





import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__magic_name__   :						Dict    =			getLogger(__name__)


__magic_name__   :						List[str]    =			"""cuda""" if torch.cuda.is_available() else """cpu"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 8					,	SCREAMING_SNAKE_CASE__ = DEFAULT_DEVICE					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__="summarization"					,	SCREAMING_SNAKE_CASE__=None					,	**SCREAMING_SNAKE_CASE__					,	):







  '''simple docstring'''



  _snake_case     		=				Path(SCREAMING_SNAKE_CASE__       ).open("w"					,	encoding="utf-8"       )
  _snake_case     		=				str(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
  if fpaa:
    _snake_case     		=				model.half()

  _snake_case     		=				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
  logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''       )  # if this is wrong, check config.model_type.

  _snake_case     		=				time.time()
  # update config with task specific params
  use_task_specific_params(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  if prefix is None:
    _snake_case     		=				prefix or getattr(model.config					,	"prefix"					,	""       ) or ""
  for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )       )       ):
    _snake_case     		=				[prefix + text for text in examples_chunk]
    _snake_case     		=				tokenizer(SCREAMING_SNAKE_CASE__					,	return_tensors="pt"					,	truncation=SCREAMING_SNAKE_CASE__					,	padding="longest"       ).to(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				model.generate(
        input_ids=batch.input_ids					,	attention_mask=batch.attention_mask					,	**SCREAMING_SNAKE_CASE__					,	)
    _snake_case     		=				tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					,	skip_special_tokens=SCREAMING_SNAKE_CASE__					,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__       )
    for hypothesis in dec:
      fout.write(hypothesis + "\n"       )
      fout.flush()
  fout.close()
  _snake_case     		=				int(time.time() - start_time       )  # seconds
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					,	4       )}




def 				snake_case_      (		):







  '''simple docstring'''



  return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__=True       ):







  '''simple docstring'''



  _snake_case     		=				argparse.ArgumentParser()
  parser.add_argument("model_name"					,	type=SCREAMING_SNAKE_CASE__					,	help="like facebook/bart-large-cnn,t5-base, etc."       )
  parser.add_argument("input_path"					,	type=SCREAMING_SNAKE_CASE__					,	help="like cnn_dm/test.source"       )
  parser.add_argument("save_path"					,	type=SCREAMING_SNAKE_CASE__					,	help="where to save summaries"       )
  parser.add_argument("--reference_path"					,	type=SCREAMING_SNAKE_CASE__					,	required=SCREAMING_SNAKE_CASE__					,	help="like cnn_dm/test.target"       )
  parser.add_argument("--score_path"					,	type=SCREAMING_SNAKE_CASE__					,	required=SCREAMING_SNAKE_CASE__					,	default="metrics.json"					,	help="where to save metrics"       )
  parser.add_argument("--device"					,	type=SCREAMING_SNAKE_CASE__					,	required=SCREAMING_SNAKE_CASE__					,	default=SCREAMING_SNAKE_CASE__					,	help="cuda, cuda:1, cpu etc."       )
  parser.add_argument(
      "--prefix"					,	type=SCREAMING_SNAKE_CASE__					,	required=SCREAMING_SNAKE_CASE__					,	default=SCREAMING_SNAKE_CASE__					,	help="will be added to the begininng of src examples"       )
  parser.add_argument("--task"					,	type=SCREAMING_SNAKE_CASE__					,	default="summarization"					,	help="used for task_specific_params + metrics"       )
  parser.add_argument("--bs"					,	type=SCREAMING_SNAKE_CASE__					,	default=8					,	required=SCREAMING_SNAKE_CASE__					,	help="batch size"       )
  parser.add_argument(
      "--n_obs"					,	type=SCREAMING_SNAKE_CASE__					,	default=-1					,	required=SCREAMING_SNAKE_CASE__					,	help="How many observations. Defaults to all."       )
  parser.add_argument("--fp16"					,	action="store_true"       )
  parser.add_argument("--dump-args"					,	action="store_true"					,	help="print the custom hparams with the results"       )
  parser.add_argument(
      "--info"					,	nargs="?"					,	type=SCREAMING_SNAKE_CASE__					,	const=datetime_now()					,	help=(
          "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
          " lang=en-ru. If no value is passed, the current datetime string will be used."
      )					,	)
  # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  _snake_case		,		_snake_case     		=				parser.parse_known_args()
  _snake_case     		=				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__       )
  if parsed_args and verbose:
    print(f'''parsed the following generate kwargs: {parsed_args}'''       )
  _snake_case     		=				[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path       ).readlines()]
  if args.n_obs > 0:
    _snake_case     		=				examples[: args.n_obs]
  Path(args.save_path       ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )

  if args.reference_path is None and Path(args.score_path       ).exists():
    warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''       )

  if args.device == "cpu" and args.fpaa:
    # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
    raise ValueError("Can't mix --fp16 and --device cpu"       )

  _snake_case     		=				generate_summaries_or_translations(
      SCREAMING_SNAKE_CASE__					,	args.save_path					,	args.model_name					,	batch_size=args.bs					,	device=args.device					,	fpaa=args.fpaa					,	task=args.task					,	prefix=args.prefix					,	**SCREAMING_SNAKE_CASE__					,	)

  if args.reference_path is None:
    return {}

  # Compute scores
  _snake_case     		=				calculate_bleu if "translation" in args.task else calculate_rouge
  _snake_case     		=				[x.rstrip() for x in open(args.save_path       ).readlines()]
  _snake_case     		=				[x.rstrip() for x in open(args.reference_path       ).readlines()][: len(SCREAMING_SNAKE_CASE__       )]
  _snake_case     		=				score_fn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  scores.update(SCREAMING_SNAKE_CASE__       )

  if args.dump_args:
    scores.update(SCREAMING_SNAKE_CASE__       )
  if args.info:
    _snake_case     		=				args.info

  if verbose:
    print(SCREAMING_SNAKE_CASE__       )

  if args.score_path is not None:
    json.dump(SCREAMING_SNAKE_CASE__					,	open(args.score_path					,	"w"       )       )

  return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)









'''simple docstring'''



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   LongformerTokenizer
     UpperCAmelCase__       :						Dict      =   True
     UpperCAmelCase__       :						Union[str, Any]      =   LongformerTokenizerFast
     UpperCAmelCase__       :						List[str]      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _snake_case     		=				[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       _snake_case     		=				dict(zip(lowerCamelCase						,   range(len(lowerCamelCase	)	)	)	)
       _snake_case     		=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       _snake_case     		=				{"unk_token": "<unk>"}

       _snake_case     		=				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES["vocab_file"]	)
       _snake_case     		=				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES["merges_file"]	)
       with open(self.vocab_file						,   "w"						,   encoding="utf-8"	) as fp:
         fp.write(json.dumps(lowerCamelCase	) + "\n"	)
       with open(self.merges_file						,   "w"						,   encoding="utf-8"	) as fp:
         fp.write("\n".join(lowerCamelCase	)	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				"lower newer"
       _snake_case     		=				"lower newer"
       return input_text, output_text

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.tokenizer_class(self.vocab_file						,   self.merges_file						,   **self.special_tokens_map	)
       _snake_case     		=				"lower newer"
       _snake_case     		=				["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       _snake_case     		=				tokenizer.tokenize(lowerCamelCase	)  # , add_prefix_space=True)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokens + [tokenizer.unk_token]
       _snake_case     		=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       self.assertListEqual(tokenizer.encode("Hello world!"						,   add_special_tokens=lowerCamelCase	)						,   [0, 31_414, 232, 328, 2]	)
       self.assertListEqual(
           tokenizer.encode("Hello world! cécé herlolip 418"						,   add_special_tokens=lowerCamelCase	)						,   [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]						,   )

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"	)

       _snake_case     		=				tokenizer.encode("sequence builders"						,   add_special_tokens=lowerCamelCase	)
       _snake_case     		=				tokenizer.encode("multi-sequence build"						,   add_special_tokens=lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(
           "sequence builders"						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)
       _snake_case     		=				tokenizer.encode(
           "sequence builders"						,   "multi-sequence build"						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)

       _snake_case     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase	)
       _snake_case     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase						,   lowerCamelCase	)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				"Encode this sequence."
       _snake_case     		=				tokenizer.byte_encoder[" ".encode("utf-8"	)[0]]

       # Testing encoder arguments
       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
       self.assertNotEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
       self.assertEqual(lowerCamelCase						,   lowerCamelCase	)

       tokenizer.add_special_tokens({"bos_token": "<s>"}	)
       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
       self.assertNotEqual(lowerCamelCase						,   lowerCamelCase	)

       # Testing spaces after special tokens
       _snake_case     		=				"<mask>"
       tokenizer.add_special_tokens(
           {"mask_token": AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	)}	)  # mask token has a left space
       _snake_case     		=				tokenizer.convert_tokens_to_ids(lowerCamelCase	)

       _snake_case     		=				"Encode <mask> sequence"
       _snake_case     		=				"Encode <mask>sequence"

       _snake_case     		=				tokenizer.encode(lowerCamelCase	)
       _snake_case     		=				encoded.index(lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
       self.assertEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(lowerCamelCase	)
       _snake_case     		=				encoded.index(lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
       self.assertNotEqual(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				self.tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				"A, <mask> AllenNLP sentence."
           _snake_case     		=				tokenizer_r.encode_plus(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)
           _snake_case     		=				tokenizer_p.encode_plus(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]	)						,   sum(tokens_p["token_type_ids"]	)	)

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)						,   sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)						,   )

           _snake_case     		=				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
           _snake_case     		=				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p["input_ids"]						,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
           self.assertSequenceEqual(tokens_r["input_ids"]						,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

           self.assertSequenceEqual(
               lowerCamelCase						,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
           self.assertSequenceEqual(
               lowerCamelCase						,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)

     def 	UpperCamelCase(					self	):
       for trim_offsets, add_prefix_space in itertools.product([True, False]						,   repeat=2	):
         _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)

         _snake_case     		=				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
         _snake_case     		=				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

         self.assertEqual(pre_tokenizer_state["add_prefix_space"]						,   lowerCamelCase	)

         self.assertEqual(post_processor_state["add_prefix_space"]						,   lowerCamelCase	)
         self.assertEqual(post_processor_state["trim_offsets"]						,   lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
       # `trim_offsets`
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
           _snake_case     		=				F'''{text_of_1_token} {text_of_1_token}'''

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	) + 1, len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	) + 1, len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	), len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	), len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				F''' {text}'''

           # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
           #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
           # )
           # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
           # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
           # self.assertEqual(
           #     encoding.offset_mapping[1],
           #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
           # )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (1, 1 + len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (1 + len(lowerCamelCase	) + 1, 1 + len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, 1 + len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (1 + len(lowerCamelCase	), 1 + len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, 1 + len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (1 + len(lowerCamelCase	), 1 + len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )


