



from typing import TYPE_CHECKING

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


_lowercase     							=						{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

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

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase     							=						[
							    "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:
							_lowercase     							=						[
							    "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

							_lowercase     							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



