import inspect
import math
import tempfile
import unittest

import numpy as np

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

from ...test_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 torch import nn

		from transformers import ViTMAEForPreTraining, ViTMAEModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class 		a__						:

					def __init__(    self      :       Any,_A      :       str,_A      :       Union[str, Any]=13,_A      :       Dict=30,_A      :       Optional[Any]=2,_A      :       List[Any]=3,_A      :       Optional[int]=True,_A      :       Tuple=True,_A      :       List[Any]=32,_A      :       Tuple=5,_A      :       List[Any]=4,_A      :       Optional[Any]=37,_A      :       Optional[int]="gelu",_A      :       Tuple=0.1,_A      :       Optional[int]=0.1,_A      :       List[Any]=10,_A      :       Optional[int]=0.02,_A      :       Tuple=3,_A      :       Optional[int]=0.6,_A      :       Any=None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		str				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		str				      =    image_size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    patch_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    num_channels
							SCREAMING_SNAKE_CASE_  :		int				      =    is_training
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		Any				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		str				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    type_sequence_label_size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    mask_ratio
							SCREAMING_SNAKE_CASE_  :		str				      =    scope

							# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
							# (we add 1 for the [CLS] token)
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (image_size // patch_size) ** 2
							SCREAMING_SNAKE_CASE_  :		Any				      =    int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    None
							if self.use_labels:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ids_tensor([self.batch_size],self.type_sequence_label_size    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.get_config()

							return config, pixel_values, labels

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							return ViTMAEConfig(
							    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=_A,initializer_range=self.initializer_range,mask_ratio=self.mask_ratio,)

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Any,_A      :       List[Any],_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    ViTMAEModel(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Any,_A      :       str,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    ViTMAEForPreTraining(_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    (self.image_size // self.patch_size) ** 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape,(self.batch_size, num_patches, expected_num_channels)    )

							# test greyscale images
							SCREAMING_SNAKE_CASE_  :		int				      =    1
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ViTMAEForPreTraining(_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		str				      =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.patch_size**2
							self.parent.assertEqual(result.logits.shape,(self.batch_size, num_patches, expected_num_channels)    )






					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






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







@require_torch
class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					A	       =							{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

					A	       =							False
					A	       =							False
					A	       =							False
					A	       =							False

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    ViTMAEModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ConfigTester(self,config_class=_A,has_text_modality=_A,hidden_size=37    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					@unittest.skip(reason="ViTMAE does not use inputs_embeds"    )
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							pass

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs_for_common()

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

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE_  :		int				      =    ["pixel_values"]
									self.assertListEqual(arg_names[:1],_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_A    )

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[str],_A      :       Optional[Any],_A      :       Union[str, Any]    ):







							"""simple docstring"""






							np.random.seed(2    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.from_numpy(_A    )

							# Add `noise` argument.
							# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
							SCREAMING_SNAKE_CASE_  :		Dict				      =    pt_noise

							super().check_pt_tf_models(_A,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class(_A    )
									model.to(_A    )
									model.eval()
									# make random mask reproducible
									torch.manual_seed(2    )
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_  :		Any				      =    model(**self._prepare_for_class(_A,_A    )    )

									SCREAMING_SNAKE_CASE_  :		Tuple				      =    outputs[0].cpu().numpy()
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    0

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_A    )
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model_class.from_pretrained(_A    )
											model.to(_A    )
											# make random mask reproducible
											torch.manual_seed(2    )
											with torch.no_grad():
													SCREAMING_SNAKE_CASE_  :		List[Any]				      =    model(**self._prepare_for_class(_A,_A    )    )

											# Make sure we don't have nans
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    after_outputs[0].cpu().numpy()
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
											SCREAMING_SNAKE_CASE_  :		Any				      =    np.amax(np.abs(out_a - out_a    )    )
											self.assertLessEqual(_A,1E-5    )

					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."    )
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							pass

					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."    )
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							pass

					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."    )
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							pass

					@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"    )
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							pass

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







							"""simple docstring"""






							pass






					@slow
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    ViTMAEModel.from_pretrained(_A    )
									self.assertIsNotNone(_A    )







def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image







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

					@cached_property
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"    ) if is_vision_available() else None






					@slow
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							np.random.seed(2    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"    ).to(_A    )

							SCREAMING_SNAKE_CASE_  :		int				      =    self.default_image_processor
							SCREAMING_SNAKE_CASE_  :		Any				      =    prepare_img()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    image_processor(images=_A,return_tensors="pt"    ).to(_A    )

							# prepare a noise vector that will be also used for testing the TF model
							# (this way we can ensure that the PT and TF models operate on the same inputs)
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    ViTMAEConfig()
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.uniform(size=(1, num_patches)    )

							# forward pass
							with torch.no_grad():
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(**_A,noise=torch.from_numpy(_A    ).to(device=_A    )    )

							# verify the logits
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.Size((1, 196, 768)    )
							self.assertEqual(outputs.logits.shape,_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.tensor(
							    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]    )

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

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


__lowerCamelCase					:	List[str]       =  logging.get_logger(__name__)


__lowerCamelCase					:	Tuple       =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__lowerCamelCase					:	List[Any]       =  {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

__lowerCamelCase					:	int       =  {
    '''allenai/longformer-base-4096''': 40_96,
    '''allenai/longformer-large-4096''': 40_96,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
		    list(range(ord("!"	)   , ord("~"	) + 1	)	) + list(range(ord("¡"	)   , ord("¬"	) + 1	)	) + list(range(ord("®"	)   , ord("ÿ"	) + 1	)	)
		)
		SCREAMING_SNAKE_CASE_  :		str				      =    bs[:]
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    0
		for b in range(2**8	):
				if b not in bs:
						bs.append(lowerCAmelCase	)
						cs.append(2**8 + n	)
						n += 1
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    [chr(lowerCAmelCase	) for n in cs]
		return dict(zip(lowerCAmelCase   , lowerCAmelCase	)	)




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







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







class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							['input_ids', 'attention_mask']

					def __init__(    self      :       Union[str, Any],_A      :       List[Any],_A      :       Tuple,_A      :       str="replace",_A      :       Optional[int]="<s>",_A      :       Dict="</s>",_A      :       Any="</s>",_A      :       Optional[Any]="<s>",_A      :       Union[str, Any]="<unk>",_A      :       int="<pad>",_A      :       Dict="<mask>",_A      :       int=False,**_A      :       Dict,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else bos_token
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else eos_token
							SCREAMING_SNAKE_CASE_  :		str				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else sep_token
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else cls_token
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else unk_token
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_  :		Dict				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else mask_token

							super().__init__(
							    errors=_A,bos_token=_A,eos_token=_A,unk_token=_A,sep_token=_A,cls_token=_A,pad_token=_A,mask_token=_A,add_prefix_space=_A,**_A,)

							with open(_A,encoding="utf-8"    ) as vocab_handle:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    json.load(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {v: k for k, v in self.encoder.items()}
							SCREAMING_SNAKE_CASE_  :		Any				      =    errors  # how to handle errors in decoding
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    bytes_to_unicode()
							SCREAMING_SNAKE_CASE_  :		str				      =    {v: k for k, v in self.byte_encoder.items()}
							with open(_A,encoding="utf-8"    ) as merges_handle:
									SCREAMING_SNAKE_CASE_  :		int				      =    merges_handle.read().split("\n"    )[1:-1]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(merge.split()    ) for merge in bpe_merges]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    dict(zip(_A,range(len(_A    )    )    )    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    {}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"    )

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							return len(self.encoder    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Any,_A      :       int    ):







							"""simple docstring"""






							if token in self.cache:
									return self.cache[token]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tuple(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    get_pairs(_A    )

							if not pairs:
									return token

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

											if word[i] == first and i < len(_A    ) - 1 and word[i + 1] == second:
													new_word.append(first + second    )
													i += 2
											else:
													new_word.append(word[i]    )
													i += 1
									SCREAMING_SNAKE_CASE_  :		Dict				      =    tuple(_A    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    new_word
									if len(_A    ) == 1:
											break
									else:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    get_pairs(_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    " ".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    word
							return word

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    []
							for token in re.findall(self.pat,_A    ):
									SCREAMING_SNAKE_CASE_  :		Any				      =    "".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A    ).split(" "    )    )
							return bpe_tokens

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       str    ):







							"""simple docstring"""






							return self.decoder.get(_A    )

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8",errors=self.errors    )
							return text

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






							if not os.path.isdir(_A    ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
									return
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    os.path.join(
							    _A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    os.path.join(
							    _A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

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

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
							with open(_A,"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 _A    : 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!"    )
													SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    token_index
											writer.write(" ".join(_A    ) + "\n"    )
											index += 1

							return vocab_file, merge_file

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		str				      =    [self.cls_token_id]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None,_A      :       bool = False    ):







							"""simple docstring"""






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

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

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		Optional[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]






					def 				__UpperCamelCase   (    self      :       Any,_A      :       Union[str, Any],_A      :       Any=False,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    kwargs.pop("add_prefix_space",self.add_prefix_space    )
							if (is_split_into_words or add_prefix_space) and (len(_A    ) > 0 and not text[0].isspace()):
									SCREAMING_SNAKE_CASE_  :		str				      =    " " + text
							return (text, kwargs)
