






"""simple docstring"""



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


A__	:    int   = get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:int             =			ReformerTokenizer
			_UpperCAmelCase		:int             =			ReformerTokenizerFast
			_UpperCAmelCase		:Tuple             =			True
			_UpperCAmelCase		:Dict             =			False
			_UpperCAmelCase		:Optional[Any]             =			True


			def      UpperCAmelCase__						(				self					:  Any ):
										super().setUp()

										lowerCamelCase_	:							List[Any]									=ReformerTokenizer(_snake_case       ,	keep_accents=_snake_case )
										tokenizer.save_pretrained(self.tmpdirname )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Tuple									="<s>"
										lowerCamelCase_	:							List[str]									=1

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


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Union[str, Any]									=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(_snake_case )       ,	1000 )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										self.assertEqual(self.get_tokenizer().vocab_size       ,	1000 )


			def      UpperCAmelCase__						(				self					:  str ):


										if not self.test_rust_tokenizer:
																	return

										lowerCamelCase_	:							List[Any]									=self.get_tokenizer()
										lowerCamelCase_	:							int									=self.get_rust_tokenizer()

										lowerCamelCase_	:							int									="I was born in 92000, and this is falsé."

										lowerCamelCase_	:							List[str]									=tokenizer.tokenize(_snake_case )
										lowerCamelCase_	:							List[str]									=rust_tokenizer.tokenize(_snake_case )
										self.assertListEqual(_snake_case       ,	_snake_case )

										lowerCamelCase_	:							Any									=tokenizer.encode(_snake_case       ,	add_special_tokens=_snake_case )
										lowerCamelCase_	:							List[str]									=rust_tokenizer.encode(_snake_case       ,	add_special_tokens=_snake_case )
										self.assertListEqual(_snake_case       ,	_snake_case )

										lowerCamelCase_	:							str									=self.get_rust_tokenizer()
										lowerCamelCase_	:							Optional[Any]									=tokenizer.encode(_snake_case )
										lowerCamelCase_	:							Tuple									=rust_tokenizer.encode(_snake_case )
										self.assertListEqual(_snake_case       ,	_snake_case )


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Any=15 ):


										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
																								lowerCamelCase_	:							List[Any]									=self.rust_tokenizer_class.from_pretrained(_snake_case       ,	**_snake_case )

																								# Simple input
																								lowerCamelCase_	:							int									="This is a simple input"
																								lowerCamelCase_	:							str									=["This is a simple input 1", "This is a simple input 2"]
																								lowerCamelCase_	:							Any									=("This is a simple input", "This is a pair")
																								lowerCamelCase_	:							str									=[
																								    ("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(_snake_case       ,	tokenizer_r.encode       ,	_snake_case       ,	max_length=_snake_case       ,	padding="max_length" )

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

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

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

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

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


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										pass


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Dict									=ReformerTokenizer(_snake_case       ,	keep_accents=_snake_case )

										lowerCamelCase_	:							str									=tokenizer.tokenize("This is a test" )
										self.assertListEqual(_snake_case       ,	["▁This", "▁is", "▁a", "▁t", "est"] )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(_snake_case )       ,	[285, 46, 10, 170, 382]       ,	)

										lowerCamelCase_	:							Optional[Any]									=tokenizer.tokenize("I was born in 92000, and this is falsé." )
										self.assertListEqual(
										    _snake_case       ,	[
										        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",
										        ".",
										    ]       ,	)
										lowerCamelCase_	:							Dict									=tokenizer.convert_tokens_to_ids(_snake_case )
										self.assertListEqual(
										    _snake_case       ,	[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,	)

										lowerCamelCase_	:							Union[str, Any]									=tokenizer.convert_ids_to_tokens(_snake_case )
										self.assertListEqual(
										    _snake_case       ,	[
										        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      UpperCAmelCase__						(				self					:  List[Any] ):
										return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment" )


			@slow
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Union[str, Any]									="Hello World!"
										lowerCamelCase_	:							Tuple									=[126, 32, 262, 152, 38, 72, 287]

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


			@slow
			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Union[str, Any]									=(
										    "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"
										)
										lowerCamelCase_	:							Dict									=[
										    108,
										    265,
										    24,
										    111,
										    4,
										    258,
										    156,
										    35,
										    28,
										    275,
										    3,
										    259,
										    297,
										    260,
										    84,
										    4,
										    35,
										    110,
										    44,
										    8,
										    259,
										    91,
										    268,
										    21,
										    11,
										    209,
										    274,
										    109,
										    266,
										    277,
										    117,
										    86,
										    93,
										    315,
										    258,
										    278,
										    258,
										    277,
										    258,
										    0,
										    258,
										    288,
										    258,
										    319,
										    258,
										    0,
										    258,
										    0,
										    258,
										    0,
										    258,
										    0,
										    258,
										    287,
										    258,
										    315,
										    258,
										    289,
										    258,
										    278,
										    99,
										    269,
										    266,
										    262,
										    8,
										    259,
										    241,
										    4,
										    217,
										    230,
										    268,
										    266,
										    55,
										    168,
										    106,
										    75,
										    193,
										    266,
										    223,
										    27,
										    49,
										    26,
										    282,
										    25,
										    264,
										    299,
										    19,
										    26,
										    0,
										    258,
										    277,
										    117,
										    86,
										    93,
										    176,
										    183,
										    270,
										    11,
										    262,
										    42,
										    61,
										    265,
										]

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


			@require_torch
			@slow
			def      UpperCAmelCase__						(				self					:  List[str] ):
										import torch

										from transformers import ReformerConfig, ReformerModel

										# Build sequence
										lowerCamelCase_	:							Union[str, Any]									=list(self.big_tokenizer.get_vocab().keys() )[:10]
										lowerCamelCase_	:							Any									=" ".join(_snake_case )
										lowerCamelCase_	:							str									=self.big_tokenizer.encode_plus(_snake_case       ,	return_tensors="pt" )
										lowerCamelCase_	:							Tuple									=self.big_tokenizer.batch_encode_plus([sequence, sequence]       ,	return_tensors="pt" )

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

										# 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(**_snake_case )
																	model(**_snake_case )


			@slow
			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							List[Any]									={"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "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
										lowerCamelCase_	:							Any									=[
										    "This is a very simple sentence.",
										    "The quick brown fox jumps over the lazy dog.",
										]

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


A__	:    List[str]			 = {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

						A__	:    List[str]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



