






import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
       MODEL_MAPPING,
       DataaVecVisionForImageClassification,
       DataaVecVisionForSemanticSegmentation,
       DataaVecVisionModel,
   )
   from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor


class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=100							, _UpperCAmelCase=13							, _UpperCAmelCase=30							, _UpperCAmelCase=2							, _UpperCAmelCase=3							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=32							, _UpperCAmelCase=4							, _UpperCAmelCase=4							, _UpperCAmelCase=37							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.1							, _UpperCAmelCase=0.1							, _UpperCAmelCase=10							, _UpperCAmelCase=0.02							, _UpperCAmelCase=3							, _UpperCAmelCase=None							, _UpperCAmelCase=[0, 1, 2, 3]							, )		->				Dict:
        lowerCamelCase									=parent
        lowerCamelCase									=100
        lowerCamelCase									=batch_size
        lowerCamelCase									=image_size
        lowerCamelCase									=patch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=is_training
        lowerCamelCase									=use_labels
        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=type_sequence_label_size
        lowerCamelCase									=initializer_range
        lowerCamelCase									=scope
        lowerCamelCase									=out_indices
        lowerCamelCase									=num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(image_size // patch_size) ** 2
        lowerCamelCase									=num_patches + 1





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        lowerCamelCase									=None
        lowerCamelCase									=None
        if self.use_labels:
           lowerCamelCase									=ids_tensor([self.batch_size]							, self.type_sequence_label_size  )
           lowerCamelCase									=ids_tensor([self.batch_size, self.image_size, self.image_size]							, self.num_labels  )

        lowerCamelCase									=self.get_config()

        return config, pixel_values, labels, pixel_labels





     def 						lowerCamelCase							(	self  )		->				str:
        return DataaVecVisionConfig(
            vocab_size=self.vocab_size							, image_size=self.image_size							, patch_size=self.patch_size							, num_channels=self.num_channels							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, is_decoder=_UpperCAmelCase							, initializer_range=self.initializer_range							, out_indices=self.out_indices							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=DataaVecVisionModel(config=_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, num_patches + 1, self.hidden_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Any:
        lowerCamelCase									=self.type_sequence_label_size
        lowerCamelCase									=DataaVecVisionForImageClassification(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        lowerCamelCase									=self.num_labels
        lowerCamelCase									=DataaVecVisionForSemanticSegmentation(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=self.prepare_config_and_inputs()
        lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=config_and_inputs
        lowerCamelCase									={"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
class 		lowerCAmelCase_				(  UpperCamelCase				,     UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[int] 	=     (
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __lowerCamelCase       :    str 	=     (
         {
             '''feature-extraction''': DataaVecVisionModel,
             '''image-classification''': DataaVecVisionForImageClassification,
             '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __lowerCamelCase       :    Any 	=     False
     __lowerCamelCase       :    List[str] 	=     False
     __lowerCamelCase       :    int 	=     False





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=DataaVecVisionModelTester(self  )
        lowerCamelCase									=ConfigTester(
            self							, config_class=_UpperCAmelCase							, has_text_modality=_UpperCAmelCase							, hidden_size=37  )





     def 						lowerCamelCase							(	self  )		->				int:
        self.config_tester.run_common_tests()





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Data2VecVision does not use inputs_embeds
        pass





     @require_torch_multi_gpu
     @unittest.skip(
         reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
     def 						lowerCamelCase							(	self  )		->				Dict:
        pass





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           self.assertIsInstance(model.get_input_embeddings()							, (nn.Module)  )
           lowerCamelCase									=model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_UpperCAmelCase							, nn.Linear  )  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           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]							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        if not self.model_tester.is_training:
           return

        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )]:
              continue

           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
           return

        lowerCamelCase									=False
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )] or not model_class.supports_gradient_checkpointing:
              continue
           # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
           # this can then be incorporated into _prepare_for_class in test_modeling_common.py
           elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
              lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=inputs_dict["""pixel_values"""].shape
              lowerCamelCase									=torch.zeros(
                  [self.model_tester.batch_size, height, width]							, device=_UpperCAmelCase  ).long()
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.gradient_checkpointing_enable()
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        lowerCamelCase									=_config_zero_init(_UpperCAmelCase  )
        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(config=_UpperCAmelCase  )
           for name, param in model.named_parameters():
              # we skip lambda parameters as these require special initial values
              # determined by config.layer_scale_init_value
              if "lambda" in name:
                 continue
              if 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'''							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase=2E-4							, _UpperCAmelCase="outputs"							, _UpperCAmelCase=None  )		->				int:
        # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
        super().check_pt_tf_outputs(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCamelCase									=DataaVecVisionModel.from_pretrained(_UpperCAmelCase  )
           self.assertIsNotNone(_UpperCAmelCase  )






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
   return image







@require_torch
@require_vision
class 		lowerCAmelCase_				(  unittest.TestCase ):





     @cached_property
     def 						lowerCamelCase							(	self  )		->				List[str]:
        return (
            BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ) if is_vision_available() else None
        )





     @slow
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ).to(
            _UpperCAmelCase  )

        lowerCamelCase									=self.default_image_processor
        lowerCamelCase									=prepare_img()
        lowerCamelCase									=image_processor(images=_UpperCAmelCase							, return_tensors="""pt"""  ).to(_UpperCAmelCase  )

        # forward pass
        with torch.no_grad():
           lowerCamelCase									=model(**_UpperCAmelCase  )
        lowerCamelCase									=outputs.logits

        # verify the logits
        lowerCamelCase									=torch.Size((1, 1000)  )
        self.assertEqual(logits.shape							, _UpperCAmelCase  )

        lowerCamelCase									=torch.tensor([0.3277, -0.1395, 0.0911]  ).to(_UpperCAmelCase  )

        self.assertTrue(torch.allclose(logits[0, :3]							, _UpperCAmelCase							, atol=1E-4  )  )

        lowerCamelCase									=[model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
        self.assertEqual(logits[0].topk(2  ).indices.cpu().tolist()							, _UpperCAmelCase  )







import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase					:						Dict         =     logging.get_logger(__name__)

_UpperCAmelCase					:						int         =     {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

_UpperCAmelCase					:						Optional[int]         =     {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

_UpperCAmelCase					:						Optional[int]         =     {'stas/tiny-wmt19-en-de': 1024}
_UpperCAmelCase					:						List[Any]         =     {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1024,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}






def  UpperCAmelCase__						(    lowerCamelCase	:			Dict    ):
   lowerCamelCase									=set()
   lowerCamelCase									=word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)    )
      lowerCamelCase									=char
   return pairs






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[Any]    ):
   lowerCamelCase									=text.replace("""，"""			,""","""    )
   lowerCamelCase									=re.sub(R"""。\s*"""			,""". """			,lowerCamelCase    )
   lowerCamelCase									=text.replace("""、"""			,""","""    )
   lowerCamelCase									=text.replace("""”"""			,"""\""""    )
   lowerCamelCase									=text.replace("""“"""			,"""\""""    )
   lowerCamelCase									=text.replace("""∶"""			,""":"""    )
   lowerCamelCase									=text.replace("""："""			,""":"""    )
   lowerCamelCase									=text.replace("""？"""			,"""?"""    )
   lowerCamelCase									=text.replace("""《"""			,"""\""""    )
   lowerCamelCase									=text.replace("""》"""			,"""\""""    )
   lowerCamelCase									=text.replace("""）"""			,""")"""    )
   lowerCamelCase									=text.replace("""！"""			,"""!"""    )
   lowerCamelCase									=text.replace("""（"""			,"""("""    )
   lowerCamelCase									=text.replace("""；"""			,""";"""    )
   lowerCamelCase									=text.replace("""１"""			,"""1"""    )
   lowerCamelCase									=text.replace("""」"""			,"""\""""    )
   lowerCamelCase									=text.replace("""「"""			,"""\""""    )
   lowerCamelCase									=text.replace("""０"""			,"""0"""    )
   lowerCamelCase									=text.replace("""３"""			,"""3"""    )
   lowerCamelCase									=text.replace("""２"""			,"""2"""    )
   lowerCamelCase									=text.replace("""５"""			,"""5"""    )
   lowerCamelCase									=text.replace("""６"""			,"""6"""    )
   lowerCamelCase									=text.replace("""９"""			,"""9"""    )
   lowerCamelCase									=text.replace("""７"""			,"""7"""    )
   lowerCamelCase									=text.replace("""８"""			,"""8"""    )
   lowerCamelCase									=text.replace("""４"""			,"""4"""    )
   lowerCamelCase									=re.sub(R"""．\s*"""			,""". """			,lowerCamelCase    )
   lowerCamelCase									=text.replace("""～"""			,"""~"""    )
   lowerCamelCase									=text.replace("""’"""			,"""'"""    )
   lowerCamelCase									=text.replace("""…"""			,"""..."""    )
   lowerCamelCase									=text.replace("""━"""			,"""-"""    )
   lowerCamelCase									=text.replace("""〈"""			,"""<"""    )
   lowerCamelCase									=text.replace("""〉"""			,""">"""    )
   lowerCamelCase									=text.replace("""【"""			,"""["""    )
   lowerCamelCase									=text.replace("""】"""			,"""]"""    )
   lowerCamelCase									=text.replace("""％"""			,"""%"""    )
   return text






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]    ):
   lowerCamelCase									=[]
   for char in text:
      lowerCamelCase									=unicodedata.category(lowerCamelCase    )
      if cat.startswith("""C"""    ):
         continue
      output.append(lowerCamelCase    )
   return "".join(lowerCamelCase    )







class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Optional[int] 	=     VOCAB_FILES_NAMES
     __lowerCamelCase       :    List[Any] 	=     PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase       :    List[Any] 	=     PRETRAINED_INIT_CONFIGURATION
     __lowerCamelCase       :    Tuple 	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase       :    Optional[int] 	=     ['''input_ids''', '''attention_mask''']





     def __init__(	self							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=False							, _UpperCAmelCase="<unk>"							, _UpperCAmelCase="<s>"							, _UpperCAmelCase="</s>"							, _UpperCAmelCase="<pad>"							, **_UpperCAmelCase							, )		->				Tuple:
        super().__init__(
            langs=_UpperCAmelCase							, src_vocab_file=_UpperCAmelCase							, tgt_vocab_file=_UpperCAmelCase							, merges_file=_UpperCAmelCase							, do_lower_case=_UpperCAmelCase							, unk_token=_UpperCAmelCase							, bos_token=_UpperCAmelCase							, sep_token=_UpperCAmelCase							, pad_token=_UpperCAmelCase							, **_UpperCAmelCase							, )

        try:
           import sacremoses
        except ImportError:
           raise ImportError(
               """You need to install sacremoses to use XLMTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""  )

        lowerCamelCase									=sacremoses

        lowerCamelCase									=src_vocab_file
        lowerCamelCase									=tgt_vocab_file
        lowerCamelCase									=merges_file
        lowerCamelCase									=do_lower_case

        # cache of sm.MosesPunctNormalizer instance
        lowerCamelCase									={}
        # cache of sm.MosesTokenizer instance
        lowerCamelCase									={}
        lowerCamelCase									={}

        if langs and len(_UpperCAmelCase  ) == 2:
           lowerCamelCase				,       lowerCamelCase									=langs
        else:
           raise ValueError(
               F'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
               """Usually that means that tokenizer can't find a mapping for the given model path """
               """in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."""  )

        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as src_vocab_handle:
           lowerCamelCase									=json.load(_UpperCAmelCase  )
        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as tgt_vocab_handle:
           lowerCamelCase									=json.load(_UpperCAmelCase  )
           lowerCamelCase									={v: k for k, v in tgt_vocab.items()}
        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as merges_handle:
           lowerCamelCase									=merges_handle.read().split("""\n"""  )[:-1]
        lowerCamelCase									=[tuple(merge.split()[:2]  ) for merge in merges]
        lowerCamelCase									=dict(zip(_UpperCAmelCase							, range(len(_UpperCAmelCase  )  )  )  )
        lowerCamelCase									={}





     def 						lowerCamelCase							(	self  )		->				Dict[str, int]:
        return self.get_src_vocab()





     @property
     def 						lowerCamelCase							(	self  )		->				int:
        return self.src_vocab_size





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				int:
        if lang not in self.cache_moses_punct_normalizer:
           lowerCamelCase									=self.sm.MosesPunctNormalizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=punct_normalizer
        return self.cache_moses_punct_normalizer[lang].normalize(_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        if lang not in self.cache_moses_tokenizer:
           lowerCamelCase									=self.sm.MosesTokenizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            _UpperCAmelCase							, aggressive_dash_splits=_UpperCAmelCase							, return_str=_UpperCAmelCase							, escape=_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Dict:
        if lang not in self.cache_moses_detokenizer:
           lowerCamelCase									=self.sm.MosesDetokenizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				List[str]:
        lowerCamelCase									=replace_unicode_punct(_UpperCAmelCase  )
        lowerCamelCase									=self.moses_punct_norm(_UpperCAmelCase							, _UpperCAmelCase  )
        lowerCamelCase									=remove_non_printing_char(_UpperCAmelCase  )
        return text





     @property
     def 						lowerCamelCase							(	self  )		->				Dict:
        return len(self.encoder  )





     @property
     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        return len(self.decoder  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return dict(self.encoder							, **self.added_tokens_encoder  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        return dict(self.decoder							, **self.added_tokens_decoder  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=tuple(token[:-1]  ) + (token[-1] + """</w>""",)
        if token in self.cache:
           return self.cache[token]
        lowerCamelCase									=get_pairs(_UpperCAmelCase  )

        if not pairs:
           return token + "</w>"

        while True:
           lowerCamelCase									=min(_UpperCAmelCase							, key=lambda _UpperCAmelCase  : self.bpe_ranks.get(_UpperCAmelCase							, float("""inf"""  )  )  )
           if bigram not in self.bpe_ranks:
              break
           lowerCamelCase				,       lowerCamelCase									=bigram
           lowerCamelCase									=[]
           lowerCamelCase									=0
           while i < len(_UpperCAmelCase  ):
              try:
                 lowerCamelCase									=word.index(_UpperCAmelCase							, _UpperCAmelCase  )
              except ValueError:
                 new_word.extend(word[i:]  )
                 break
              else:
                 new_word.extend(word[i:j]  )
                 lowerCamelCase									=j

              if word[i] == first and i < len(_UpperCAmelCase  ) - 1 and word[i + 1] == second:
                 new_word.append(first + second  )
                 i += 2
              else:
                 new_word.append(word[i]  )
                 i += 1
           lowerCamelCase									=tuple(_UpperCAmelCase  )
           lowerCamelCase									=new_word
           if len(_UpperCAmelCase  ) == 1:
              break
           else:
              lowerCamelCase									=get_pairs(_UpperCAmelCase  )
        lowerCamelCase									=""" """.join(_UpperCAmelCase  )
        if word == "\n  </w>":
           lowerCamelCase									="""\n</w>"""
        lowerCamelCase									=word
        return word





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase="en"							, _UpperCAmelCase=False  )		->				str:
        lowerCamelCase									=self.src_lang

        if self.do_lower_case:
           lowerCamelCase									=text.lower()

        if bypass_tokenizer:
           lowerCamelCase									=text.split()
        else:
           lowerCamelCase									=self.moses_pipeline(_UpperCAmelCase							, lang=_UpperCAmelCase  )
           lowerCamelCase									=self.moses_tokenize(_UpperCAmelCase							, lang=_UpperCAmelCase  )

        lowerCamelCase									=[]
        for token in text:
           if token:
              split_tokens.extend(list(self.bpe(_UpperCAmelCase  ).split(""" """  )  )  )

        return split_tokens





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				List[str]:
        return self.encoder.get(_UpperCAmelCase							, self.encoder.get(self.unk_token  )  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Tuple:
        return self.decoder.get(_UpperCAmelCase							, self.unk_token  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				List[Any]:
        lowerCamelCase									=[t.replace(""" """							, """"""  ).replace("""</w>"""							, """ """  ) for t in tokens]
        lowerCamelCase									="""""".join(_UpperCAmelCase  ).split()
        # detokenize
        lowerCamelCase									=self.moses_detokenize(_UpperCAmelCase							, self.tgt_lang  )
        return text





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				List[int]:
        lowerCamelCase									=[self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
           return token_ids_a + sep
        return token_ids_a + sep + token_ids_a + sep





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None							, _UpperCAmelCase = False  )		->				List[int]:

        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase							, token_ids_a=_UpperCAmelCase							, already_has_special_tokens=_UpperCAmelCase  )
        # no bos used in fairseq
        if token_ids_a is not None:
           return ([0] * len(_UpperCAmelCase  )) + [1] + ([0] * len(_UpperCAmelCase  )) + [1]
        return ([0] * len(_UpperCAmelCase  )) + [1]





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				List[int]:
        lowerCamelCase									=[self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
           return len(token_ids_a + sep  ) * [0]
        return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				Tuple[str]:
        if not os.path.isdir(_UpperCAmelCase  ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
           return

        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""src_vocab_file"""]  )
        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""tgt_vocab_file"""]  )
        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as f:
           f.write(json.dumps(self.encoder							, indent=2							, sort_keys=_UpperCAmelCase							, ensure_ascii=_UpperCAmelCase  ) + """\n"""  )

        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as f:
           lowerCamelCase									={v: k for k, v in self.decoder.items()}
           f.write(json.dumps(_UpperCAmelCase							, indent=2							, sort_keys=_UpperCAmelCase							, ensure_ascii=_UpperCAmelCase  ) + """\n"""  )

        lowerCamelCase									=0
        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as writer:
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							, key=lambda _UpperCAmelCase  : kv[1]  ):
              if index != token_index:
                 logger.warning(
                     F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                     """ Please check that the tokenizer is not corrupted!"""  )
                 lowerCamelCase									=token_index
              writer.write(""" """.join(_UpperCAmelCase  ) + """\n"""  )
              index += 1

        return src_vocab_file, tgt_vocab_file, merges_file





     def __getstate__(	self  )		->				List[Any]:
        lowerCamelCase									=self.__dict__.copy()
        lowerCamelCase									=None
        return state





     def __setstate__(	self							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=d

        try:
           import sacremoses
        except ImportError:
           raise ImportError(
               """You need to install sacremoses to use XLMTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""  )

        lowerCamelCase									=sacremoses