


import inspect
import unittest

from transformers import MobileViTVaConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       List[str]				    = self.config_class(**self.inputs_dict)
            self.parent.assertTrue(hasattr(__lowerCamelCase ,						"width_multiplier"))







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=1_3 ,						__lowerCamelCase=6_4 ,						__lowerCamelCase=2 ,						__lowerCamelCase=3 ,						__lowerCamelCase="swish" ,						__lowerCamelCase=3 ,						__lowerCamelCase=3_2 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=1_0 ,						__lowerCamelCase=None ,						__lowerCamelCase=0.2_5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						)			->				int:
            _A      :       Optional[Any]				    = parent
            _A      :       List[str]				    = batch_size
            _A      :       Dict				    = image_size
            _A      :       Dict				    = patch_size
            _A      :       Optional[int]				    = num_channels
            _A      :       List[str]				    = make_divisible(5_1_2 * width_multiplier ,						divisor=8)
            _A      :       Any				    = hidden_act
            _A      :       Union[str, Any]				    = conv_kernel_size
            _A      :       Tuple				    = output_stride
            _A      :       List[Any]				    = classifier_dropout_prob
            _A      :       Any				    = use_labels
            _A      :       Union[str, Any]				    = is_training
            _A      :       Union[str, Any]				    = num_labels
            _A      :       str				    = initializer_range
            _A      :       Dict				    = scope
            _A      :       List[str]				    = width_multiplier
            _A      :       str				    = ffn_dropout
            _A      :       List[str]				    = attn_dropout


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

            _A      :       Union[str, Any]				    = None
            _A      :       List[Any]				    = None
            if self.use_labels:
                   _A      :       List[str]				    = ids_tensor([self.batch_size] ,						self.num_labels)
                   _A      :       List[Any]				    = ids_tensor([self.batch_size, self.image_size, self.image_size] ,						self.num_labels)

            _A      :       Optional[Any]				    = self.get_config()

            return config, pixel_values, labels, pixel_labels


     def  _lowerCamelCase (						self)			->				int:
            return MobileViTVaConfig(
                image_size=self.image_size ,						patch_size=self.patch_size ,						num_channels=self.num_channels ,						hidden_act=self.hidden_act ,						conv_kernel_size=self.conv_kernel_size ,						output_stride=self.output_stride ,						classifier_dropout_prob=self.classifier_dropout_prob ,						initializer_range=self.initializer_range ,						width_multiplier=self.width_multiplier ,						ffn_dropout=self.ffn_dropout_prob ,						attn_dropout=self.attn_dropout_prob ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       Union[str, Any]				    = MobileViTVaModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       List[str]				    = model(__lowerCamelCase)
            self.parent.assertEqual(
                result.last_hidden_state.shape ,						(
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       List[Any]				    = self.num_labels
            _A      :       Union[str, Any]				    = MobileViTVaForImageClassification(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       List[Any]				    = model(__lowerCamelCase ,						labels=__lowerCamelCase)
            self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels))


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       Tuple				    = self.num_labels
            _A      :       Tuple				    = MobileViTVaForSemanticSegmentation(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       str				    = model(__lowerCamelCase)
            self.parent.assertEqual(
                result.logits.shape ,						(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,						)
            _A      :       Optional[int]				    = model(__lowerCamelCase ,						labels=__lowerCamelCase)
            self.parent.assertEqual(
                result.logits.shape ,						(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,						)


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







@require_torch
class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= (
         (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )

     __SCREAMING_SNAKE_CASE  			= (
         {
             "feature-extraction": MobileViTVaModel,
             "image-classification": MobileViTVaForImageClassification,
             "image-segmentation": MobileViTVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       int				    = MobileViTVaModelTester(self)
            _A      :       int				    = MobileViTVaConfigTester(self ,						config_class=__lowerCamelCase ,						has_text_modality=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[Any]:
            self.config_tester.run_common_tests()


     @unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
     def  _lowerCamelCase (						self)			->				Dict:
            pass


     @unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
     def  _lowerCamelCase (						self)			->				Tuple:
            pass


     @unittest.skip(reason="MobileViTV2 does not output attentions")
     def  _lowerCamelCase (						self)			->				Dict:
            pass


     @require_torch_multi_gpu
     @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
     def  _lowerCamelCase (						self)			->				int:
            pass


     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            pass


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A    ,			_A      :       Tuple				    = self.model_tester.prepare_config_and_inputs_for_common()

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

                   _A      :       Optional[Any]				    = ["pixel_values"]
                   self.assertListEqual(arg_names[:1] ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Optional[int]				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            def check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase):
                   _A      :       str				    = model_class(__lowerCamelCase)
                   model.to(__lowerCamelCase)
                   model.eval()

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

                   _A      :       Any				    = outputs.hidden_states

                   _A      :       str				    = 5
                   self.assertEqual(len(__lowerCamelCase) ,						__lowerCamelCase)

                   # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                   # with the width and height being successively divided by 2.
                   _A      :       List[Any]				    = 2
                   for i in range(len(__lowerCamelCase)):
                          self.assertListEqual(
                              list(hidden_states[i].shape[-2:]) ,						[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,						)
                          divisor *= 2

                   self.assertEqual(self.model_tester.output_stride ,						divisor // 2)

            _A    ,			_A      :       Dict				    = self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A      :       str				    = True
                   check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _A      :       str				    = True

                   check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       int				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[str]				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase)


     @slow
     def  _lowerCamelCase (						self)			->				Dict:
            for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A      :       Optional[int]				    = MobileViTVaModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)




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







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




     '''simple docstring'''


     @cached_property
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            return (
                MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
                if is_vision_available()
                else None
            )


     @slow
     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       str				    = MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
                __lowerCamelCase)

            _A      :       int				    = self.default_image_processor
            _A      :       Any				    = prepare_img()
            _A      :       Dict				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

            # forward pass
            with torch.no_grad():
                   _A      :       Union[str, Any]				    = model(**__lowerCamelCase)

            # verify the logits
            _A      :       Dict				    = torch.Size((1, 1_0_0_0))
            self.assertEqual(outputs.logits.shape ,						__lowerCamelCase)

            _A      :       str				    = torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]).to(__lowerCamelCase)

            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,						__lowerCamelCase ,						atol=1e-4))


     @slow
     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Union[str, Any]				    = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
            _A      :       List[str]				    = model.to(__lowerCamelCase)

            _A      :       int				    = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

            _A      :       Union[str, Any]				    = prepare_img()
            _A      :       Tuple				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

            # forward pass
            with torch.no_grad():
                   _A      :       Any				    = model(**__lowerCamelCase)
            _A      :       Tuple				    = outputs.logits

            # verify the logits
            _A      :       str				    = torch.Size((1, 2_1, 3_2, 3_2))
            self.assertEqual(logits.shape ,						__lowerCamelCase)

            _A      :       Any				    = torch.tensor(
                [
                    [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
                    [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
                    [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
                ] ,						device=__lowerCamelCase ,						)

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


     @slow
     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       str				    = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
            _A      :       Optional[Any]				    = model.to(__lowerCamelCase)

            _A      :       int				    = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

            _A      :       Dict				    = prepare_img()
            _A      :       int				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

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

            _A      :       Dict				    = outputs.logits.detach().cpu()

            _A      :       List[Any]				    = image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase ,						target_sizes=[(5_0, 6_0)])
            _A      :       Dict				    = torch.Size((5_0, 6_0))
            self.assertEqual(segmentation[0].shape ,						__lowerCamelCase)

            _A      :       List[Any]				    = image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase)
            _A      :       Optional[Any]				    = torch.Size((3_2, 3_2))
            self.assertEqual(segmentation[0].shape ,						__lowerCamelCase)




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

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


lowerCAmelCase__      		= logging.get_logger(__name__)


lowerCAmelCase__      		= {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

lowerCAmelCase__      		= '</w>'
lowerCAmelCase__      		= '@@ '
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]						):
       _A      :       Optional[int]				    = set()
       _A      :       Optional[Any]				    = word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              _A      :       List[Any]				    = char
       return pairs


# Speech2Text2 has no max input length
lowerCAmelCase__      		= {'facebook/s2t-wav2vec2-large-en-de': 10_24}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase=False ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[Any]:
            super().__init__(
                unk_token=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						do_lower_case=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Dict				    = do_lower_case

            with open(__lowerCamelCase ,						encoding="utf-8") as vocab_handle:
                   _A      :       Optional[int]				    = json.load(__lowerCamelCase)
            _A      :       Optional[Any]				    = {v: k for k, v in self.encoder.items()}

            if merges_file is None:
                   logger.info(F"No merges files provided. {self.__class__.__name__} can only be used for decoding.")

                   _A      :       Optional[Any]				    = None
                   _A      :       Tuple				    = None
            else:
                   with open(__lowerCamelCase ,						encoding="utf-8") as merges_handle:
                          _A      :       Optional[int]				    = merges_handle.read().split("\n")[:-1]

                   _A      :       Union[str, Any]				    = [tuple(merge.split()[:2]) for merge in merges]
                   _A      :       Optional[int]				    = dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase))))
                   _A      :       List[Any]				    = {}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return len(self.decoder)


     def  _lowerCamelCase (						self)			->				Dict:
            return dict(self.encoder ,						**self.added_tokens_encoder)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            _A      :       Tuple				    = tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
            if token in self.cache:
                   return self.cache[token]
            _A      :       int				    = get_pairs(__lowerCamelCase)

            if not pairs:
                   return token

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

                          if word[i] == first and i < len(__lowerCamelCase) - 1 and word[i + 1] == second:
                                 new_word.append(first + second)
                                 i += 2
                          else:
                                 new_word.append(word[i])
                                 i += 1
                   _A      :       List[str]				    = tuple(__lowerCamelCase)
                   _A      :       List[str]				    = new_word
                   if len(__lowerCamelCase) == 1:
                          break
                   else:
                          _A      :       List[Any]				    = get_pairs(__lowerCamelCase)
            _A      :       Tuple				    = " ".join(__lowerCamelCase)
            if word == "\n  " + BPE_TOKEN_MERGES:
                   _A      :       List[str]				    = "\n" + BPE_TOKEN_MERGES

            if word.endswith(__lowerCamelCase):
                   _A      :       int				    = word.replace(__lowerCamelCase ,						"")

            _A      :       int				    = word.replace(" " ,						__lowerCamelCase)
            _A      :       Union[str, Any]				    = word
            return word


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:

            if self.bpe_ranks is None:
                   raise ValueError(
                       "This tokenizer was instantiated without a `merges.txt` file, so"
                       " that it can only be used for decoding, not for encoding."
                       "Make sure to provide `merges.txt` file at instantiation to enable "
                       "encoding.")

            if self.do_lower_case:
                   _A      :       List[Any]				    = text.lower()

            _A      :       Optional[int]				    = text.split()

            _A      :       List[str]				    = []
            for token in text:
                   if token:
                          split_tokens.extend(list(self.bpe(__lowerCamelCase).split(" ")))

            return split_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            return self.encoder.get(__lowerCamelCase ,						self.encoder.get(self.unk_token))


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            _A      :       List[str]				    = self.decoder.get(__lowerCamelCase ,						self.unk_token)
            return result


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            _A      :       str				    = " ".join(__lowerCamelCase)

            # make sure @@ tokens are concatenated
            _A      :       int				    = "".join(string.split(__lowerCamelCase))

            return string


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                   return
            _A      :       Any				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
            _A      :       Any				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

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

            _A      :       Union[str, Any]				    = 0
            if self.bpe_ranks is None:
                   return (vocab_file,)

            with open(__lowerCamelCase ,						"w" ,						encoding="utf-8") as writer:
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,						key=lambda __lowerCamelCase: 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!")
                                 _A      :       Optional[int]				    = token_index
                          writer.write(" ".join(__lowerCamelCase) + "\n")
                          index += 1

            return (vocab_file, merges_file)
