'''simple docstring'''




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image






class 		__A       :






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_=2				,     UpperCamelCase_=True				,     UpperCamelCase_=False				,     UpperCamelCase_=10				,     UpperCamelCase_=3				,     UpperCamelCase_=32 * 8				,     UpperCamelCase_=32 * 8				,     UpperCamelCase_=4				,     UpperCamelCase_=64				,     ):
              __UpperCAmelCase  :				List[Any]       				=		parent
              __UpperCAmelCase  :				Optional[Any]       				=		batch_size
              __UpperCAmelCase  :				Dict       				=		is_training
              __UpperCAmelCase  :				str       				=		use_auxiliary_loss
              __UpperCAmelCase  :				Optional[int]       				=		num_queries
              __UpperCAmelCase  :				Union[str, Any]       				=		num_channels
              __UpperCAmelCase  :				Optional[Any]       				=		min_size
              __UpperCAmelCase  :				Union[str, Any]       				=		max_size
              __UpperCAmelCase  :				int       				=		num_labels
              __UpperCAmelCase  :				Dict       				=		hidden_dim
              __UpperCAmelCase  :				Optional[int]       				=		hidden_dim






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
                  _a      )

              __UpperCAmelCase  :				List[Any]       				=		torch.ones([self.batch_size, self.min_size, self.max_size]				,     device=_a      )

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

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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		MaskaFormerConfig(
                  hidden_size=self.hidden_dim				,     )
              __UpperCAmelCase  :				Optional[Any]       				=		self.num_queries
              __UpperCAmelCase  :				Any       				=		self.num_labels

              __UpperCAmelCase  :				Dict       				=		[1, 1, 1, 1]
              __UpperCAmelCase  :				str       				=		self.num_channels

              __UpperCAmelCase  :				List[Any]       				=		64
              __UpperCAmelCase  :				Optional[Any]       				=		1_28
              __UpperCAmelCase  :				List[str]       				=		self.hidden_dim
              __UpperCAmelCase  :				str       				=		self.hidden_dim
              __UpperCAmelCase  :				List[Any]       				=		self.hidden_dim
              return config






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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		output.encoder_hidden_states
              __UpperCAmelCase  :				Tuple       				=		output.pixel_decoder_hidden_states
              __UpperCAmelCase  :				str       				=		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_layers      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=False      ):




              with torch.no_grad():
                     __UpperCAmelCase  :				Optional[Any]       				=		MaskaFormerModel(config=_a      )
                     model.to(_a      )
                     model.eval()

                     __UpperCAmelCase  :				Dict       				=		model(pixel_values=_a				,     pixel_mask=_a      )
                     __UpperCAmelCase  :				str       				=		model(_a				,     output_hidden_states=_a      )

              self.parent.assertEqual(
                  output.transformer_decoder_last_hidden_state.shape				,     (self.batch_size, self.num_queries, self.hidden_dim)				,     )
              # let's ensure the other two hidden state exists
              self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None      )
              self.parent.assertTrue(output.encoder_last_hidden_state is not None      )

              if output_hidden_states:
                     self.check_output_hidden_state(_a				,     _a      )







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		MaskaFormerForUniversalSegmentation(config=_a      )
              model.to(_a      )
              model.eval()

              def comm_check_on_output(UpperCamelCase_      ):
                     # 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  :				Union[str, Any]       				=		model(pixel_values=_a				,     pixel_mask=_a      )
                     __UpperCAmelCase  :				Dict       				=		model(_a      )

                     comm_check_on_output(_a      )

                     __UpperCAmelCase  :				Union[str, 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 		__A       (__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					unittest.TestCase   ):
       snake_case       :Dict				    =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       snake_case       :str				    =					{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

       snake_case       :Optional[int]				    =					False
       snake_case       :str				    =					False
       snake_case       :List[str]				    =					False
       snake_case       :Tuple				    =					False






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		MaskaFormerModelTester(self      )
              __UpperCAmelCase  :				Dict       				=		ConfigTester(self				,     config_class=_a				,     has_text_modality=_a      )






       def 			_snake_case						(      self      ):
              self.config_tester.run_common_tests()






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.create_and_check_maskaformer_model(_a				,     **_a				,     output_hidden_states=_a      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_a      )






       @unittest.skip(reason="Mask2Former does not use inputs_embeds"      )
       def 			_snake_case						(      self      ):
              pass






       @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"      )
       def 			_snake_case						(      self      ):
              pass






       @unittest.skip(reason="Mask2Former is not a generative model"      )
       def 			_snake_case						(      self      ):
              pass






       @unittest.skip(reason="Mask2Former does not use token embeddings"      )
       def 			_snake_case						(      self      ):
              pass






       @require_torch_multi_gpu
       @unittest.skip(
           reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"      )
       def 			_snake_case						(      self      ):
              pass






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.model_tester.prepare_config_and_inputs_for_common()

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

                     __UpperCAmelCase  :				Optional[int]       				=		["""pixel_values"""]
                     self.assertListEqual(arg_names[:1]				,     _a      )






       @slow
       def 			_snake_case						(      self      ):




              for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                     __UpperCAmelCase  :				str       				=		MaskaFormerModel.from_pretrained(_a      )
                     self.assertIsNotNone(_a      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		(self.model_tester.min_size,) * 2
              __UpperCAmelCase  :				Any       				=		{
                  """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  :				Optional[Any]       				=		self.model_tester.get_config()

              __UpperCAmelCase  :				List[str]       				=		MaskaFormerForUniversalSegmentation(_a      ).to(_a      )
              __UpperCAmelCase  :				int       				=		model(**_a      )
              self.assertTrue(outputs.loss is not None      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.create_and_check_maskaformer_model(_a				,     **_a				,     output_hidden_states=_a      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     __UpperCAmelCase  :				Dict       				=		model_class(_a      ).to(_a      )
                     __UpperCAmelCase  :				Optional[int]       				=		model(**_a				,     output_attentions=_a      )
                     self.assertTrue(outputs.attentions is not None      )






       def 			_snake_case						(      self      ):




              if not self.model_tester.is_training:
                     return

              __UpperCAmelCase  :				List[Any]       				=		self.all_model_classes[1]
              __UpperCAmelCase  :				int       				=		self.model_tester.prepare_config_and_inputs()

              __UpperCAmelCase  :				Tuple       				=		model_class(_a      )
              model.to(_a      )
              model.train()

              __UpperCAmelCase  :				Dict       				=		model(_a				,     mask_labels=_a				,     class_labels=_a      ).loss
              loss.backward()







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		self.all_model_classes[1]
              __UpperCAmelCase  :				List[str]       				=		self.model_tester.prepare_config_and_inputs()
              __UpperCAmelCase  :				int       				=		True
              __UpperCAmelCase  :				Tuple       				=		True

              __UpperCAmelCase  :				Union[str, Any]       				=		model_class(_a      ).to(_a      )
              model.train()

              __UpperCAmelCase  :				Dict       				=		model(_a				,     mask_labels=_a				,     class_labels=_a      )

              __UpperCAmelCase  :				Optional[int]       				=		outputs.encoder_hidden_states[0]
              encoder_hidden_states.retain_grad()

              __UpperCAmelCase  :				List[Any]       				=		outputs.pixel_decoder_hidden_states[0]
              pixel_decoder_hidden_states.retain_grad()

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

              __UpperCAmelCase  :				List[str]       				=		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      )




_a   :	Optional[int] 							=   1e-4

def _lowercase	(      )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				List[str]       				=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       return image






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






       @cached_property
       def 			_snake_case						(      self      ):
              return "facebook/mask2former-swin-small-coco-instance"






       @cached_property
       def 			_snake_case						(      self      ):
              return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(_a      )
              __UpperCAmelCase  :				Dict       				=		self.default_image_processor
              __UpperCAmelCase  :				Optional[int]       				=		prepare_img()
              __UpperCAmelCase  :				Union[str, Any]       				=		image_processor(_a				,     return_tensors="pt"      ).to(_a      )
              __UpperCAmelCase  :				str       				=		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, 3_84, 3_84)      )

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

              __UpperCAmelCase  :				Tuple       				=		torch.tensor(
                  [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]      ).to(_a      )
              self.assertTrue(
                  torch.allclose(
                      outputs.encoder_last_hidden_state[0, 0, :3, :3]				,     _a				,     atol=_a      )      )

              __UpperCAmelCase  :				Optional[Any]       				=		torch.tensor(
                  [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]      ).to(_a      )
              self.assertTrue(
                  torch.allclose(
                      outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,     _a				,     atol=_a      )      )

              __UpperCAmelCase  :				str       				=		torch.tensor(
                  [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]      ).to(_a      )
              self.assertTrue(
                  torch.allclose(
                      outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,     _a				,     atol=_a      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(_a      ).eval()
              __UpperCAmelCase  :				str       				=		self.default_image_processor
              __UpperCAmelCase  :				str       				=		prepare_img()
              __UpperCAmelCase  :				str       				=		image_processor(_a				,     return_tensors="pt"      ).to(_a      )
              __UpperCAmelCase  :				Optional[Any]       				=		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, 3_84, 3_84)      )

              with torch.no_grad():
                     __UpperCAmelCase  :				str       				=		model(**_a      )
              # masks_queries_logits
              __UpperCAmelCase  :				Dict       				=		outputs.masks_queries_logits
              self.assertEqual(
                  masks_queries_logits.shape				,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
              __UpperCAmelCase  :				List[str]       				=		[
                  [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
                  [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
                  [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
              ]
              __UpperCAmelCase  :				Any       				=		torch.tensor(_a      ).to(_a      )
              self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,     _a				,     atol=_a      )      )
              # class_queries_logits
              __UpperCAmelCase  :				List[str]       				=		outputs.class_queries_logits
              self.assertEqual(class_queries_logits.shape				,     (1, model.config.num_queries, model.config.num_labels + 1)      )
              __UpperCAmelCase  :				Dict       				=		torch.tensor(
                  [
                      [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
                      [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
                      [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
                  ]      ).to(_a      )
              self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,     _a				,     atol=_a      )      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(_a      ).eval()
              __UpperCAmelCase  :				Optional[int]       				=		self.default_image_processor

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

              __UpperCAmelCase  :				Optional[int]       				=		inputs["""pixel_values"""].to(_a      )
              __UpperCAmelCase  :				int       				=		[el.to(_a      ) for el in inputs["""mask_labels"""]]
              __UpperCAmelCase  :				Any       				=		[el.to(_a      ) for el in inputs["""class_labels"""]]

              with torch.no_grad():
                     __UpperCAmelCase  :				Tuple       				=		model(**_a      )

              self.assertTrue(outputs.loss is not None      )







'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a   :	str 							=   logging.get_logger(__name__)

_a   :	Tuple 							=   "▁"

_a   :	Optional[int] 							=   {"vocab_file": "sentencepiece.bpe.model"}

_a   :	Tuple 							=   {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_a   :	Optional[Any] 							=   {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}






class 		__A       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					VOCAB_FILES_NAMES
       snake_case       :Any				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Tuple				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Optional[int]				    =					["input_ids", "attention_mask"]






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="<unk>"				,     UpperCamelCase_="<pad>"				,     UpperCamelCase_="<mask>"				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              # Mask token behave like a normal word, i.e. include the space before it
              __UpperCAmelCase  :				Optional[int]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else mask_token

              __UpperCAmelCase  :				int       				=		{} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  bos_token=UpperCamelCase_				,     eos_token=UpperCamelCase_				,     unk_token=UpperCamelCase_				,     sep_token=UpperCamelCase_				,     cls_token=UpperCamelCase_				,     pad_token=UpperCamelCase_				,     mask_token=UpperCamelCase_				,     sp_model_kwargs=self.sp_model_kwargs				,     **UpperCamelCase_				,     )

              __UpperCAmelCase  :				Optional[int]       				=		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
              self.sp_model.Load(str(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Union[str, Any]       				=		vocab_file

              # Original fairseq vocab and spm vocab must be "aligned":
              # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
              # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
              # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
              # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

              # Mimic fairseq token-to-id alignment for the first 4 token
              __UpperCAmelCase  :				Optional[Any]       				=		{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

              # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
              __UpperCAmelCase  :				List[Any]       				=		1

              __UpperCAmelCase  :				Optional[Any]       				=		len(self.sp_model      ) + self.fairseq_offset
              __UpperCAmelCase  :				str       				=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def __getstate__(      self      ):
              __UpperCAmelCase  :				List[str]       				=		self.__dict__.copy()
              __UpperCAmelCase  :				str       				=		None
              __UpperCAmelCase  :				str       				=		self.sp_model.serialized_model_proto()
              return state






       def __setstate__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		d

              # for backward compatibility
              if not hasattr(self				,     "sp_model_kwargs"      ):
                     __UpperCAmelCase  :				Tuple       				=		{}

              __UpperCAmelCase  :				Dict       				=		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):

              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __UpperCAmelCase  :				List[Any]       				=		[self.cls_token_id]
              __UpperCAmelCase  :				Union[str, Any]       				=		[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False      ):

              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=UpperCamelCase_				,     token_ids_a=UpperCamelCase_				,     already_has_special_tokens=UpperCamelCase_      )

              if token_ids_a is None:
                     return [1] + ([0] * len(UpperCamelCase_      )) + [1]
              return [1] + ([0] * len(UpperCamelCase_      )) + [1, 1] + ([0] * len(UpperCamelCase_      )) + [1]






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				Dict       				=		[self.sep_token_id]
              __UpperCAmelCase  :				List[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]






       @property
       def 			_snake_case						(      self      ):
              return len(self.sp_model      ) + self.fairseq_offset + 1  # Add the <mask> token






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		{self.convert_ids_to_tokens(UpperCamelCase_      ): i for i in range(self.vocab_size      )}
              vocab.update(self.added_tokens_encoder      )
              return vocab






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.sp_model.encode(UpperCamelCase_				,     out_type=UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              __UpperCAmelCase  :				Optional[int]       				=		self.sp_model.PieceToId(UpperCamelCase_      )

              # Need to return unknown token if the SP model returned 0
              return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if index in self.fairseq_ids_to_tokens:
                     return self.fairseq_ids_to_tokens[index]
              return self.sp_model.IdToPiece(index - self.fairseq_offset      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		"".join(UpperCamelCase_      ).replace(UpperCamelCase_				,     " "      ).strip()
              return out_string







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if not os.path.isdir(UpperCamelCase_      ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
                     return
              __UpperCAmelCase  :				List[str]       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

              if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase_      ) and os.path.isfile(self.vocab_file      ):
                     copyfile(self.vocab_file				,     UpperCamelCase_      )
              elif not os.path.isfile(self.vocab_file      ):
                     with open(UpperCamelCase_				,     "wb"      ) as fi:
                            __UpperCAmelCase  :				Optional[int]       				=		self.sp_model.serialized_model_proto()
                            fi.write(UpperCamelCase_      )

              return (out_vocab_file,)






