
'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

A			      =      {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
A			      =      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							Optional[Any]):
				for attribute in key.split('.'):
								lowerCamelCase					:			int									= getattr(UpperCAmelCase__       ,							UpperCAmelCase__)

				if weight_type is not None:
								lowerCamelCase					:			Any									= getattr(UpperCAmelCase__       ,							UpperCAmelCase__).shape
				else:
								lowerCamelCase					:			int									= hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
								    F''' {value.shape} for {full_name}''')

				if weight_type == "weight":
								lowerCamelCase					:			str									= value
				elif weight_type == "weight_g":
								lowerCamelCase					:			List[Any]									= value
				elif weight_type == "weight_v":
								lowerCamelCase					:			Dict									= value
				elif weight_type == "bias":
								lowerCamelCase					:			Tuple									= value
				elif weight_type == "running_mean":
								lowerCamelCase					:			List[str]									= value
				elif weight_type == "running_var":
								lowerCamelCase					:			Dict									= value
				elif weight_type == "num_batches_tracked":
								lowerCamelCase					:			Union[str, Any]									= value
				elif weight_type == "inv_freq":
								lowerCamelCase					:			Any									= value
				else:
								lowerCamelCase					:			List[Any]									= value

				logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''')





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Optional[int]):
				lowerCamelCase					:			str									= []
				lowerCamelCase					:			Optional[Any]									= fairseq_model.state_dict()

				lowerCamelCase					:			Optional[Any]									= hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCamelCase					:			Any									= False
								if "conv_layers" in name:
												load_conv_layer(
												    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							hf_model.config.feat_extract_norm == 'group'       ,							)
												lowerCamelCase					:			Tuple									= True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCamelCase					:			List[str]									= 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split('w2v_model.')[-1] == name.split('.')[0]:
																				lowerCamelCase					:			List[Any]									= True
																				if "*" in mapped_key:
																								lowerCamelCase					:			Optional[Any]									= name.split(UpperCAmelCase__)[0].split('.')[-2]
																								lowerCamelCase					:			Union[str, Any]									= mapped_key.replace('*'       ,							UpperCAmelCase__)
																				if "pos_bias_u" in name:
																								lowerCamelCase					:			Optional[int]									= None
																				elif "pos_bias_v" in name:
																								lowerCamelCase					:			List[str]									= None
																				elif "weight_g" in name:
																								lowerCamelCase					:			str									= 'weight_g'
																				elif "weight_v" in name:
																								lowerCamelCase					:			Union[str, Any]									= 'weight_v'
																				elif "bias" in name:
																								lowerCamelCase					:			str									= 'bias'
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowerCamelCase					:			List[str]									= 'weight'
																				elif "running_mean" in name:
																								lowerCamelCase					:			List[str]									= 'running_mean'
																				elif "inv_freq" in name:
																								lowerCamelCase					:			int									= 'inv_freq'
																				elif "running_var" in name:
																								lowerCamelCase					:			int									= 'running_var'
																				elif "num_batches_tracked" in name:
																								lowerCamelCase					:			Union[str, Any]									= 'num_batches_tracked'
																				else:
																								lowerCamelCase					:			str									= None
																				set_recursively(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
																continue
								if not is_used:
												unused_weights.append(UpperCAmelCase__)

				logger.warning(F'''Unused weights: {unused_weights}''')





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							str):
				lowerCamelCase					:			Dict									= full_name.split('conv_layers.')[-1]
				lowerCamelCase					:			int									= name.split('.')
				lowerCamelCase					:			Any									= int(items[0])
				lowerCamelCase					:			Dict									= int(items[1])

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''')
												lowerCamelCase					:			Optional[Any]									= value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''')
												lowerCamelCase					:			List[str]									= value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''')
												lowerCamelCase					:			Union[str, Any]									= value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''')
												lowerCamelCase					:			Union[str, Any]									= value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
				else:
								unused_weights.append(UpperCAmelCase__)





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Dict=None       ,							UpperCAmelCase__							:							Any=None       ,							UpperCAmelCase__							:							Optional[int]=True):
				if config_path is not None:
								lowerCamelCase					:			Union[str, Any]									= WavaVecaConformerConfig.from_pretrained(UpperCAmelCase__       ,							hidden_act='swish')
				else:
								lowerCamelCase					:			Tuple									= WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								lowerCamelCase					:			Optional[int]									= 'rotary'

				if is_finetuned:
								if dict_path:
												lowerCamelCase					:			Dict									= Dictionary.load(UpperCAmelCase__)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCamelCase					:			Tuple									= target_dict.pad_index
												lowerCamelCase					:			List[Any]									= target_dict.bos_index
												lowerCamelCase					:			str									= target_dict.eos_index
												lowerCamelCase					:			Dict									= len(target_dict.symbols)
												lowerCamelCase					:			Union[str, Any]									= os.path.join(UpperCAmelCase__       ,							'vocab.json')
												if not os.path.isdir(UpperCAmelCase__):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase__))
																return
												os.makedirs(UpperCAmelCase__       ,							exist_ok=UpperCAmelCase__)
												lowerCamelCase					:			Optional[int]									= target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowerCamelCase					:			Any									= 0
												lowerCamelCase					:			List[str]									= 1
												with open(UpperCAmelCase__       ,							'w'       ,							encoding='utf-8') as vocab_handle:
																json.dump(UpperCAmelCase__       ,							UpperCAmelCase__)
												lowerCamelCase					:			Optional[Any]									= WavaVecaCTCTokenizer(
												    UpperCAmelCase__       ,							unk_token=target_dict.unk_word       ,							pad_token=target_dict.pad_word       ,							bos_token=target_dict.bos_word       ,							eos_token=target_dict.eos_word       ,							word_delimiter_token='|'       ,							do_lower_case=UpperCAmelCase__       ,							)
												lowerCamelCase					:			int									= True if config.feat_extract_norm == 'layer' else False
												lowerCamelCase					:			List[str]									= WavaVecaFeatureExtractor(
												    feature_size=1       ,							sampling_rate=1_60_00       ,							padding_value=0       ,							do_normalize=UpperCAmelCase__       ,							return_attention_mask=UpperCAmelCase__       ,							)
												lowerCamelCase					:			int									= WavaVecaProcessor(feature_extractor=UpperCAmelCase__       ,							tokenizer=UpperCAmelCase__)
												processor.save_pretrained(UpperCAmelCase__)

								lowerCamelCase					:			int									= WavaVecaConformerForCTC(UpperCAmelCase__)
				else:
								lowerCamelCase					:			Union[str, Any]									= WavaVecaConformerForPreTraining(UpperCAmelCase__)

				if is_finetuned:
								lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			Optional[int]									= fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]       ,							arg_overrides={'data': '/'.join(dict_path.split('/')[:-1])})
				else:
								lowerCamelCase					:			Tuple									= argparse.Namespace(task='audio_pretraining')
								lowerCamelCase					:			Any									= fairseq.tasks.setup_task(UpperCAmelCase__)

								lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			Union[str, Any]									= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,							task=UpperCAmelCase__)

				lowerCamelCase					:			Optional[Any]									= model[0].eval()

				recursively_load_weights(UpperCAmelCase__       ,							UpperCAmelCase__       ,							not is_finetuned)

				hf_wavavec.save_pretrained(UpperCAmelCase__)


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
					parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					parser.add_argument(
					    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
					)
					A			      =      parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


'''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      )
