

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a:    str 							=			{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[Any] 							=			["""XLNetTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    Any 							=			["""XLNetTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    Any 							=			[
				    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLNetForMultipleChoice""",
				    """XLNetForQuestionAnswering""",
				    """XLNetForQuestionAnsweringSimple""",
				    """XLNetForSequenceClassification""",
				    """XLNetForTokenClassification""",
				    """XLNetLMHeadModel""",
				    """XLNetModel""",
				    """XLNetPreTrainedModel""",
				    """load_tf_weights_in_xlnet""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    str 							=			[
				    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFXLNetForMultipleChoice""",
				    """TFXLNetForQuestionAnsweringSimple""",
				    """TFXLNetForSequenceClassification""",
				    """TFXLNetForTokenClassification""",
				    """TFXLNetLMHeadModel""",
				    """TFXLNetMainLayer""",
				    """TFXLNetModel""",
				    """TFXLNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet import XLNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet_fast import XLNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlnet import (
								    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLNetForMultipleChoice,
								    XLNetForQuestionAnswering,
								    XLNetForQuestionAnsweringSimple,
								    XLNetForSequenceClassification,
								    XLNetForTokenClassification,
								    XLNetLMHeadModel,
								    XLNetModel,
								    XLNetPreTrainedModel,
								    load_tf_weights_in_xlnet,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlnet import (
								    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLNetForMultipleChoice,
								    TFXLNetForQuestionAnsweringSimple,
								    TFXLNetForSequenceClassification,
								    TFXLNetForTokenClassification,
								    TFXLNetLMHeadModel,
								    TFXLNetMainLayer,
								    TFXLNetModel,
								    TFXLNetPreTrainedModel,
								)

else:
				import sys

				_a:    Union[str, Any] 							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


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:    str 							=			get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
@require_tokenizers
class 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     ReformerTokenizer
	SCREAMING_SNAKE_CASE__    				=     ReformerTokenizerFast
	SCREAMING_SNAKE_CASE__    				=     True
	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     True




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		super().setUp()

		UpperCAmelCase_       				=			ReformerTokenizer(lowerCAmelCase		,	keep_accents=lowerCAmelCase							)
		tokenizer.save_pretrained(self.tmpdirname							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"<s>"
		UpperCAmelCase_       				=			1

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




	def       __A					(		self	:						Dict							):


		'''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(lowerCAmelCase							)		,	1_000							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		self.assertEqual(self.get_tokenizer().vocab_size		,	1_000							)




	def       __A					(		self	:						str							):


		'''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(lowerCAmelCase							)
		UpperCAmelCase_       				=			rust_tokenizer.tokenize(lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
		UpperCAmelCase_       				=			rust_tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			self.get_rust_tokenizer()
		UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase							)
		UpperCAmelCase_       				=			rust_tokenizer.encode(lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						Optional[int]=15							):


		'''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(lowerCAmelCase		,	**lowerCAmelCase							)

				# 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(lowerCAmelCase		,	tokenizer_r.encode		,	lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding="max_length"							)

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

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

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

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

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




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			ReformerTokenizer(lowerCAmelCase		,	keep_accents=lowerCAmelCase							)

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

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

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

		UpperCAmelCase_       				=			tokenizer.convert_ids_to_tokens(lowerCAmelCase							)
		self.assertListEqual(
		    lowerCAmelCase		,	[
		        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       __A					(		self	:						int							):


		'''simple docstring'''







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




	@slow
	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"Hello World!"
		UpperCAmelCase_       				=			[126, 32, 262, 152, 38, 72, 287]

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




	@slow
	def       __A					(		self	:						int							):


		'''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_       				=			[
		    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(lowerCAmelCase		,	self.big_tokenizer.encode(lowerCAmelCase							)							)




	@require_torch
	@slow
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		import torch

		from transformers import ReformerConfig, ReformerModel

		# Build sequence
		UpperCAmelCase_       				=			list(self.big_tokenizer.get_vocab().keys()							)[:10]
		UpperCAmelCase_       				=			" ".join(lowerCAmelCase							)
		UpperCAmelCase_       				=			self.big_tokenizer.encode_plus(lowerCAmelCase		,	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(lowerCAmelCase							)

		# 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(**lowerCAmelCase							)
			model(**lowerCAmelCase							)




	@slow
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{"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
		UpperCAmelCase_       				=			[
		    "This is a very simple sentence.",
		    "The quick brown fox jumps over the lazy dog.",
		]

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