



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCamelCase     :							Tuple  							=				logging.getLogger(__name__)





@dataclass
class  __magic_name__   :
		lowercase      :       str          =field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
		lowercase      :       Optional[str]          =field(
		    default='''NER''',  metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}						)
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
		lowercase      :       bool          =field(default=A__,  metadata={'''help''': '''Set this flag to use fast tokenization.'''}						)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},  )





@dataclass
class  __magic_name__   :
		lowercase      :       str          =field(
		    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}						)
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''},  )
		lowercase      :       int          =field(
		    default=128,  metadata={
		        '''help''': (
		            '''The maximum total input sequence length after tokenization. Sequences longer '''
		            '''than this will be truncated, sequences shorter will be padded.'''
		        )
		    },  )
		lowercase      :       bool          =field(
		    default=A__,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)

def    lowerCamelCase_() ->       int:
						UpperCAmelCase         =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
						if len(sys.argv    ) == 2 and sys.argv[1].endswith(".json"    ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
						else:
												UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir    )
						    and os.listdir(training_args.output_dir    )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
												    " --overwrite_output_dir to overcome."    )

						UpperCAmelCase         =     import_module("tasks"    )
						try:
												UpperCAmelCase         =     getattr(lowerCamelCase_    ,   model_args.task_type    )
												UpperCAmelCase         =     token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    F'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
												    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'    )

						# Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,   datefmt="%m/%d/%Y %H:%M:%S"    ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,   )
						logger.warning(
						    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,   training_args.local_rank    ,   training_args.device    ,   training_args.n_gpu    ,   bool(training_args.local_rank != -1    )    ,   training_args.fpaa    ,   )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank    ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("Training/evaluation parameters %s"    ,   lowerCamelCase_    )

						# Set seed
						set_seed(training_args.seed    )

						# Prepare CONLL-2003 task
						UpperCAmelCase         =     token_classification_task.get_labels(data_args.labels    )
						UpperCAmelCase         =     dict(enumerate(lowerCamelCase_    )    )
						UpperCAmelCase         =     len(lowerCamelCase_    )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						UpperCAmelCase         =     AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,   num_labels=lowerCamelCase_    ,   idalabel=lowerCamelCase_    ,   labelaid={label: i for i, label in enumerate(lowerCamelCase_    )}    ,   cache_dir=model_args.cache_dir    ,   )
						UpperCAmelCase         =     AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,   cache_dir=model_args.cache_dir    ,   use_fast=model_args.use_fast    ,   )
						UpperCAmelCase         =     AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path    ,   from_tf=bool(".ckpt" in model_args.model_name_or_path    )    ,   config=lowerCamelCase_    ,   cache_dir=model_args.cache_dir    ,   )

						# Get datasets
						UpperCAmelCase         =     (
						    TokenClassificationDataset(
						        token_classification_task=lowerCamelCase_    ,   data_dir=data_args.data_dir    ,   tokenizer=lowerCamelCase_    ,   labels=lowerCamelCase_    ,   model_type=config.model_type    ,   max_seq_length=data_args.max_seq_length    ,   overwrite_cache=data_args.overwrite_cache    ,   mode=Split.train    ,   )
						    if training_args.do_train
						    else None
						)
						UpperCAmelCase         =     (
						    TokenClassificationDataset(
						        token_classification_task=lowerCamelCase_    ,   data_dir=data_args.data_dir    ,   tokenizer=lowerCamelCase_    ,   labels=lowerCamelCase_    ,   model_type=config.model_type    ,   max_seq_length=data_args.max_seq_length    ,   overwrite_cache=data_args.overwrite_cache    ,   mode=Split.dev    ,   )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(lowerCamelCase_    ,   lowerCamelCase_    ) -> Tuple[List[int], List[int]]:
												UpperCAmelCase         =     np.argmax(lowerCamelCase_    ,   axis=2    )

												UpperCAmelCase       ,		UpperCAmelCase         =     preds.shape

												UpperCAmelCase         =     [[] for _ in range(lowerCamelCase_    )]
												UpperCAmelCase         =     [[] for _ in range(lowerCamelCase_    )]

												for i in range(lowerCamelCase_    ):
																		for j in range(lowerCamelCase_    ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]    )
																														preds_list[i].append(label_map[preds[i][j]]    )

												return preds_list, out_label_list

						def compute_metrics(lowerCamelCase_    ) -> Dict:
												UpperCAmelCase       ,		UpperCAmelCase         =     align_predictions(p.predictions    ,   p.label_ids    )
												return {
												    "accuracy_score": accuracy_score(lowerCamelCase_    ,   lowerCamelCase_    ),
												    "precision": precision_score(lowerCamelCase_    ,   lowerCamelCase_    ),
												    "recall": recall_score(lowerCamelCase_    ,   lowerCamelCase_    ),
												    "f1": fa_score(lowerCamelCase_    ,   lowerCamelCase_    ),
												}

						# Data collator
						UpperCAmelCase         =     DataCollatorWithPadding(lowerCamelCase_    ,   pad_to_multiple_of=8    ) if training_args.fpaa else None

						# Initialize our Trainer
						UpperCAmelCase         =     Trainer(
						    model=lowerCamelCase_    ,   args=lowerCamelCase_    ,   train_dataset=lowerCamelCase_    ,   eval_dataset=lowerCamelCase_    ,   compute_metrics=lowerCamelCase_    ,   data_collator=lowerCamelCase_    ,   )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
						UpperCAmelCase         =     {}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"    )

												UpperCAmelCase         =     trainer.evaluate()

												UpperCAmelCase         =     os.path.join(training_args.output_dir    ,   "eval_results.txt"    )
												if trainer.is_world_process_zero():
																		with open(lowerCamelCase_    ,   "w"    ) as writer:
																								logger.info("***** Eval results *****"    )
																								for key, value in result.items():
																														logger.info("  %s = %s"    ,   lowerCamelCase_    ,   lowerCamelCase_    )
																														writer.write("%s = %s\n" % (key, value)    )

																		results.update(lowerCamelCase_    )

    # Predict
						if training_args.do_predict:
												UpperCAmelCase         =     TokenClassificationDataset(
												    token_classification_task=lowerCamelCase_    ,   data_dir=data_args.data_dir    ,   tokenizer=lowerCamelCase_    ,   labels=lowerCamelCase_    ,   model_type=config.model_type    ,   max_seq_length=data_args.max_seq_length    ,   overwrite_cache=data_args.overwrite_cache    ,   mode=Split.test    ,   )

												UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     trainer.predict(lowerCamelCase_    )
												UpperCAmelCase       ,		UpperCAmelCase         =     align_predictions(lowerCamelCase_    ,   lowerCamelCase_    )

												UpperCAmelCase         =     os.path.join(training_args.output_dir    ,   "test_results.txt"    )
												if trainer.is_world_process_zero():
																		with open(lowerCamelCase_    ,   "w"    ) as writer:
																								for key, value in metrics.items():
																														logger.info("  %s = %s"    ,   lowerCamelCase_    ,   lowerCamelCase_    )
																														writer.write("%s = %s\n" % (key, value)    )

        # Save predictions
												UpperCAmelCase         =     os.path.join(training_args.output_dir    ,   "test_predictions.txt"    )
												if trainer.is_world_process_zero():
																		with open(lowerCamelCase_    ,   "w"    ) as writer:
																								with open(os.path.join(data_args.data_dir    ,   "test.txt"    )    ,   "r"    ) as f:
																														token_classification_task.write_predictions_to_file(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    )

						return results

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[Any]:
						main()


if __name__ == "__main__":
			main()








import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase     :							Any  							=				get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Any          =ReformerTokenizer
		lowercase      :       str          =ReformerTokenizerFast
		lowercase      :       str          =True
		lowercase      :       Optional[int]          =False
		lowercase      :       List[str]          =True






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								super().setUp()

								UpperCAmelCase         =     ReformerTokenizer(UpperCamelCase__				,		keep_accents=UpperCamelCase__      )
								tokenizer.save_pretrained(self.tmpdirname      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "<s>"
								UpperCAmelCase         =     1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__      )				,		UpperCamelCase__      )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__      )				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     list(self.get_tokenizer().get_vocab().keys()      )

								self.assertEqual(vocab_keys[0]				,		"<unk>"      )
								self.assertEqual(vocab_keys[1]				,		"<s>"      )
								self.assertEqual(vocab_keys[-1]				,		"j"      )
								self.assertEqual(len(UpperCamelCase__      )				,		10_00      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								self.assertEqual(self.get_tokenizer().vocab_size				,		10_00      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
								'''simple docstring'''







								if not self.test_rust_tokenizer:
														return

								UpperCAmelCase         =     self.get_tokenizer()
								UpperCAmelCase         =     self.get_rust_tokenizer()

								UpperCAmelCase         =     "I was born in 92000, and this is falsé."

								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase         =     self.get_rust_tokenizer()
								UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.encode(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     Union[str, Any]=15      )				->					List[str]:
								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																				UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(UpperCamelCase__				,		**UpperCamelCase__      )

																				# Simple input
																				UpperCAmelCase         =     "This is a simple input"
																				UpperCAmelCase         =     ["This is a simple input 1", "This is a simple input 2"]
																				UpperCAmelCase         =     ("This is a simple input", "This is a pair")
																				UpperCAmelCase         =     [
																				    ("This is a simple input 1", "This is a simple input 2"),
																				    ("This is a simple pair 1", "This is a simple pair 2"),
																				]

																				# Simple input tests
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Simple input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Simple input
																				self.assertRaises(
																				    UpperCamelCase__				,		tokenizer_r.batch_encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"				,		)

																				# Pair input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Pair input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Pair input
																				self.assertRaises(
																				    UpperCamelCase__				,		tokenizer_r.batch_encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     ReformerTokenizer(UpperCamelCase__				,		keep_accents=UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.tokenize("This is a test"      )
								self.assertListEqual(UpperCamelCase__				,		["▁This", "▁is", "▁a", "▁t", "est"]      )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		[2_85, 46, 10, 1_70, 3_82]				,		)

								UpperCAmelCase         =     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
								self.assertListEqual(
								    UpperCamelCase__				,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]				,		)
								UpperCAmelCase         =     tokenizer.convert_tokens_to_ids(UpperCamelCase__      )
								self.assertListEqual(
								    UpperCamelCase__				,		[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,		)

								UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(UpperCamelCase__      )
								self.assertListEqual(
								    UpperCamelCase__				,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]				,		)






		@cached_property
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     "Hello World!"
								UpperCAmelCase         =     [1_26, 32, 2_62, 1_52, 38, 72, 2_87]

								self.assertListEqual(UpperCamelCase__				,		self.big_tokenizer.encode(UpperCamelCase__      )      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     (
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								UpperCAmelCase         =     [
								    1_08,
								    2_65,
								    24,
								    1_11,
								    4,
								    2_58,
								    1_56,
								    35,
								    28,
								    2_75,
								    3,
								    2_59,
								    2_97,
								    2_60,
								    84,
								    4,
								    35,
								    1_10,
								    44,
								    8,
								    2_59,
								    91,
								    2_68,
								    21,
								    11,
								    2_09,
								    2_74,
								    1_09,
								    2_66,
								    2_77,
								    1_17,
								    86,
								    93,
								    3_15,
								    2_58,
								    2_78,
								    2_58,
								    2_77,
								    2_58,
								    0,
								    2_58,
								    2_88,
								    2_58,
								    3_19,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    2_87,
								    2_58,
								    3_15,
								    2_58,
								    2_89,
								    2_58,
								    2_78,
								    99,
								    2_69,
								    2_66,
								    2_62,
								    8,
								    2_59,
								    2_41,
								    4,
								    2_17,
								    2_30,
								    2_68,
								    2_66,
								    55,
								    1_68,
								    1_06,
								    75,
								    1_93,
								    2_66,
								    2_23,
								    27,
								    49,
								    26,
								    2_82,
								    25,
								    2_64,
								    2_99,
								    19,
								    26,
								    0,
								    2_58,
								    2_77,
								    1_17,
								    86,
								    93,
								    1_76,
								    1_83,
								    2_70,
								    11,
								    2_62,
								    42,
								    61,
								    2_65,
								]

								self.assertListEqual(UpperCamelCase__				,		self.big_tokenizer.encode(UpperCamelCase__      )      )






		@require_torch
		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								UpperCAmelCase         =     list(self.big_tokenizer.get_vocab().keys()      )[:10]
								UpperCAmelCase         =     " ".join(UpperCamelCase__      )
								UpperCAmelCase         =     self.big_tokenizer.encode_plus(UpperCamelCase__				,		return_tensors="pt"      )
								UpperCAmelCase         =     self.big_tokenizer.batch_encode_plus([sequence, sequence]				,		return_tensors="pt"      )

								UpperCAmelCase         =     ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								UpperCAmelCase         =     encoded_sequence["input_ids"].shape
								UpperCAmelCase         =     ReformerModel(UpperCamelCase__      )

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
														model(**UpperCamelCase__      )
														model(**UpperCamelCase__      )







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     {"input_ids": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								UpperCAmelCase         =     [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCamelCase__				,		model_name="google/reformer-crime-and-punishment"				,		revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"				,		padding=UpperCamelCase__				,		sequences=UpperCamelCase__				,		)



