code: 
  
  
  # Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  ####################################################################################################
  
  #
  # Note: If when running this conversion script you're getting an exception:
  #     ModuleNotFoundError: No module named 'megatron.model.enums'
  # you need to tell python where to find the clone of Megatron-LM, e.g.:
  #
  # cd /tmp
  # git clone https://github.com/NVIDIA/Megatron-LM
  # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
  #
  # if you already have it cloned elsewhere, simply adjust the path to the existing path
  #
  # If the training was done using a Megatron-LM fork, e.g.,
  # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
  # in your path, i.e., /path/to/Megatron-DeepSpeed/
  #
  
  import argparse
  import os
  import re
  import zipfile
  
  import torch
  
  from transformers import MegatronBertConfig
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__=0			)		->			Dict:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if name is None:
  				__lowercase=    None
  		else:
  				__lowercase=    '.' * max(0     ,				spaces - 2			) + '# {:' + str(5_0 - spaces			) + 's}'
  				__lowercase=    fmt.format(lowercase__			)
  
  		# Print and recurse (if needed).
  		if isinstance(lowercase__     ,				lowercase__			):
  				if msg is not None:
  						print(lowercase__			)
  				for k in val.keys():
  						recursive_print(lowercase__     ,				val[k]     ,				spaces + 2			)
  		elif isinstance(lowercase__     ,				torch.Tensor			):
  				print(lowercase__     ,				':'     ,				val.size()			)
  		else:
  				print(lowercase__     ,				':'     ,				lowercase__			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    param.size()
  		if checkpoint_version == 1.0:
  				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
  				__lowercase=    (num_heads, hidden_size, num_splits) + input_shape[1:]
  				__lowercase=    param.view(*lowercase__			)
  				__lowercase=    param.transpose(0     ,				2			)
  				__lowercase=    param.transpose(1     ,				2			).contiguous()
  		elif checkpoint_version >= 2.0:
  				# other versions store [num_heads * num_splits * hidden_size, :]
  				__lowercase=    (num_heads, num_splits, hidden_size) + input_shape[1:]
  				__lowercase=    param.view(*lowercase__			)
  				__lowercase=    param.transpose(0     ,				1			).contiguous()
  		__lowercase=    param.view(*lowercase__			)
  		return param
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			Optional[int]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    {}
  
  		# old versions did not store training args
  		__lowercase=    input_state_dict.get('args'     ,				lowercase__			)
  		if ds_args is not None:
  				# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
  				# from pprint import pprint
  				# pprint(vars(ds_args))
  
  				__lowercase=    ds_args.tokenizer_type
  				__lowercase=    ds_args.padded_vocab_size
  				__lowercase=    ds_args.max_position_embeddings
  				__lowercase=    ds_args.hidden_size
  				__lowercase=    ds_args.num_layers
  				__lowercase=    ds_args.num_attention_heads
  				__lowercase=    ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
  				# pprint(config)
  
  		# The number of heads.
  		__lowercase=    config.num_attention_heads
  		# The hidden_size per head.
  		__lowercase=    config.hidden_size // heads
  		# Megatron-LM checkpoint version
  		if "checkpoint_version" in input_state_dict.keys():
  				__lowercase=    input_state_dict['checkpoint_version']
  		else:
  				__lowercase=    0.0
  
  		# The model.
  		__lowercase=    input_state_dict['model']
  		# The language model.
  		__lowercase=    model['language_model']
  		# The embeddings.
  		__lowercase=    lm['embedding']
  
  		# The word embeddings.
  		__lowercase=    embeddings['word_embeddings']['weight']
  		# Truncate the embedding table to vocab_size rows.
  		__lowercase=    word_embeddings[: config.vocab_size, :]
  		# Store the word embeddings.
  		__lowercase=    word_embeddings
  
  		# The position embeddings.
  		__lowercase=    embeddings['position_embeddings']['weight']
  		assert pos_embeddings.size(0			) == config.max_position_embeddings and pos_embeddings.size(1			) == config.hidden_size
  		# Store the position embeddings.
  		__lowercase=    pos_embeddings
  
  		# The token-type embeddings.
  		__lowercase=    embeddings['tokentype_embeddings']['weight']
  		# Store the position embeddings.
  		__lowercase=    tokentype_embeddings
  
  		# The transformer.
  		__lowercase=    lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
  
  		# The regex to extract layer names.
  		__lowercase=    re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'			)
  
  		# The simple map of names for "automated" rules.
  		__lowercase=    {
  		    'attention.dense': '.attention.output.dense.',
  		    'self_attention.dense': '.attention.output.dense.',
  		    'mlp.dense_h_to_4h': '.intermediate.dense.',
  		    'mlp.dense_4h_to_h': '.output.dense.',
  		}
  
  		# Keep track of the attention/query/value tensor.
  		__lowercase=    None
  
  		# Extract the layers.
  		for key, val in transformer.items():
  				# Match the name.
  				__lowercase=    layer_re.match(lowercase__			)
  
  				# Stop if that's not a layer
  				if m is None:
  						break
  
  				# The index of the layer.
  				__lowercase=    int(m.group(1			)			)
  				# The name of the operation.
  				__lowercase=    m.group(2			)
  				# Is it a weight or a bias?
  				__lowercase=    m.group(3			)
  
  				# The name of the layer.
  				__lowercase=    F'bert.encoder.layer.{layer_idx}'
  
  				# For layernorm(s), simply store the layer norm.
  				if op_name.endswith('layernorm'			):
  						__lowercase=    'attention.ln' if op_name.startswith('input'			) else 'ln'
  						__lowercase=    val
  
  				# Transpose the QKV matrix.
  				elif (
  				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  				) and weight_or_bias == "weight":
  						# Make sure the QKV pointer is nil.
  						assert attention_qkv_weight is None, ""
  
  						__lowercase=    fix_query_key_value_ordering(lowercase__     ,				lowercase__     ,				3     ,				lowercase__     ,				lowercase__			)
  						# Store the tensor as we need the bias as well to interleave QKV and biases.
  						__lowercase=    out_val
  
  				# Transpose the bias.
  				elif (
  				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  				) and weight_or_bias == "bias":
  						# Make sure we read the weight tensor.
  						assert attention_qkv_weight is not None, ""
  
  						# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
  						__lowercase=    attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
  						__lowercase=    attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
  						__lowercase=    attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
  						__lowercase=    fix_query_key_value_ordering(lowercase__     ,				lowercase__     ,				3     ,				lowercase__     ,				lowercase__			)
  						# Split the bias.
  						__lowercase=    out_val[0 * config.hidden_size : 1 * config.hidden_size]
  						__lowercase=    out_val[1 * config.hidden_size : 2 * config.hidden_size]
  						__lowercase=    out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
  						# Store.
  						__lowercase=    q
  						__lowercase=    q_bias
  						__lowercase=    k
  						__lowercase=    k_bias
  						__lowercase=    v
  						__lowercase=    v_bias
  
  						# Clear the stored tensor.
  						__lowercase=    None
  
  				# Copy weights and biases as is.
  				elif weight_or_bias in ["weight", "bias"]:
  						__lowercase=    megatron_to_transformers[op_name]
  						__lowercase=    val
  
      # The final layernorm.
  		__lowercase=    transformer['final_layernorm.weight']
  		__lowercase=    transformer['final_layernorm.bias']
  
  		# The pooler.
  		__lowercase=    lm['pooler']
  
  		# Store the matrix and the bias.
  		__lowercase=    pooler['dense.weight']
  		__lowercase=    pooler['dense.bias']
  
  		# The LM head from Megatron (for RACE).
  		__lowercase=    model['lm_head']
  
  		# The transform matrix.
  		__lowercase=    lm_head['dense.weight']
  		__lowercase=    lm_head['dense.bias']
  
  		# The transform LN.
  		__lowercase=    lm_head['layernorm.weight']
  		__lowercase=    lm_head['layernorm.bias']
  
  		# For the decoder, we replicate the weights.
  		__lowercase=    word_embeddings
  		__lowercase=    lm_head['bias']
  
  		# The classifier from Megatron (for MLNI).
  		__lowercase=    model['binary_head']
  
  		# Store the classifier.
  		__lowercase=    binary_head['weight']
  		__lowercase=    binary_head['bias']
  
  		# It should be done!
  		return output_state_dict
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    argparse.ArgumentParser()
  		parser.add_argument('--print-checkpoint-structure'     ,				action='store_true'			)
  		parser.add_argument('path_to_checkpoint'     ,				type=lowercase__     ,				help='Path to the ZIP file containing the checkpoint'			)
  		parser.add_argument(
  		    '--config_file'     ,				default=''     ,				type=lowercase__     ,				help='An optional config json file describing the pre-trained model.'     ,				)
  		__lowercase=    parser.parse_args()
  
  		# Extract the basename.
  		__lowercase=    os.path.dirname(args.path_to_checkpoint			)
  
  		# Load the model.
  		# the .zip is very optional, let's keep it for backward compatibility
  		print(F'Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'			)
  		if args.path_to_checkpoint.endswith('.zip'			):
  				with zipfile.ZipFile(args.path_to_checkpoint     ,				'r'			) as checkpoint:
  						with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'			) as pytorch_dict:
  								__lowercase=    torch.load(lowercase__     ,				map_location='cpu'			)
  		else:
  				__lowercase=    torch.load(args.path_to_checkpoint     ,				map_location='cpu'			)
  
  		if args.config_file == "":
  				# Default config of megatron-bert 345m
  				__lowercase=    MegatronBertConfig()
  
  				# different megatron-bert-*-345m models have different vocab sizes, so override the default
  				# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
  				__lowercase=    input_state_dict['model']['lm_head']['bias'].numel()
  		else:
  				__lowercase=    MegatronBertConfig.from_json_file(args.config_file			)
  
  		# Convert.
  		print('Converting'			)
  		__lowercase=    convert_megatron_checkpoint(lowercase__     ,				lowercase__     ,				lowercase__			)
  
  		# Print the structure of converted state dict.
  		if args.print_checkpoint_structure:
  				recursive_print(lowercase__     ,				lowercase__			)
  
  		# Store the config to file.
  		print('Saving config'			)
  		config.save_pretrained(lowercase__			)
  
  		# Store the state_dict to file.
  		__lowercase=    os.path.join(lowercase__     ,				'pytorch_model.bin'			)
  		print(F'Saving checkpoint to "{output_checkpoint_file}"'			)
  		torch.save(lowercase__     ,				lowercase__			)
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
  	main()
  
  ####################################################################################################
  
  
  
  
  
  
  
style_context: 
  
  
  import os
  import unittest
  
  from transformers import ElectraTokenizerFast
  from transformers.models.electra.tokenization_electra import (
      VOCAB_FILES_NAMES,
      BasicTokenizer,
      ElectraTokenizer,
      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    A					(     A_      ,       unittest.TestCase				):
  							UpperCamelCase_     :							Tuple =ElectraTokenizer
  							UpperCamelCase_     :							Tuple =ElectraTokenizerFast
  							UpperCamelCase_     :							Any =True
  							UpperCamelCase_     :							List[str] =True
  							UpperCamelCase_     :							Any =filter_non_english
  
  
  							def     _A (self      ):
  									super().setUp()
  
  									__lowercase=    [
  									    '[UNK]',
  									    '[CLS]',
  									    '[SEP]',
  									    '[PAD]',
  									    '[MASK]',
  									    'want',
  									    '##want',
  									    '##ed',
  									    'wa',
  									    'un',
  									    'runn',
  									    '##ing',
  									    ',',
  									    'low',
  									    'lowest',
  									]
  									__lowercase=    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     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    'UNwant\u00E9d,running'
  									__lowercase=    'unwanted, running'
  									return input_text, output_text
  
  
  							def     _A (self      ):
  									__lowercase=    self.tokenizer_class(self.vocab_file      )
  
  									__lowercase=    tokenizer.tokenize('UNwant\u00E9d,running'      )
  									self.assertListEqual(lowerCAmelCase   ,       ['un', '##want', '##ed', ',', 'runn', '##ing']      )
  									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase      )   ,       [9, 6, 7, 1_2, 1_0, 1_1]      )
  
  
  							def     _A (self      ):
  									if not self.test_rust_tokenizer:
  											return
  
  									__lowercase=    self.get_tokenizer()
  									__lowercase=    self.get_rust_tokenizer()
  
  									__lowercase=    'UNwant\u00E9d,running'
  
  									__lowercase=    tokenizer.tokenize(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.tokenize(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    self.get_rust_tokenizer()
  									__lowercase=    tokenizer.encode(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									# With lower casing
  									__lowercase=    self.get_tokenizer(do_lower_case=lowerCAmelCase      )
  									__lowercase=    self.get_rust_tokenizer(do_lower_case=lowerCAmelCase      )
  
  									__lowercase=    'UNwant\u00E9d,running'
  
  									__lowercase=    tokenizer.tokenize(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.tokenize(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									__lowercase=    self.get_rust_tokenizer()
  									__lowercase=    tokenizer.encode(lowerCAmelCase      )
  									__lowercase=    rust_tokenizer.encode(lowerCAmelCase      )
  									self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer()
  
  									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      )   ,       ['ah', '\u535A', '\u63A8', 'zz']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )   ,       ['hello', '!', 'how', 'are', 'you', '?']      )
  									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )   ,       ['hello']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       strip_accents=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )   ,       ['hällo', '!', 'how', 'are', 'you', '?']      )
  									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )   ,       ['h\u00E9llo']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       strip_accents=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )   ,       ['hallo', '!', 'how', 'are', 'you', '?']      )
  									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )   ,       ['hello']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )   ,       ['hallo', '!', 'how', 'are', 'you', '?']      )
  									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )   ,       ['hello']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )   ,       ['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       strip_accents=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )   ,       ['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       strip_accents=lowerCAmelCase      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )   ,       ['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )
  
  
  							def     _A (self      ):
  									__lowercase=    BasicTokenizer(do_lower_case=lowerCAmelCase   ,       never_split=['[UNK]']      )
  
  									self.assertListEqual(
  									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      )   ,       ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )
  
  
  							def     _A (self      ):
  									__lowercase=    ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
  
  									__lowercase=    {}
  									for i, token in enumerate(lowerCAmelCase      ):
  											__lowercase=    i
  									__lowercase=    WordpieceTokenizer(vocab=lowerCAmelCase   ,       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     _A (self      ):
  									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     _A (self      ):
  									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     _A (self      ):
  									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     _A (self      ):
  									__lowercase=    self.get_tokenizer()
  									__lowercase=    self.get_rust_tokenizer()
  
  									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
  									self.assertListEqual([tokenizer.tokenize(lowerCAmelCase      ) for t in ['Test', '\xad', 'test']]   ,       [['[UNK]'], [], ['[UNK]']]      )
  
  									self.assertListEqual(
  									    [rust_tokenizer.tokenize(lowerCAmelCase      ) for t in ['Test', '\xad', 'test']]   ,       [['[UNK]'], [], ['[UNK]']]      )
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    self.tokenizer_class.from_pretrained('google/electra-base-discriminator'      )
  
  									__lowercase=    tokenizer.encode('sequence builders'   ,       add_special_tokens=lowerCAmelCase      )
  									__lowercase=    tokenizer.encode('multi-sequence build'   ,       add_special_tokens=lowerCAmelCase      )
  
  									__lowercase=    tokenizer.build_inputs_with_special_tokens(lowerCAmelCase      )
  									__lowercase=    tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									assert encoded_sentence == [1_0_1] + text + [1_0_2]
  									assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]
  
  
  							def     _A (self      ):
  									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
  													__lowercase=    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  
  													__lowercase=    f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
  													__lowercase=    tokenizer_r.encode_plus(
  													    lowerCAmelCase   ,       return_attention_mask=lowerCAmelCase   ,       return_token_type_ids=lowerCAmelCase   ,       return_offsets_mapping=lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase   ,       )
  
  													__lowercase=    tokenizer_r.do_lower_case if hasattr(lowerCAmelCase   ,       'do_lower_case'      ) else False
  													__lowercase=    (
  													    [
  													        ((0, 0), tokenizer_r.cls_token),
  													        ((0, 1), 'A'),
  													        ((1, 2), ','),
  													        ((3, 5), 'na'),
  													        ((5, 6), '##ï'),
  													        ((6, 8), '##ve'),
  													        ((9, 1_5), tokenizer_r.mask_token),
  													        ((1_6, 2_1), 'Allen'),
  													        ((2_1, 2_3), '##NL'),
  													        ((2_3, 2_4), '##P'),
  													        ((2_5, 3_3), 'sentence'),
  													        ((3_3, 3_4), '.'),
  													        ((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, 1_5), tokenizer_r.mask_token),
  													        ((1_6, 2_1), 'allen'),
  													        ((2_1, 2_3), '##nl'),
  													        ((2_3, 2_4), '##p'),
  													        ((2_5, 3_3), 'sentence'),
  													        ((3_3, 3_4), '.'),
  													        ((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     _A (self      ):
  									__lowercase=    ['的', '人', '有']
  									__lowercase=    ''.join(lowerCAmelCase      )
  									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
  													__lowercase=    True
  													__lowercase=    self.tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  													__lowercase=    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  
  													__lowercase=    tokenizer_p.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  													__lowercase=    tokenizer_r.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  
  													__lowercase=    tokenizer_r.convert_ids_to_tokens(lowerCAmelCase      )
  													__lowercase=    tokenizer_p.convert_ids_to_tokens(lowerCAmelCase      )
  
  													# it is expected that each Chinese character is not preceded by "##"
  													self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  													self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  													__lowercase=    False
  													__lowercase=    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  													__lowercase=    self.tokenizer_class.from_pretrained(lowerCAmelCase   ,       **lowerCAmelCase      )
  
  													__lowercase=    tokenizer_r.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  													__lowercase=    tokenizer_p.encode(lowerCAmelCase   ,       add_special_tokens=lowerCAmelCase      )
  
  													__lowercase=    tokenizer_r.convert_ids_to_tokens(lowerCAmelCase      )
  													__lowercase=    tokenizer_p.convert_ids_to_tokens(lowerCAmelCase      )
  
  													# it is expected that only the first Chinese character is not preceded by "##".
  													__lowercase=    [
  													    f'##{token}' if idx != 0 else token for idx, token in enumerate(lowerCAmelCase      )
  													]
  													self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  													self.assertListEqual(lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  
  
  
  
  