



"""simple docstring"""







import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
							from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class      UpperCAmelCase__		:



		def __init__( self  :    List[Any]      ,							snake_case  :    Dict      ,							snake_case  :    Optional[Any]=13      ,							snake_case  :    Optional[int]=32      ,							snake_case  :    str=2      ,							snake_case  :    Optional[int]=3      ,							snake_case  :    Dict=16      ,							snake_case  :    Tuple=[1, 2, 1]      ,							snake_case  :    Optional[int]=[2, 2, 4]      ,							snake_case  :    List[Any]=2      ,							snake_case  :    List[Any]=2.0      ,							snake_case  :    Dict=True      ,							snake_case  :    Optional[int]=0.0      ,							snake_case  :    str=0.0      ,							snake_case  :    List[str]=0.1      ,							snake_case  :    Any="gelu"      ,							snake_case  :    List[Any]=False      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    str=1E-5      ,							snake_case  :    str=True      ,							snake_case  :    List[str]=None      ,							snake_case  :    Dict=True      ,							snake_case  :    Optional[Any]=10      ,							snake_case  :    List[Any]=8      ,							)			->						Any:






									'''simple docstring'''

									A         =					parent
									A         =					batch_size
									A         =					image_size
									A         =					patch_size
									A         =					num_channels
									A         =					embed_dim
									A         =					depths
									A         =					num_heads
									A         =					window_size
									A         =					mlp_ratio
									A         =					qkv_bias
									A         =					hidden_dropout_prob
									A         =					attention_probs_dropout_prob
									A         =					drop_path_rate
									A         =					hidden_act
									A         =					use_absolute_embeddings
									A         =					patch_norm
									A         =					layer_norm_eps
									A         =					initializer_range
									A         =					is_training
									A         =					scope
									A         =					use_labels
									A         =					type_sequence_label_size
									A         =					encoder_stride



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






									'''simple docstring'''

									A         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

									A         =					None
									if self.use_labels:
																A         =					ids_tensor([self.batch_size]      ,							self.type_sequence_label_size				)

									A         =					self.get_config()

									return config, pixel_values, labels



		def 					A_		( self  :    Dict				)			->						Tuple:






									'''simple docstring'''

									return SwinvaConfig(
									    image_size=self.image_size      ,							patch_size=self.patch_size      ,							num_channels=self.num_channels      ,							embed_dim=self.embed_dim      ,							depths=self.depths      ,							num_heads=self.num_heads      ,							window_size=self.window_size      ,							mlp_ratio=self.mlp_ratio      ,							qkv_bias=self.qkv_bias      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							drop_path_rate=self.drop_path_rate      ,							hidden_act=self.hidden_act      ,							use_absolute_embeddings=self.use_absolute_embeddings      ,							path_norm=self.patch_norm      ,							layer_norm_eps=self.layer_norm_eps      ,							initializer_range=self.initializer_range      ,							encoder_stride=self.encoder_stride      ,							)



		def 					A_		( self  :    List[str]      ,							snake_case  :    Dict      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[Any]				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					SwinvaModel(config=snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(snake_case				)

									A         =					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
									A         =					int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

									self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, expected_seq_len, expected_dim)				)



		def 					A_		( self  :    List[Any]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Dict				)			->						Tuple:






									'''simple docstring'''

									A         =					SwinvaForMaskedImageModeling(config=snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(snake_case				)
									self.parent.assertEqual(
									    result.logits.shape      ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

									# test greyscale images
									A         =					1
									A         =					SwinvaForMaskedImageModeling(snake_case				)
									model.to(snake_case				)
									model.eval()

									A         =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
									A         =					model(snake_case				)
									self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, 1, self.image_size, self.image_size)				)



		def 					A_		( self  :    Any      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					self.type_sequence_label_size
									A         =					SwinvaForImageClassification(snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(snake_case      ,							labels=snake_case				)
									self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)				)



		def 					A_		( self  :    Tuple				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					self.prepare_config_and_inputs()
									A      ,  A      ,  A         =					config_and_inputs
									A         =					{'pixel_values': pixel_values}
									return config, inputs_dict






@require_torch
class      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Tuple 							=     (
		    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
		)
		lowerCAmelCase_       :	List[Any] 							=     (
		    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCAmelCase_       :	str 							=     False
		lowerCAmelCase_       :	List[str] 							=     False
		lowerCAmelCase_       :	Any 							=     False
		lowerCAmelCase_       :	List[Any] 							=     False



		def 					A_		( self  :    str				)			->						Optional[int]:






									'''simple docstring'''

									A         =					SwinvaModelTester(self				)
									A         =					ConfigTester(self      ,							config_class=snake_case      ,							embed_dim=37				)



		def 					A_		( self  :    str				)			->						Dict:






									'''simple docstring'''

									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()



		def 					A_		( self  :    List[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case				)



		@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'				)
		def 					A_		( self  :    Tuple				)			->						Union[str, Any]:






									'''simple docstring'''

									pass



		@unittest.skip(reason='Swinv2 does not use inputs_embeds'				)
		def 					A_		( self  :    Optional[Any]				)			->						Union[str, Any]:






									'''simple docstring'''

									pass



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






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()

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



		def 					A_		( self  :    Optional[int]				)			->						List[Any]:






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()

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

																A         =					['pixel_values']
																self.assertListEqual(arg_names[:1]      ,							snake_case				)



		def 					A_		( self  :    Tuple				)			->						Any:






									'''simple docstring'''

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

									for model_class in self.all_model_classes:
																A         =					True
																A         =					False
																A         =					True
																A         =					model_class(snake_case				)
																model.to(snake_case				)
																model.eval()
																with torch.no_grad():
																							A         =					model(**self._prepare_for_class(snake_case      ,							snake_case				)				)
																A         =					outputs.attentions
																A         =					len(self.model_tester.depths				)
																self.assertEqual(len(snake_case				)      ,							snake_case				)

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																A         =					True
																A         =					config.window_size**2
																A         =					model_class(snake_case				)
																model.to(snake_case				)
																model.eval()
																with torch.no_grad():
																							A         =					model(**self._prepare_for_class(snake_case      ,							snake_case				)				)
																A         =					outputs.attentions
																self.assertEqual(len(snake_case				)      ,							snake_case				)

																self.assertListEqual(
																    list(attentions[0].shape[-3:]				)      ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,							)
																A         =					len(snake_case				)

																# Check attention is always last and order is fine
																A         =					True
																A         =					True
																A         =					model_class(snake_case				)
																model.to(snake_case				)
																model.eval()
																with torch.no_grad():
																							A         =					model(**self._prepare_for_class(snake_case      ,							snake_case				)				)

																if hasattr(self.model_tester      ,							'num_hidden_states_types'				):
																							A         =					self.model_tester.num_hidden_states_types
																else:
																							# also another +1 for reshaped_hidden_states
																							A         =					2
																self.assertEqual(out_len + added_hidden_states      ,							len(snake_case				)				)

																A         =					outputs.attentions

																self.assertEqual(len(snake_case				)      ,							snake_case				)

																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]				)      ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,							)



		def 					A_		( self  :    List[str]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Tuple      ,							snake_case  :    List[str]				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					model_class(snake_case				)
									model.to(snake_case				)
									model.eval()

									with torch.no_grad():
																A         =					model(**self._prepare_for_class(snake_case      ,							snake_case				)				)

									A         =					outputs.hidden_states

									A         =					getattr(
									    self.model_tester      ,							'expected_num_hidden_layers'      ,							len(self.model_tester.depths				) + 1				)
									self.assertEqual(len(snake_case				)      ,							snake_case				)

									# Swinv2 has a different seq_length
									A         =					(
									    config.patch_size
									    if isinstance(config.patch_size      ,							collections.abc.Iterable				)
									    else (config.patch_size, config.patch_size)
									)

									A         =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]				)      ,							[num_patches, self.model_tester.embed_dim]      ,							)

									A         =					outputs.reshaped_hidden_states
									self.assertEqual(len(snake_case				)      ,							snake_case				)

									A      ,  A      ,  A      ,  A         =					reshaped_hidden_states[0].shape
									A         =					(
									    reshaped_hidden_states[0].view(snake_case      ,							snake_case      ,							height * width				).permute(0      ,							2      ,							1				)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]				)      ,							[num_patches, self.model_tester.embed_dim]      ,							)



		def 					A_		( self  :    List[str]				)			->						Any:






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()

									A         =					(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size      ,							collections.abc.Iterable				)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
																A         =					True
																self.check_hidden_states_output(snake_case      ,							snake_case      ,							snake_case      ,							snake_case				)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																A         =					True

																self.check_hidden_states_output(snake_case      ,							snake_case      ,							snake_case      ,							snake_case				)



		def 					A_		( self  :    str				)			->						int:






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()
									A         =					3

									A         =					(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size      ,							collections.abc.Iterable				)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									A         =					(
									    config.patch_size
									    if isinstance(config.patch_size      ,							collections.abc.Iterable				)
									    else (config.patch_size, config.patch_size)
									)

									A         =					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									A         =					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
																A         =					True
																self.check_hidden_states_output(snake_case      ,							snake_case      ,							snake_case      ,							(padded_height, padded_width)				)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																A         =					True
																self.check_hidden_states_output(snake_case      ,							snake_case      ,							snake_case      ,							(padded_height, padded_width)				)



		def 					A_		( self  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*snake_case				)



		def 					A_		( self  :    List[str]				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case				)



		@slow
		def 					A_		( self  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																A         =					SwinvaModel.from_pretrained(snake_case				)
																self.assertIsNotNone(snake_case				)



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






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()

									A         =					_config_zero_init(snake_case				)
									for model_class in self.all_model_classes:
																A         =					model_class(config=snake_case				)
																for name, param in model.named_parameters():
																							if "embeddings" not in name and "logit_scale" not in name and 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"""      ,							)






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



		@cached_property
		def 					A_		( self  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									return (
									    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'				)
									    if is_vision_available()
									    else None
									)



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'				).to(
									    snake_case				)
									A         =					self.default_image_processor

									A         =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
									A         =					image_processor(images=snake_case      ,							return_tensors='pt'				).to(snake_case				)

									# forward pass
									with torch.no_grad():
																A         =					model(**snake_case				)

									# verify the logits
									A         =					torch.Size((1, 1_000)				)
									self.assertEqual(outputs.logits.shape      ,							snake_case				)
									A         =					torch.tensor([-0.3947, -0.4306, 0.0026]				).to(snake_case				)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							snake_case      ,							atol=1E-4				)				)








"""simple docstring"""







import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


A    =			logging.get_logger(__name__)

A    =			{'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

A    =			{
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

A    =			{
    'abeja/gpt-neox-japanese-2.7b': 2_0_4_8,
}

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							with open(lowerCamelCase__				,      'r'				,      encoding='utf-8'			) as f:
														A         =					json.loads(f.read()			)

							A         =					collections.OrderedDict()
							A         =					collections.OrderedDict()
							A         =					collections.OrderedDict()
							with open(lowerCamelCase__				,      'r'				,      encoding='utf-8'			) as f:
														A         =					f.readlines()
							A         =					[[t.rstrip('\n'			)] if (t == ',' or ',' not in t) else t.rstrip('\n'			).split(','			) for t in token]
							for idx, b in enumerate(lowerCamelCase__			):
														A         =					b
														A         =					idx
														for wd in b:
																					A         =					idx

							return vocab, raw_vocab, ids_to_tokens, emoji






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Optional[int] 							=     VOCAB_FILES_NAMES
		lowerCAmelCase_       :	Any 							=     PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_       :	Optional[Any] 							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_       :	str 							=     ["""input_ids""", """attention_mask"""]



		def __init__( self  :    Optional[Any]      ,							snake_case  :    Tuple      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]="<|endoftext|>"      ,							snake_case  :    List[str]="<|endoftext|>"      ,							snake_case  :    Any="<|startoftext|>"      ,							snake_case  :    Any="<|endoftext|>"      ,							snake_case  :    Tuple=False      ,							**snake_case  :    List[str]      ,							)			->						Optional[Any]:






									'''simple docstring'''

									super().__init__(
									    unk_token=snake_case      ,							pad_token=snake_case      ,							bos_token=snake_case      ,							eos_token=snake_case      ,							do_clean_text=snake_case      ,							**snake_case      ,							)
									if not os.path.isfile(snake_case				):
																raise ValueError(
																    f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
																    ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'				)
									if not os.path.isfile(snake_case				):
																raise ValueError(
																    f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
																    ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'				)
									A         =					do_clean_text
									A      ,  A      ,  A      ,  A         =					load_vocab_and_emoji(snake_case      ,							snake_case				)
									A         =					SubWordJapaneseTokenizer(
									    vocab=self.vocab      ,							ids_to_tokens=self.ids_to_tokens      ,							emoji=self.emoji				)



		@property
		def 					A_		( self  :    Any				)			->						List[str]:






									'''simple docstring'''

									return len(self.raw_vocab				)



		def 					A_		( self  :    str				)			->						str:






									'''simple docstring'''

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



		def 					A_		( self  :    List[str]      ,							snake_case  :    Tuple				)			->						Optional[Any]:






									'''simple docstring'''

									return self.subword_tokenizer.tokenize(snake_case      ,							clean=self.do_clean_text				)



		def 					A_		( self  :    int      ,							snake_case  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									return self.vocab.get(snake_case      ,							self.vocab.get(self.unk_token				)				)



		def 					A_		( self  :    int      ,							snake_case  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									return self.subword_tokenizer.convert_id_to_token(snake_case				)



		def 					A_		( self  :    str      ,							snake_case  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					''.join(snake_case				).strip()
									return out_string



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    "Conversation"				)			->						List[int]:






									'''simple docstring'''

									A         =					[]
									for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(snake_case      ,							add_special_tokens=snake_case				) + [self.eos_token_id]				)

									if len(snake_case				) > self.model_max_length:
																A         =					input_ids[-self.model_max_length :]
									return input_ids



		def 					A_		( self  :    int      ,							snake_case  :    str      ,							snake_case  :    Optional[str] = None				)			->						Tuple[str]:






									'''simple docstring'''

									A         =					0
									if os.path.isdir(snake_case				):
																A         =					os.path.join(
																    snake_case      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
																A         =					os.path.join(
																    snake_case      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']				)
									else:
																A         =					(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
																)
																A         =					(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
																)
									with open(snake_case      ,							'w'      ,							encoding='utf-8'				) as writer:
																for token_index, token in self.ids_to_tokens.items():
																							if index != token_index:
																														logger.warning(
																														    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																														    ' Please check that the vocabulary is not corrupted!'				)
																														A         =					token_index
																							writer.write(','.join(snake_case				) + '\n'				)
																							index += 1
									with open(snake_case      ,							'w'      ,							encoding='utf-8'				) as writer:
																json.dump(self.emoji      ,							snake_case				)
									return vocab_file, emoji_file






class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    str      ,							snake_case  :    Dict      ,							snake_case  :    Optional[Any]      ,							snake_case  :    List[Any]				)			->						int:






									'''simple docstring'''

									A         =					vocab  # same as swe
									A         =					ids_to_tokens  # same as bpe
									A         =					emoji
									A         =					np.max([len(snake_case				) for w in self.vocab.keys()]				)
									A         =					re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'				)
									A         =					re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'				)
									A         =					re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'				)
									A         =					re.compile(
									    r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'				)
									A         =					re.compile(
									    r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'				)
									A         =					re.compile(
									    r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'				)
									A         =					'─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
									A         =					'▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
									A         =					str.maketrans({k: '<BLOCK>' for k in keisen + blocks}				)



		def __len__( self  :    List[str]				)			->						List[str]:






									'''simple docstring'''

									return len(self.ids_to_tokens				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.content_repattera.sub('<URL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<EMAIL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<TEL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<DATE>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<DATE>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<PRICE>'      ,							snake_case				)
									A         =					content.translate(self.content_transa				)
									while "<BLOCK><BLOCK>" in content:
																A         =					content.replace('<BLOCK><BLOCK>'      ,							'<BLOCK>'				)
									return content



		def 					A_		( self  :    Any      ,							snake_case  :    int      ,							snake_case  :    Any=False				)			->						Any:






									'''simple docstring'''

									A         =					text.replace(' '      ,							'<SP>'				)
									A         =					text.replace('　'      ,							'<SP>'				)
									A         =					text.replace('\r\n'      ,							'<BR>'				)
									A         =					text.replace('\n'      ,							'<BR>'				)
									A         =					text.replace('\r'      ,							'<BR>'				)
									A         =					text.replace('\t'      ,							'<TAB>'				)
									A         =					text.replace('—'      ,							'ー'				)
									A         =					text.replace('−'      ,							'ー'				)
									for k, v in self.emoji["emoji"].items():
																if k in text:
																							A         =					text.replace(snake_case      ,							snake_case				)
									if clean:
																A         =					self.clean_text(snake_case				)

									def check_simbol(snake_case  :    Union[str, Any]				):
																A         =					x.encode()
																if len(snake_case				) == 1 and len(snake_case				) == 2:
																							A         =					(int(e[0]				) << 8) + int(e[1]				)
																							if (
																							    (c >= 0xc2a1 and c <= 0xc2bf)
																							    or (c >= 0xc780 and c <= 0xc783)
																							    or (c >= 0xcab9 and c <= 0xcbbf)
																							    or (c >= 0xcc80 and c <= 0xcda2)
																							):
																														return True
																return False

									def checkuae(snake_case  :    List[Any]				):
																A         =					x.encode()
																if len(snake_case				) == 1 and len(snake_case				) == 3:
																							A         =					(int(e[0]				) << 16) + (int(e[1]				) << 8) + int(e[2]				)
																							if c >= 0xe2_8080 and c <= 0xe2_b07f:
																														return True
																return False

									A         =					0
									A         =					[]
									while pos < len(snake_case				):
																A         =					min(len(snake_case				)      ,							pos + self.maxlen + 1				) if text[pos] == '<' else pos + 3
																A         =					[]  # (token_id, token, pos)
																for e in range(snake_case      ,							snake_case      ,							-1				):
																							A         =					text[pos:e]
																							if wd in self.vocab:
																														if wd[0] == "<" and len(snake_case				) > 2:
																																					A         =					[(self.vocab[wd], wd, e)]
																																					break
																														else:
																																					candidates.append((self.vocab[wd], wd, e)				)
																if len(snake_case				) > 0:
																							# the smallest token_id is adopted
																							A      ,  A      ,  A         =					sorted(snake_case      ,							key=lambda snake_case				: x[0]				)[0]
																							result.append(snake_case				)
																							A         =					e
																else:
																							A         =					pos + 1
																							A         =					text[pos:end]
																							if check_simbol(snake_case				):
																														result.append('<KIGOU>'				)
																							elif checkuae(snake_case				):
																														result.append('<U2000U2BFF>'				)
																							else:
																														for i in wd.encode('utf-8'				):
																																					result.append('<|byte%d|>' % i				)
																							A         =					end
									return result



		def 					A_		( self  :    List[str]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]="\n"				)			->						List[Any]:






									'''simple docstring'''

									A         =					[]
									A         =					[]
									A         =					self.ids_to_tokens[index][0]
									if word[:6] == "<|byte" and word[-2:] == "|>":
																byte_tokens.append(int(word[6:-2]				)				)
									else:
																if len(snake_case				) > 0:
																							words.append(bytearray(snake_case				).decode('utf-8'      ,							errors='replace'				)				)
																							A         =					[]
																if word[:7] == "<|emoji" and word[-2:] == "|>":
																							words.append(self.emoji['emoji_inv'][word]				)
																elif word == "<SP>":
																							words.append(' '				)
																elif word == "<BR>":
																							words.append(snake_case				)
																elif word == "<TAB>":
																							words.append('\t'				)
																elif word == "<BLOCK>":
																							words.append('▀'				)
																elif word == "<KIGOU>":
																							words.append('ǀ'				)
																elif word == "<U2000U2BFF>":
																							words.append('‖'				)
																else:
																							words.append(snake_case				)
									if len(snake_case				) > 0:
																words.append(bytearray(snake_case				).decode('utf-8'      ,							errors='replace'				)				)
									A         =					''.join(snake_case				)
									return text



