.initializer_range				,     encoder_stride=self.encoder_stride				,     out_features=self.out_features				,     out_indices=self.out_indices				,     )

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     FocalNetModel(config=__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase     )

     lowerCamelCase__  =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
     lowerCamelCase__  =     int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

     self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, expected_seq_len, expected_dim)     )

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Optional[int]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     FocalNetBackbone(config=__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase     )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps     )				,     len(config.out_features     )     )
     self.parent.assertListEqual(list(result.feature_maps[0].shape     )				,     [self.batch_size, self.image_size, 8, 8]     )

     # verify channels
     self.parent.assertEqual(len(model.channels     )				,     len(config.out_features     )     )
     self.parent.assertListEqual(model.channels				,     config.hidden_sizes[:-1]     )

     # verify backbone works with out_features=None
     lowerCamelCase__  =     None
     lowerCamelCase__  =     FocalNetBackbone(config=__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase     )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps     )				,     1     )
     self.parent.assertListEqual(list(result.feature_maps[0].shape     )				,     [self.batch_size, self.image_size * 2, 4, 4]     )

     # verify channels
     self.parent.assertEqual(len(model.channels     )				,     1     )
     self.parent.assertListEqual(model.channels				,     [config.hidden_sizes[-1]]     )

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     List[Any]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     FocalNetForMaskedImageModeling(config=__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase     )
     self.parent.assertEqual(
         result.reconstruction.shape				,     (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

     # test greyscale images
     lowerCamelCase__  =     1
     lowerCamelCase__  =     FocalNetForMaskedImageModeling(__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()

     lowerCamelCase__  =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
     lowerCamelCase__  =     model(__lowerCamelCase     )
     self.parent.assertEqual(result.reconstruction.shape				,     (self.batch_size, 1, self.image_size, self.image_size)     )

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Dict     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.type_sequence_label_size
     lowerCamelCase__  =     FocalNetForImageClassification(__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase				,     labels=__lowerCamelCase     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)     )

     # test greyscale images
     lowerCamelCase__  =     1
     lowerCamelCase__  =     FocalNetForImageClassification(__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()

     lowerCamelCase__  =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
     lowerCamelCase__  =     model(__lowerCamelCase     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)     )




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

     '''simple docstring'''




     lowerCamelCase__  =     self.prepare_config_and_inputs()

     lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     config_and_inputs
     lowerCamelCase__  =     {"pixel_values": pixel_values}
     return config, inputs_dict
@require_torch
class 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase__      				=							(
        {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    lowerCAmelCase__      				=							False

    lowerCAmelCase__      				=							False
    lowerCAmelCase__      				=							False
    lowerCAmelCase__      				=							False
    lowerCAmelCase__      				=							False

    def    a__				(					self     :     Dict     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     FocalNetModelTester(self     )
     lowerCamelCase__  =     ConfigTester(self				,     config_class=__lowerCamelCase				,     embed_dim=37				,     has_text_modality=__lowerCamelCase     )

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

     '''simple docstring'''




     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()

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

     '''simple docstring'''




     return

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

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase     )

    def    a__				(					self     :     Any     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*__lowerCamelCase     )

    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase     )

    def    a__				(					self     :     int     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase     )

    @unittest.skip(reason="FocalNet does not use inputs_embeds"     )
    def    a__				(					self     :     Union[str, Any]     )							->							int:

     '''simple docstring'''




     pass

    @unittest.skip(reason="FocalNet does not use feedforward chunking"     )
    def    a__				(					self     :     Dict     )							->							Optional[int]:

     '''simple docstring'''




     pass

    def    a__				(					self     :     Any     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
      lowerCamelCase__  =     model_class(__lowerCamelCase     )
      self.assertIsInstance(model.get_input_embeddings()				,     (nn.Module)     )
      lowerCamelCase__  =     model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(__lowerCamelCase				,     nn.Linear     )     )

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

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
      lowerCamelCase__  =     model_class(__lowerCamelCase     )
      lowerCamelCase__  =     inspect.signature(model.forward     )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      lowerCamelCase__  =     [*signature.parameters.keys()]

      lowerCamelCase__  =     ["pixel_values"]
      self.assertListEqual(arg_names[:1]				,     __lowerCamelCase     )

    def    a__				(					self     :     str				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     List[Any]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     model_class(__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()

     with torch.no_grad():
      lowerCamelCase__  =     model(**self._prepare_for_class(__lowerCamelCase				,     __lowerCamelCase     )     )

     lowerCamelCase__  =     outputs.hidden_states

     lowerCamelCase__  =     getattr(
         self.model_tester				,     "expected_num_hidden_layers"				,     len(self.model_tester.depths     ) + 1     )
     self.assertEqual(len(__lowerCamelCase     )				,     __lowerCamelCase     )

     # FocalNet has a different seq_length
     lowerCamelCase__  =     (
         config.patch_size
         if isinstance(config.patch_size				,     collections.abc.Iterable     )
         else (config.patch_size, config.patch_size)
     )

     lowerCamelCase__  =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]     )				,     [num_patches, self.model_tester.embed_dim]				,     )

     lowerCamelCase__  =     outputs.reshaped_hidden_states
     self.assertEqual(len(__lowerCamelCase     )				,     __lowerCamelCase     )

     lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     reshaped_hidden_states[0].shape
     lowerCamelCase__  =     (
         reshaped_hidden_states[0].view(__lowerCamelCase				,     __lowerCamelCase				,     height * width     ).permute(0				,     2				,     1     )
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:]     )				,     [num_patches, self.model_tester.embed_dim]				,     )

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

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()

     lowerCamelCase__  =     (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size				,     collections.abc.Iterable     )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes[:-1]:
      lowerCamelCase__  =     True
      self.check_hidden_states_output(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      lowerCamelCase__  =     True

      self.check_hidden_states_output(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )

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

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()
     lowerCamelCase__  =     3

     lowerCamelCase__  =     (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size				,     collections.abc.Iterable     )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )
     lowerCamelCase__  =     (
         config.patch_size
         if isinstance(config.patch_size				,     collections.abc.Iterable     )
         else (config.patch_size, config.patch_size)
     )

     lowerCamelCase__  =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     lowerCamelCase__  =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes[:-1]:
      lowerCamelCase__  =     True
      self.check_hidden_states_output(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     (padded_height, padded_width)     )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      lowerCamelCase__  =     True
      self.check_hidden_states_output(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     (padded_height, padded_width)     )

    @slow
    def    a__				(					self     :     int     )							->							Optional[int]:

     '''simple docstring'''




     for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      lowerCamelCase__  =     FocalNetModel.from_pretrained(__lowerCamelCase     )
      self.assertIsNotNone(__lowerCamelCase     )




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

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()

     lowerCamelCase__  =     _config_zero_init(__lowerCamelCase     )
     for model_class in self.all_model_classes:
      lowerCamelCase__  =     model_class(config=__lowerCamelCase     )
      for name, param in model.named_parameters():
       if "embeddings" not in name and param.requires_grad:
        self.assertIn(
            ((param.data.mean() * 1E9).round() / 1E9).item()				,     [0.0, 1.0]				,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''				,     )
@require_vision
@require_torch
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    @cached_property
    def    a__				(					self     :     Optional[int]     )							->							Tuple:

     '''simple docstring'''




     return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"     ) if is_vision_available() else None




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

     '''simple docstring'''




     lowerCamelCase__  =     FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     self.default_image_processor

     lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
     lowerCamelCase__  =     image_processor(images=__lowerCamelCase				,     return_tensors="pt"     ).to(__lowerCamelCase     )

     # forward pass
     with torch.no_grad():
      lowerCamelCase__  =     model(**__lowerCamelCase     )

     # verify the logits
     lowerCamelCase__  =     torch.Size((1, 1000)     )
     self.assertEqual(outputs.logits.shape				,     __lowerCamelCase     )
     lowerCamelCase__  =     torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]     ).to(__lowerCamelCase     )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     __lowerCamelCase				,     atol=1E-4     )     )
     self.assertTrue(outputs.logits.argmax(dim=-1     ).item()				,     281     )
@require_torch
class 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							(FocalNetBackbone,) if is_torch_available() else ()
    lowerCAmelCase__      				=							FocalNetConfig

    lowerCAmelCase__      				=							False

    def    a__				(					self     :     str     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     FocalNetModelTester(self     )









'''simple docstring'''





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"char"
    lowerCAmelCase__      				=							"bpe"
    lowerCAmelCase__      				=							"wp"
__A							:							Any  							=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["image_processor", "char_tokenizer"]
    lowerCAmelCase__      				=							"ViTImageProcessor"
    lowerCAmelCase__      				=							"MgpstrTokenizer"

    def __init__(					self     :     Any				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     List[str]=None				,     **__lowerCamelCase     :     Any     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,     __lowerCamelCase				,     )
      lowerCamelCase__  =     kwargs.pop("feature_extractor"     )

     lowerCamelCase__  =     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."     )
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."     )

     lowerCamelCase__  =     tokenizer
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("gpt2"     )
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("bert-base-uncased"     )

     super().__init__(__lowerCamelCase				,     __lowerCamelCase     )

    def __call__(					self     :     List[str]				,     __lowerCamelCase     :     Union[str, Any]=None				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     str=None				,     **__lowerCamelCase     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     if images is None and text is None:
      raise ValueError("You need to specify either an `images` or `text` input to process."     )

     if images is not None:
      lowerCamelCase__  =     self.image_processor(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )
     if text is not None:
      lowerCamelCase__  =     self.char_tokenizer(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )

     if text is None:
      return inputs
     elif images is None:
      return encodings
     else:
      lowerCamelCase__  =     encodings["input_ids"]
      return inputs

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     sequences
     lowerCamelCase__  =     char_preds.size(0     )

     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "char"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "bpe"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "wp"     )

     lowerCamelCase__  =     []
     lowerCamelCase__  =     []
     for i in range(__lowerCamelCase     ):
      lowerCamelCase__  =     [char_scores[i], bpe_scores[i], wp_scores[i]]
      lowerCamelCase__  =     [char_strs[i], bpe_strs[i], wp_strs[i]]
      lowerCamelCase__  =     scores.index(max(__lowerCamelCase     )     )
      final_strs.append(strs[max_score_index]     )
      final_scores.append(scores[max_score_index]     )

     lowerCamelCase__  =     {}
     lowerCamelCase__  =     final_strs
     lowerCamelCase__  =     final_scores
     lowerCamelCase__  =     char_strs
     lowerCamelCase__  =     bpe_strs
     lowerCamelCase__  =     wp_strs
     return out

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     if format == DecodeType.CHARACTER:
      lowerCamelCase__  =     self.char_decode
      lowerCamelCase__  =     1
      lowerCamelCase__  =     "[s]"
     elif format == DecodeType.BPE:
      lowerCamelCase__  =     self.bpe_decode
      lowerCamelCase__  =     2
      lowerCamelCase__  =     "#"
     elif format == DecodeType.WORDPIECE:
      lowerCamelCase__  =     self.wp_decode
      lowerCamelCase__  =     102
      lowerCamelCase__  =     "[SEP]"
     else:
      raise ValueError(f'''Format {format} is not supported.'''     )

     lowerCamelCase__  ,	lowerCamelCase__  =     [], []
     lowerCamelCase__  =     pred_logits.size(0     )
     lowerCamelCase__  =     pred_logits.size(1     )
     lowerCamelCase__  ,	lowerCamelCase__  =     pred_logits.topk(1				,     dim=-1				,     largest=__lowerCamelCase				,     sorted=__lowerCamelCase     )
     lowerCamelCase__  =     preds_index.view(-1				,     __lowerCamelCase     )[:, 1:]
     lowerCamelCase__  =     decoder(__lowerCamelCase     )
     lowerCamelCase__  ,	lowerCamelCase__  =     torch.nn.functional.softmax(__lowerCamelCase				,     dim=2     ).max(dim=2     )
     lowerCamelCase__  =     preds_max_prob[:, 1:]

     for index in range(__lowerCamelCase     ):
      lowerCamelCase__  =     preds_str[index].find(__lowerCamelCase     )
      lowerCamelCase__  =     preds_str[index][:pred_eos]
      lowerCamelCase__  =     preds_index[index].cpu().tolist()
      lowerCamelCase__  =     pred_index.index(__lowerCamelCase     ) if eos_token in pred_index else -1
      lowerCamelCase__  =     preds_max_prob[index][: pred_eos_index + 1]
      lowerCamelCase__  =     pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
      dec_strs.append(__lowerCamelCase     )
      conf_scores.append(__lowerCamelCase     )

     return dec_strs, conf_scores

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     return self.bpe_tokenizer.batch_decode(__lowerCamelCase     )




    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

