
'''simple docstring'''






import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
A			      =      logging.get_logger(__name__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any):
				print('Loading config file...')

				def flatten_yaml_as_dict(UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[Any]=""       ,							UpperCAmelCase__							:							Optional[Any]="."):
								lowerCamelCase					:			int									= []
								for k, v in d.items():
												lowerCamelCase					:			List[Any]									= parent_key + sep + k if parent_key else k
												if isinstance(UpperCAmelCase__       ,							collections.abc.MutableMapping):
																items.extend(flatten_yaml_as_dict(UpperCAmelCase__       ,							UpperCAmelCase__       ,							sep=UpperCAmelCase__).items())
												else:
																items.append((new_key, v))
								return dict(UpperCAmelCase__)

				lowerCamelCase					:			List[str]									= argparse.Namespace()
				with open(UpperCAmelCase__       ,							'r') as yaml_file:
								try:
												lowerCamelCase					:			int									= yaml.load(UpperCAmelCase__       ,							Loader=yaml.FullLoader)

												lowerCamelCase					:			Any									= flatten_yaml_as_dict(UpperCAmelCase__)
												for k, v in flat_cfg.items():
																setattr(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
								except yaml.YAMLError as exc:
												logger.error('Error while loading config file: {}. Error message: {}'.format(UpperCAmelCase__       ,							str(UpperCAmelCase__)))
				return config





def    UpperCAmelCase							(  UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							str):
				lowerCamelCase					:			Dict									= MobileViTVaConfig()

				lowerCamelCase					:			Tuple									= False

				# dataset
				if task_name.startswith('imagenet1k_'):
								lowerCamelCase					:			str									= 10_00
								if int(task_name.strip().split('_')[-1]) == 3_84:
												lowerCamelCase					:			List[Any]									= 3_84
								else:
												lowerCamelCase					:			Any									= 2_56
								lowerCamelCase					:			str									= 'imagenet-1k-id2label.json'
				elif task_name.startswith('imagenet21k_to_1k_'):
								lowerCamelCase					:			int									= 2_10_00
								if int(task_name.strip().split('_')[-1]) == 3_84:
												lowerCamelCase					:			Tuple									= 3_84
								else:
												lowerCamelCase					:			Optional[Any]									= 2_56
								lowerCamelCase					:			Dict									= 'imagenet-22k-id2label.json'
				elif task_name.startswith('ade20k_'):
								lowerCamelCase					:			List[Any]									= 1_51
								lowerCamelCase					:			Optional[Any]									= 5_12
								lowerCamelCase					:			Dict									= 'ade20k-id2label.json'
								lowerCamelCase					:			int									= True
				elif task_name.startswith('voc_'):
								lowerCamelCase					:			Tuple									= 21
								lowerCamelCase					:			List[Any]									= 5_12
								lowerCamelCase					:			Any									= 'pascal-voc-id2label.json'
								lowerCamelCase					:			Tuple									= True

				# orig_config
				lowerCamelCase					:			str									= load_orig_config_file(UpperCAmelCase__)
				assert getattr(UpperCAmelCase__       ,							'model.classification.name'       ,							-1) == "mobilevit_v2", "Invalid model"
				lowerCamelCase					:			Union[str, Any]									= getattr(UpperCAmelCase__       ,							'model.classification.mitv2.width_multiplier'       ,							1.0)
				assert (
				    getattr(UpperCAmelCase__       ,							'model.classification.mitv2.attn_norm_layer'       ,							-1) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				lowerCamelCase					:			List[Any]									= getattr(UpperCAmelCase__       ,							'model.classification.activation.name'       ,							'swish')
				# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

				if is_segmentation_model:
								lowerCamelCase					:			str									= getattr(UpperCAmelCase__       ,							'model.segmentation.output_stride'       ,							16)
								if "_deeplabv3" in task_name:
												lowerCamelCase					:			Any									= getattr(UpperCAmelCase__       ,							'model.segmentation.deeplabv3.aspp_rates'       ,							[12, 24, 36])
												lowerCamelCase					:			Tuple									= getattr(UpperCAmelCase__       ,							'model.segmentation.deeplabv3.aspp_out_channels'       ,							5_12)
												lowerCamelCase					:			Union[str, Any]									= getattr(UpperCAmelCase__       ,							'model.segmentation.deeplabv3.aspp_dropout'       ,							0.1)

    # id2label
				lowerCamelCase					:			List[str]									= 'huggingface/label-files'
				lowerCamelCase					:			List[str]									= json.load(open(hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__       ,							repo_type='dataset')       ,							'r'))
				lowerCamelCase					:			int									= {int(UpperCAmelCase__): v for k, v in idalabel.items()}
				lowerCamelCase					:			List[Any]									= idalabel
				lowerCamelCase					:			Union[str, Any]									= {v: k for k, v in idalabel.items()}

				return config





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							str):
				lowerCamelCase					:			str									= dct.pop(UpperCAmelCase__)
				lowerCamelCase					:			List[Any]									= val





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Tuple=False):
				if base_model:
								lowerCamelCase					:			Tuple									= ''
				else:
								lowerCamelCase					:			Optional[int]									= 'mobilevitv2.'

				lowerCamelCase					:			Optional[Any]									= []
				for k in state_dict.keys():
								if k[:8] == "encoder.":
												lowerCamelCase					:			Tuple									= k[8:]
								else:
												lowerCamelCase					:			Any									= k

								if ".block." in k:
												lowerCamelCase					:			List[Any]									= k_new.replace('.block.'       ,							'.')
								if ".conv." in k:
												lowerCamelCase					:			Tuple									= k_new.replace('.conv.'       ,							'.convolution.')
								if ".norm." in k:
												lowerCamelCase					:			Any									= k_new.replace('.norm.'       ,							'.normalization.')

								if "conv_1." in k:
												lowerCamelCase					:			str									= k_new.replace('conv_1.'       ,							F'''{model_prefix}conv_stem.''')
								for i in [1, 2]:
												if F'''layer_{i}.''' in k:
																lowerCamelCase					:			int									= k_new.replace(F'''layer_{i}.'''       ,							F'''{model_prefix}encoder.layer.{i-1}.layer.''')
								if ".exp_1x1." in k:
												lowerCamelCase					:			Dict									= k_new.replace('.exp_1x1.'       ,							'.expand_1x1.')
								if ".red_1x1." in k:
												lowerCamelCase					:			Tuple									= k_new.replace('.red_1x1.'       ,							'.reduce_1x1.')

								for i in [3, 4, 5]:
												if F'''layer_{i}.0.''' in k:
																lowerCamelCase					:			Tuple									= k_new.replace(F'''layer_{i}.0.'''       ,							F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''')
												if F'''layer_{i}.1.local_rep.0.''' in k:
																lowerCamelCase					:			Dict									= k_new.replace(F'''layer_{i}.1.local_rep.0.'''       ,							F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''')
												if F'''layer_{i}.1.local_rep.1.''' in k:
																lowerCamelCase					:			str									= k_new.replace(F'''layer_{i}.1.local_rep.1.'''       ,							F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''')

								for i in [3, 4, 5]:
												if i == 3:
																lowerCamelCase					:			Any									= [0, 1]
												elif i == 4:
																lowerCamelCase					:			Optional[Any]									= [0, 1, 2, 3]
												elif i == 5:
																lowerCamelCase					:			List[Any]									= [0, 1, 2]

												for j in j_in:
																if F'''layer_{i}.1.global_rep.{j}.''' in k:
																				lowerCamelCase					:			int									= k_new.replace(
																				    F'''layer_{i}.1.global_rep.{j}.'''       ,							F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''')
												if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
																lowerCamelCase					:			Any									= k_new.replace(
																    F'''layer_{i}.1.global_rep.{j+1}.'''       ,							F'''{model_prefix}encoder.layer.{i-1}.layernorm.''')

												if F'''layer_{i}.1.conv_proj.''' in k:
																lowerCamelCase					:			List[Any]									= k_new.replace(F'''layer_{i}.1.conv_proj.'''       ,							F'''{model_prefix}encoder.layer.{i-1}.conv_projection.''')

								if "pre_norm_attn.0." in k:
												lowerCamelCase					:			int									= k_new.replace('pre_norm_attn.0.'       ,							'layernorm_before.')
								if "pre_norm_attn.1." in k:
												lowerCamelCase					:			Dict									= k_new.replace('pre_norm_attn.1.'       ,							'attention.')
								if "pre_norm_ffn.0." in k:
												lowerCamelCase					:			List[Any]									= k_new.replace('pre_norm_ffn.0.'       ,							'layernorm_after.')
								if "pre_norm_ffn.1." in k:
												lowerCamelCase					:			Optional[Any]									= k_new.replace('pre_norm_ffn.1.'       ,							'ffn.conv1.')
								if "pre_norm_ffn.3." in k:
												lowerCamelCase					:			Optional[Any]									= k_new.replace('pre_norm_ffn.3.'       ,							'ffn.conv2.')

								if "classifier.1." in k:
												lowerCamelCase					:			str									= k_new.replace('classifier.1.'       ,							'classifier.')

								if "seg_head." in k:
												lowerCamelCase					:			Optional[Any]									= k_new.replace('seg_head.'       ,							'segmentation_head.')
								if ".aspp_layer." in k:
												lowerCamelCase					:			List[str]									= k_new.replace('.aspp_layer.'       ,							'.')
								if ".aspp_pool." in k:
												lowerCamelCase					:			List[str]									= k_new.replace('.aspp_pool.'       ,							'.')

								rename_keys.append((k, k_new))
				return rename_keys





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]):
				lowerCamelCase					:			Union[str, Any]									= []
				for k in state_dict.keys():
								if k.startswith('seg_head.aux_head.'):
												keys_to_ignore.append(UpperCAmelCase__)
				for k in keys_to_ignore:
								state_dict.pop(UpperCAmelCase__       ,							UpperCAmelCase__)





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Dict									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
				lowerCamelCase					:			Union[str, Any]									= Image.open(requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__).raw)
				return im





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			Optional[Any]									= get_mobilevitva_config(UpperCAmelCase__       ,							UpperCAmelCase__)

				# load original state_dict
				lowerCamelCase					:			Dict									= torch.load(UpperCAmelCase__       ,							map_location='cpu')

				# load huggingface model
				if task_name.startswith('ade20k_') or task_name.startswith('voc_'):
								lowerCamelCase					:			Optional[Any]									= MobileViTVaForSemanticSegmentation(UpperCAmelCase__).eval()
								lowerCamelCase					:			List[Any]									= False
				else:
								lowerCamelCase					:			Any									= MobileViTVaForImageClassification(UpperCAmelCase__).eval()
								lowerCamelCase					:			Union[str, Any]									= False

				# remove and rename some keys of load the original model
				lowerCamelCase					:			Optional[int]									= checkpoint
				remove_unused_keys(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= create_rename_keys(UpperCAmelCase__       ,							base_model=UpperCAmelCase__)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)

				# load modified state_dict
				model.load_state_dict(UpperCAmelCase__)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				lowerCamelCase					:			List[Any]									= MobileViTImageProcessor(crop_size=config.image_size       ,							size=config.image_size + 32)
				lowerCamelCase					:			Dict									= image_processor(images=prepare_img()       ,							return_tensors='pt')
				lowerCamelCase					:			Optional[int]									= model(**UpperCAmelCase__)

				# verify classification model
				if task_name.startswith('imagenet'):
								lowerCamelCase					:			Dict									= outputs.logits
								lowerCamelCase					:			List[Any]									= logits.argmax(-1).item()
								print('Predicted class:'       ,							model.config.idalabel[predicted_class_idx])
								if task_name.startswith('imagenet1k_256') and config.width_multiplier == 1.0:
												# expected_logits for base variant
												lowerCamelCase					:			List[str]									= torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1])
												assert torch.allclose(logits[0, :3]       ,							UpperCAmelCase__       ,							atol=1E-4)

				Path(UpperCAmelCase__).mkdir(exist_ok=UpperCAmelCase__)
				print(F'''Saving model {task_name} to {pytorch_dump_folder_path}''')
				model.save_pretrained(UpperCAmelCase__)
				print(F'''Saving image processor to {pytorch_dump_folder_path}''')
				image_processor.save_pretrained(UpperCAmelCase__)


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--task',
					    default='imagenet1k_256',
					    type=str,
					    help=(
					        'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
					        '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
					    ),
					    choices=[
					        'imagenet1k_256',
					        'imagenet1k_384',
					        'imagenet21k_to_1k_256',
					        'imagenet21k_to_1k_384',
					        'ade20k_deeplabv3',
					        'voc_deeplabv3',
					    ],
					)

					parser.add_argument(
					    '--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
					)
					parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
					parser.add_argument(
					    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
					)

					A			      =      parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)


'''simple docstring'''






import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	BertTokenizer
					_lowerCAmelCase			     =	BertTokenizerFast
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	filter_non_english
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().setUp()

									lowerCamelCase					:			str									= [
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									lowerCamelCase					:			Any									= os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES['vocab_file']      )
									with open(self.vocab_file,    'w',    encoding='utf-8'      ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= 'UNwant\u00E9d,running'
									lowerCamelCase					:			List[str]									= 'unwanted, running'
									return input_text, output_text
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.tokenizer_class(self.vocab_file      )

									lowerCamelCase					:			List[str]									= tokenizer.tokenize('UNwant\u00E9d,running'      )
									self.assertListEqual(A,    ['un', '##want', '##ed', ',', 'runn', '##ing']      )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(A      ),    [9, 6, 7, 12, 10, 11]      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if not self.test_rust_tokenizer:
													return

									lowerCamelCase					:			Dict									= self.get_tokenizer()
									lowerCamelCase					:			Optional[Any]									= self.get_rust_tokenizer()

									lowerCamelCase					:			Dict									= 'UNwant\u00E9d,running'

									lowerCamelCase					:			Tuple									= tokenizer.tokenize(A      )
									lowerCamelCase					:			Optional[int]									= rust_tokenizer.tokenize(A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= tokenizer.encode(A,    add_special_tokens=A      )
									lowerCamelCase					:			List[Any]									= rust_tokenizer.encode(A,    add_special_tokens=A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= self.get_rust_tokenizer()
									lowerCamelCase					:			Optional[Any]									= tokenizer.encode(A      )
									lowerCamelCase					:			Tuple									= rust_tokenizer.encode(A      )
									self.assertListEqual(A,    A      )

									# With lower casing
									lowerCamelCase					:			Union[str, Any]									= self.get_tokenizer(do_lower_case=A      )
									lowerCamelCase					:			List[Any]									= self.get_rust_tokenizer(do_lower_case=A      )

									lowerCamelCase					:			Optional[int]									= 'UNwant\u00E9d,running'

									lowerCamelCase					:			Optional[Any]									= tokenizer.tokenize(A      )
									lowerCamelCase					:			str									= rust_tokenizer.tokenize(A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= tokenizer.encode(A,    add_special_tokens=A      )
									lowerCamelCase					:			Union[str, Any]									= rust_tokenizer.encode(A,    add_special_tokens=A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= self.get_rust_tokenizer()
									lowerCamelCase					:			Any									= tokenizer.encode(A      )
									lowerCamelCase					:			Dict									= rust_tokenizer.encode(A      )
									self.assertListEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= BasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      ),    ['ah', '\u535A', '\u63A8', 'zz']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BasicTokenizer(do_lower_case=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      ),    ['hello', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['hello']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['hällo', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['h\u00E9llo']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['hallo', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['hello']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= BasicTokenizer(do_lower_case=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['hallo', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['hello']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= BasicTokenizer(do_lower_case=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      ),    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= BasicTokenizer(do_lower_case=A,    never_split=['[UNK]']      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      ),    ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= BasicTokenizer()
									lowerCamelCase					:			Optional[Any]									= 'a\n\'ll !!to?\'d of, can\'t.'
									lowerCamelCase					:			int									= ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
									self.assertListEqual(tokenizer.tokenize(A      ),    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

									lowerCamelCase					:			Optional[int]									= {}
									for i, token in enumerate(A      ):
													lowerCamelCase					:			int									= i
									lowerCamelCase					:			List[Any]									= WordpieceTokenizer(vocab=A,    unk_token='[UNK]'      )

									self.assertListEqual(tokenizer.tokenize(''      ),    []      )

									self.assertListEqual(tokenizer.tokenize('unwanted running'      ),    ['un', '##want', '##ed', 'runn', '##ing']      )

									self.assertListEqual(tokenizer.tokenize('unwantedX running'      ),    ['[UNK]', 'runn', '##ing']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertTrue(_is_whitespace(' '      )      )
									self.assertTrue(_is_whitespace('\t'      )      )
									self.assertTrue(_is_whitespace('\r'      )      )
									self.assertTrue(_is_whitespace('\n'      )      )
									self.assertTrue(_is_whitespace('\u00A0'      )      )

									self.assertFalse(_is_whitespace('A'      )      )
									self.assertFalse(_is_whitespace('-'      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertTrue(_is_control('\u0005'      )      )

									self.assertFalse(_is_control('A'      )      )
									self.assertFalse(_is_control(' '      )      )
									self.assertFalse(_is_control('\t'      )      )
									self.assertFalse(_is_control('\r'      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertTrue(_is_punctuation('-'      )      )
									self.assertTrue(_is_punctuation('$'      )      )
									self.assertTrue(_is_punctuation('`'      )      )
									self.assertTrue(_is_punctuation('.'      )      )

									self.assertFalse(_is_punctuation('A'      )      )
									self.assertFalse(_is_punctuation(' '      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.get_tokenizer()
									lowerCamelCase					:			Optional[int]									= self.get_rust_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(A      ) for t in ['Test', '\xad', 'test']],    [['[UNK]'], [], ['[UNK]']]      )

									self.assertListEqual(
									    [rust_tokenizer.tokenize(A      ) for t in ['Test', '\xad', 'test']],    [['[UNK]'], [], ['[UNK]']]      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.tokenizer_class.from_pretrained('bert-base-uncased'      )

									lowerCamelCase					:			Any									= tokenizer.encode('sequence builders',    add_special_tokens=A      )
									lowerCamelCase					:			Tuple									= tokenizer.encode('multi-sequence build',    add_special_tokens=A      )

									lowerCamelCase					:			List[Any]									= tokenizer.build_inputs_with_special_tokens(A      )
									lowerCamelCase					:			Optional[int]									= tokenizer.build_inputs_with_special_tokens(A,    A      )

									assert encoded_sentence == [101] + text + [102]
									assert encoded_pair == [101] + text + [102] + text_a + [102]
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																	lowerCamelCase					:			Optional[Any]									= self.rust_tokenizer_class.from_pretrained(A,    **A      )

																	lowerCamelCase					:			Optional[int]									= F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
																	lowerCamelCase					:			List[str]									= tokenizer_r.encode_plus(
																	    A,    return_attention_mask=A,    return_token_type_ids=A,    return_offsets_mapping=A,    add_special_tokens=A,    )

																	lowerCamelCase					:			int									= tokenizer_r.do_lower_case if hasattr(A,    'do_lower_case'      ) else False
																	lowerCamelCase					:			Union[str, Any]									= (
																	    [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'A'),
																	        ((1, 2), ','),
																	        ((3, 5), 'na'),
																	        ((5, 6), '##ï'),
																	        ((6, 8), '##ve'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'Allen'),
																	        ((21, 23), '##NL'),
																	        ((23, 24), '##P'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	    if not do_lower_case
																	    else [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'a'),
																	        ((1, 2), ','),
																	        ((3, 8), 'naive'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'allen'),
																	        ((21, 23), '##nl'),
																	        ((23, 24), '##p'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	)

																	self.assertEqual(
																	    [e[1] for e in expected_results],    tokenizer_r.convert_ids_to_tokens(tokens['input_ids']      )      )
																	self.assertEqual([e[0] for e in expected_results],    tokens['offset_mapping']      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= ['的', '人', '有']
									lowerCamelCase					:			List[str]									= ''.join(A      )
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																	lowerCamelCase					:			Dict									= True
																	lowerCamelCase					:			Tuple									= self.tokenizer_class.from_pretrained(A,    **A      )
																	lowerCamelCase					:			int									= self.rust_tokenizer_class.from_pretrained(A,    **A      )

																	lowerCamelCase					:			Union[str, Any]									= tokenizer_p.encode(A,    add_special_tokens=A      )
																	lowerCamelCase					:			Dict									= tokenizer_r.encode(A,    add_special_tokens=A      )

																	lowerCamelCase					:			Optional[Any]									= tokenizer_r.convert_ids_to_tokens(A      )
																	lowerCamelCase					:			str									= tokenizer_p.convert_ids_to_tokens(A      )

																	# it is expected that each Chinese character is not preceded by "##"
																	self.assertListEqual(A,    A      )
																	self.assertListEqual(A,    A      )

																	lowerCamelCase					:			int									= False
																	lowerCamelCase					:			int									= self.rust_tokenizer_class.from_pretrained(A,    **A      )
																	lowerCamelCase					:			Tuple									= self.tokenizer_class.from_pretrained(A,    **A      )

																	lowerCamelCase					:			int									= tokenizer_r.encode(A,    add_special_tokens=A      )
																	lowerCamelCase					:			List[str]									= tokenizer_p.encode(A,    add_special_tokens=A      )

																	lowerCamelCase					:			Optional[Any]									= tokenizer_r.convert_ids_to_tokens(A      )
																	lowerCamelCase					:			Tuple									= tokenizer_p.convert_ids_to_tokens(A      )

																	# it is expected that only the first Chinese character is not preceded by "##".
																	lowerCamelCase					:			Tuple									= [
																	    F'''##{token}''' if idx != 0 else token for idx, token in enumerate(A      )
																	]
																	self.assertListEqual(A,    A      )
																	self.assertListEqual(A,    A      )
