



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__    (							lowerCamelCase__):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.config_class(**self.inputs_dict				)
						self.parent.assertTrue(hasattr(__lowerCamelCase	,'''width_multiplier'''				)				)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=64	,__lowerCamelCase=2	,__lowerCamelCase=3	,__lowerCamelCase="swish"	,__lowerCamelCase=3	,__lowerCamelCase=32	,__lowerCamelCase=0.1	,__lowerCamelCase=0.02	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=10	,__lowerCamelCase=None	,__lowerCamelCase=0.25	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  parent
						lowerCAmelCase__  :       List[str]				=  batch_size
						lowerCAmelCase__  :       Dict				=  image_size
						lowerCAmelCase__  :       Tuple				=  patch_size
						lowerCAmelCase__  :       List[Any]				=  num_channels
						lowerCAmelCase__  :       Tuple				=  make_divisible(5_12 * width_multiplier	,divisor=8				)
						lowerCAmelCase__  :       Dict				=  hidden_act
						lowerCAmelCase__  :       Optional[int]				=  conv_kernel_size
						lowerCAmelCase__  :       int				=  output_stride
						lowerCAmelCase__  :       List[Any]				=  classifier_dropout_prob
						lowerCAmelCase__  :       Dict				=  use_labels
						lowerCAmelCase__  :       Tuple				=  is_training
						lowerCAmelCase__  :       str				=  num_labels
						lowerCAmelCase__  :       Optional[Any]				=  initializer_range
						lowerCAmelCase__  :       Tuple				=  scope
						lowerCAmelCase__  :       Tuple				=  width_multiplier
						lowerCAmelCase__  :       Optional[int]				=  ffn_dropout
						lowerCAmelCase__  :       Dict				=  attn_dropout



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowerCAmelCase__  :       Union[str, Any]				=  None
						lowerCAmelCase__  :       Dict				=  None
						if self.use_labels:
										lowerCAmelCase__  :       Optional[Any]				=  ids_tensor([self.batch_size]	,self.num_labels				)
										lowerCAmelCase__  :       Optional[Any]				=  ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels				)

						lowerCAmelCase__  :       Tuple				=  self.get_config()

						return config, pixel_values, labels, pixel_labels



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						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				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  MobileViTVaModel(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  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				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.num_labels
						lowerCAmelCase__  :       Union[str, Any]				=  MobileViTVaForImageClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.num_labels
						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaForSemanticSegmentation(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  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,
						    )	,)
						lowerCAmelCase__  :       List[str]				=  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				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.prepare_config_and_inputs()
						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  config_and_inputs
						lowerCAmelCase__  :       Dict				=  {'''pixel_values''': pixel_values}
						return config, inputs_dict



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=(
		    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)

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

		snake_case_			=False
		snake_case_			=False
		snake_case_			=False
		snake_case_			=False



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MobileViTVaModelTester(self				)
						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaConfigTester(self	,config_class=__lowerCamelCase	,has_text_modality=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MobileViTV2 does not output attentions'''				)
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						pass



		@require_torch_multi_gpu
		@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



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

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCAmelCase__  :       Union[str, Any]				=  ['''pixel_values''']
										self.assertListEqual(arg_names[:1]	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						def check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()

										with torch.no_grad():
														lowerCAmelCase__  :       Optional[Any]				=  model(**self._prepare_for_class(__lowerCamelCase	,__lowerCamelCase				)				)

										lowerCAmelCase__  :       Dict				=  outputs.hidden_states

										lowerCAmelCase__  :       Any				=  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.
										lowerCAmelCase__  :       Optional[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				)

						lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Any				=  True
										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)

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

										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__  :       Optional[int]				=  MobileViTVaModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  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]:

						"""simple docstring"""






						return (
						    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''				)
						    if is_vision_available()
						    else None
						)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''				).to(
						    __lowerCamelCase				)

						lowerCAmelCase__  :       str				=  self.default_image_processor
						lowerCAmelCase__  :       Union[str, Any]				=  prepare_img()
						lowerCAmelCase__  :       int				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

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

						# verify the logits
						lowerCAmelCase__  :       str				=  torch.Size((1, 10_00)				)
						self.assertEqual(outputs.logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]				).to(__lowerCamelCase				)

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



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)
						lowerCAmelCase__  :       Optional[Any]				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)

						lowerCAmelCase__  :       Optional[Any]				=  prepare_img()
						lowerCAmelCase__  :       Union[str, Any]				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  outputs.logits

						# verify the logits
						lowerCAmelCase__  :       Optional[int]				=  torch.Size((1, 21, 32, 32)				)
						self.assertEqual(logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  torch.tensor(
						    [
						        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
						        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
						        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
						    ]	,device=__lowerCamelCase	,)

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



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)
						lowerCAmelCase__  :       Dict				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)

						lowerCAmelCase__  :       int				=  prepare_img()
						lowerCAmelCase__  :       Any				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  outputs.logits.detach().cpu()

						lowerCAmelCase__  :       Optional[Any]				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase	,target_sizes=[(50, 60)]				)
						lowerCAmelCase__  :       Tuple				=  torch.Size((50, 60)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  torch.Size((32, 32)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)






import json
import os
from typing import Optional, Tuple

import regex as re

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


__snake_case       :	int            =logging.get_logger(__name__)

__snake_case       :	List[Any]            ={
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__snake_case       :	Any            ={
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

__snake_case       :	Optional[int]            ={
    'ctrl': 2_5_6,
}

__snake_case       :	Optional[int]            ={
    'Pregnancy': 1_6_8_6_2_9,
    'Christianity': 7_6_7_5,
    'Explain': 1_0_6_4_2_3,
    'Fitness': 6_3_4_4_0,
    'Saving': 6_3_1_6_3,
    'Ask': 2_7_1_7_1,
    'Ass': 9_5_9_8_5,
    'Joke': 1_6_3_5_0_9,
    'Questions': 4_5_6_2_2,
    'Thoughts': 4_9_6_0_5,
    'Retail': 5_2_3_4_2,
    'Feminism': 1_6_4_3_3_8,
    'Writing': 1_1_9_9_2,
    'Atheism': 1_9_2_2_6_3,
    'Netflix': 4_8_6_1_6,
    'Computing': 3_9_6_3_9,
    'Opinion': 4_3_2_1_3,
    'Alone': 4_4_9_6_7,
    'Funny': 5_8_9_1_7,
    'Gaming': 4_0_3_5_8,
    'Human': 4_0_8_8,
    'India': 1_3_3_1,
    'Joker': 7_7_1_3_8,
    'Diet': 3_6_2_0_6,
    'Legal': 1_1_8_5_9,
    'Norman': 4_9_3_9,
    'Tip': 7_2_6_8_9,
    'Weight': 5_2_3_4_3,
    'Movies': 4_6_2_7_3,
    'Running': 2_3_4_2_5,
    'Science': 2_0_9_0,
    'Horror': 3_7_7_9_3,
    'Confession': 6_0_5_7_2,
    'Finance': 1_2_2_5_0,
    'Politics': 1_6_3_6_0,
    'Scary': 1_9_1_9_8_5,
    'Support': 1_2_6_5_4,
    'Technologies': 3_2_5_1_6,
    'Teenage': 6_6_1_6_0,
    'Event': 3_2_7_6_9,
    'Learned': 6_7_4_6_0,
    'Notion': 1_8_2_7_7_0,
    'Wikipedia': 3_7_5_8_3,
    'Books': 6_6_6_5,
    'Extract': 7_6_0_5_0,
    'Confessions': 1_0_2_7_0_1,
    'Conspiracy': 7_5_9_3_2,
    'Links': 6_3_6_7_4,
    'Narcissus': 1_5_0_4_2_5,
    'Relationship': 5_4_7_6_6,
    'Relationships': 1_3_4_7_9_6,
    'Reviews': 4_1_6_7_1,
    'News': 4_2_5_6,
    'Translation': 2_6_8_2_0,
    'multilingual': 1_2_8_4_0_6,
}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  set()
				lowerCAmelCase__  :       Tuple				=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char))
								lowerCAmelCase__  :       List[Any]				=  char

				lowerCAmelCase__  :       Dict				=  set(lowerCamelCase_)
				return pairs



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=CONTROL_CODES



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase="<unk>"	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(unk_token=__lowerCamelCase	,**__lowerCamelCase				)

						with open(__lowerCamelCase	,encoding='''utf-8'''				) as vocab_handle:
										lowerCAmelCase__  :       List[str]				=  json.load(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  {v: k for k, v in self.encoder.items()}
						with open(__lowerCamelCase	,encoding='''utf-8'''				) as merges_handle:
										lowerCAmelCase__  :       str				=  merges_handle.read().split('''\n'''				)[1:-1]
						lowerCAmelCase__  :       Optional[Any]				=  [tuple(merge.split()				) for merge in merges]
						lowerCAmelCase__  :       int				=  dict(zip(__lowerCamelCase	,range(len(__lowerCamelCase				)				)				)				)
						lowerCAmelCase__  :       Union[str, Any]				=  {}



		@property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return len(self.encoder				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return dict(self.encoder	,**self.added_tokens_encoder				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						if token in self.cache:
										return self.cache[token]
						lowerCAmelCase__  :       Union[str, Any]				=  tuple(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  tuple(list(word[:-1]				) + [word[-1] + '''</w>''']				)
						lowerCAmelCase__  :       Union[str, Any]				=  get_pairs(__lowerCamelCase				)

						if not pairs:
										return token

						while True:
										lowerCAmelCase__  :       Optional[Any]				=  min(__lowerCamelCase	,key=lambda __lowerCamelCase				: self.bpe_ranks.get(__lowerCamelCase	,float('''inf'''				)				)				)
										if bigram not in self.bpe_ranks:
														break
										lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  bigram
										lowerCAmelCase__  :       Optional[int]				=  []
										lowerCAmelCase__  :       int				=  0
										while i < len(__lowerCamelCase				):
														try:
																		lowerCAmelCase__  :       Optional[Any]				=  word.index(__lowerCamelCase	,__lowerCamelCase				)
														except ValueError:
																		new_word.extend(word[i:]				)
																		break
														else:
																		new_word.extend(word[i:j]				)
																		lowerCAmelCase__  :       int				=  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
										lowerCAmelCase__  :       Optional[Any]				=  tuple(__lowerCamelCase				)
										lowerCAmelCase__  :       Any				=  new_word
										if len(__lowerCamelCase				) == 1:
														break
										else:
														lowerCAmelCase__  :       Optional[Any]				=  get_pairs(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  '''@@ '''.join(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  word[:-4]
						lowerCAmelCase__  :       List[Any]				=  word
						return word



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  []

						lowerCAmelCase__  :       int				=  re.findall(R'''\S+\n?'''	,__lowerCamelCase				)

						for token in words:
										split_tokens.extend(list(self.bpe(__lowerCamelCase				).split(''' '''				)				)				)
						return split_tokens



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						return self.encoder.get(__lowerCamelCase	,self.encoder.get(self.unk_token				)				)



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

						"""simple docstring"""






						return self.decoder.get(__lowerCamelCase	,self.unk_token				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  ''' '''.join(__lowerCamelCase				).replace('''@@ '''	,''''''				).strip()
						return out_string



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Tuple[str]:

						"""simple docstring"""






						if not os.path.isdir(__lowerCamelCase				):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
										return
						lowerCAmelCase__  :       Optional[int]				=  os.path.join(
						    __lowerCamelCase	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
						lowerCAmelCase__  :       List[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'''				)

						lowerCAmelCase__  :       Optional[Any]				=  0
						with open(__lowerCamelCase	,'''w'''	,encoding='''utf-8'''				) as writer:
										writer.write('''#version: 0.2\n'''				)
										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 {merge_file}: BPE merge indices are not consecutive."""
																		    ''' Please check that the tokenizer is not corrupted!'''				)
																		lowerCAmelCase__  :       Optional[int]				=  token_index
														writer.write(''' '''.join(__lowerCamelCase				) + '''\n'''				)
														index += 1

						return vocab_file, merge_file

		# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
		#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
		#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
		#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
		#     return ''.join(tokens_generated_so_far)

