



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase     :							List[str]  							=				logging.get_logger(__name__)

__lowerCamelCase     :							Optional[int]  							=				"▁"

__lowerCamelCase     :							List[Any]  							=				{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

__lowerCamelCase     :							Optional[Any]  							=				{
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

__lowerCamelCase     :							Dict  							=				{
    "facebook/s2t-small-librispeech-asr": 1_024,
}

__lowerCamelCase     :							Tuple  							=				["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

__lowerCamelCase     :							Dict  							=				{"mustc": MUSTC_LANGS}





class  __magic_name__   (							__lowerCamelCase						):
		lowercase      :       Union[str, Any]          =VOCAB_FILES_NAMES
		lowercase      :       Tuple          =PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       Optional[Any]          =MAX_MODEL_INPUT_SIZES
		lowercase      :       Optional[Any]          =['''input_ids''', '''attention_mask''']

		lowercase      :       Tuple          =[]






		def __init__(						self      :     Dict				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Any="<s>"				,		UpperCamelCase__      :     str="</s>"				,		UpperCamelCase__      :     Optional[int]="<pad>"				,		UpperCamelCase__      :     str="<unk>"				,		UpperCamelCase__      :     Optional[Any]=False				,		UpperCamelCase__      :     str=False				,		UpperCamelCase__      :     Union[str, Any]=None				,		UpperCamelCase__      :     Optional[int]=None				,		UpperCamelCase__      :     Optional[Dict[str, Any]] = None				,		**UpperCamelCase__      :     Tuple				,		)				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=UpperCAmelCase_				,		eos_token=UpperCAmelCase_				,		unk_token=UpperCAmelCase_				,		pad_token=UpperCAmelCase_				,		do_upper_case=UpperCAmelCase_				,		do_lower_case=UpperCAmelCase_				,		tgt_lang=UpperCAmelCase_				,		lang_codes=UpperCAmelCase_				,		sp_model_kwargs=self.sp_model_kwargs				,		**UpperCAmelCase_				,		)
								UpperCAmelCase         =     do_upper_case
								UpperCAmelCase         =     do_lower_case

								UpperCAmelCase         =     load_json(UpperCAmelCase_      )
								UpperCAmelCase         =     {v: k for k, v in self.encoder.items()}
								UpperCAmelCase         =     spm_file
								UpperCAmelCase         =     load_spm(UpperCAmelCase_				,		self.sp_model_kwargs      )

								if lang_codes is not None:
														UpperCAmelCase         =     lang_codes
														UpperCAmelCase         =     LANGUAGES[lang_codes]
														UpperCAmelCase         =     [F'<lang:{lang}>' for lang in self.langs]
														UpperCAmelCase         =     {lang: self.sp_model.PieceToId(F'<lang:{lang}>'      ) for lang in self.langs}

														UpperCAmelCase         =     self.lang_tokens
														UpperCAmelCase         =     tgt_lang if tgt_lang is not None else self.langs[0]

														self.set_tgt_lang_special_tokens(self._tgt_lang      )
								else:
														UpperCAmelCase         =     {}






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







								return len(self.encoder      )






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







								return self._tgt_lang






		@tgt_lang.setter
		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Any      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     new_tgt_lang
								self.set_tgt_lang_special_tokens(UpperCAmelCase_      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     str      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     self.lang_code_to_id[tgt_lang]
								UpperCAmelCase         =     [lang_code_id]






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







								return self.sp_model.encode(UpperCAmelCase_				,		out_type=UpperCAmelCase_      )






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







								return self.encoder.get(UpperCAmelCase_				,		self.encoder[self.unk_token]      )






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







								return self.decoder.get(UpperCAmelCase_				,		self.unk_token      )






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







								UpperCAmelCase         =     []
								UpperCAmelCase         =     ''
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				UpperCAmelCase         =     self.sp_model.decode(UpperCAmelCase_      )
																				out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																				UpperCAmelCase         =     []
														else:
																				current_sub_tokens.append(UpperCAmelCase_      )
								UpperCAmelCase         =     self.sp_model.decode(UpperCAmelCase_      )
								out_string += decoded.upper() if self.do_upper_case else decoded
								return out_string.strip()






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Optional[int]=None      )				->					Optional[int]:
								'''simple docstring'''







								if token_ids_a is None:
														return self.prefix_tokens + token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None				,		UpperCamelCase__      :     bool = False      )				->					str:
								'''simple docstring'''







								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCAmelCase_				,		token_ids_a=UpperCAmelCase_				,		already_has_special_tokens=UpperCAmelCase_      )

								UpperCAmelCase         =     [1] * len(self.prefix_tokens      )
								UpperCAmelCase         =     [1]
								if token_ids_a is None:
														return prefix_ones + ([0] * len(UpperCAmelCase_      )) + suffix_ones
								return prefix_ones + ([0] * len(UpperCAmelCase_      )) + ([0] * len(UpperCAmelCase_      )) + suffix_ones






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







								UpperCAmelCase         =     self.encoder.copy()
								vocab.update(self.added_tokens_encoder      )
								return vocab






		def __getstate__(						self      :     str      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.__dict__.copy()
								UpperCAmelCase         =     None
								return state






		def __setstate__(						self      :     int				,		UpperCamelCase__      :     Dict      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     d

								# for backward compatibility
								if not hasattr(self				,		"sp_model_kwargs"      ):
														UpperCAmelCase         =     {}

								UpperCAmelCase         =     load_spm(self.spm_file				,		self.sp_model_kwargs      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[str] = None      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     Path(UpperCAmelCase_      )
								assert save_dir.is_dir(), F'{save_directory} should be a directory'
								UpperCAmelCase         =     save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
								)
								UpperCAmelCase         =     save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
								)

								save_json(self.encoder				,		UpperCAmelCase_      )

								if os.path.abspath(self.spm_file      ) != os.path.abspath(UpperCAmelCase_      ) and os.path.isfile(self.spm_file      ):
														copyfile(self.spm_file				,		UpperCAmelCase_      )
								elif not os.path.isfile(self.spm_file      ):
														with open(UpperCAmelCase_				,		"wb"      ) as fi:
																				UpperCAmelCase         =     self.sp_model.serialized_model_proto()
																				fi.write(UpperCAmelCase_      )

								return (str(UpperCAmelCase_      ), str(UpperCAmelCase_      ))

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       sentencepiece.SentencePieceProcessor:
						UpperCAmelCase         =     sentencepiece.SentencePieceProcessor(**lowerCamelCase_    )
						spm.Load(str(lowerCamelCase_    )    )
						return spm

def    lowerCamelCase_(lowerCamelCase_    ) ->       Union[Dict, List]:
						with open(lowerCamelCase_    ,   "r"    ) as f:
												return json.load(lowerCamelCase_    )





def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       None:
						with open(lowerCamelCase_    ,   "w"    ) as f:
												json.dump(lowerCamelCase_    ,   lowerCamelCase_    ,   indent=2    )








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__				,		)



