


import os
import string
import sys


_lowercase							:   int						=						1 << 8

_lowercase							:   Optional[Any]						=						{
    "tab": ord("\t"),
    "newline": ord("\r"),
    "esc": 27,
    "up": 65 + ARROW_KEY_FLAG,
    "down": 66 + ARROW_KEY_FLAG,
    "right": 67 + ARROW_KEY_FLAG,
    "left": 68 + ARROW_KEY_FLAG,
    "mod_int": 91,
    "undefined": sys.maxsize,
    "interrupt": 3,
    "insert": 50,
    "delete": 51,
    "pg_up": 53,
    "pg_down": 54,
}

_lowercase							:   Any						=						KEYMAP["up"]
_lowercase							:   Dict						=						KEYMAP["left"]

if sys.platform == "win32":
				_lowercase							:   Optional[Any]						=						[]
				_lowercase							:   List[str]						=						{
				    b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
				    b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
				    b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
				    b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
				    b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
				    b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
				    b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
				    b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
				}

for i in range(10):
				_lowercase							:   int						=						ord(str(i))





def 	_lowerCAmelCase					( )			-> List[str]:





	"""simple docstring"""







	if os.name == "nt":
		import msvcrt

		A             =					"""mbcs"""
		# Flush the keyboard buffer
		while msvcrt.kbhit():
			msvcrt.getch()
		if len(UpperCamelCase__    ) == 0:
			# Read the keystroke
			A             =					msvcrt.getch()

			# If it is a prefix char, get second part
			if ch in (b"\x00", b"\xe0"):
				A             =					ch + msvcrt.getch()
				# Translate actual Win chars to bullet char types
				try:
					A             =					chr(WIN_KEYMAP[cha]    )
					WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]    )    )
					WIN_CH_BUFFER.append(UpperCamelCase__    )
					if ord(UpperCamelCase__    ) in (
					    KEYMAP["insert"] - 1 << 9,
					    KEYMAP["delete"] - 1 << 9,
					    KEYMAP["pg_up"] - 1 << 9,
					    KEYMAP["pg_down"] - 1 << 9,
					):
						WIN_CH_BUFFER.append(chr(1_26    )    )
					A             =					chr(KEYMAP["""esc"""]    )
				except KeyError:
					A             =					cha[1]
			else:
				A             =					ch.decode(UpperCamelCase__    )
		else:
			A             =					WIN_CH_BUFFER.pop(0    )
	elif os.name == "posix":
		import termios
		import tty

		A             =					sys.stdin.fileno()
		A             =					termios.tcgetattr(UpperCamelCase__    )
		try:
			tty.setraw(UpperCamelCase__    )
			A             =					sys.stdin.read(1    )
		finally:
			termios.tcsetattr(UpperCamelCase__				,       termios.TCSADRAIN				,       UpperCamelCase__    )
	return ch





def 	_lowerCAmelCase					( )			-> Union[str, Any]:





	"""simple docstring"""







	A             =					get_raw_chars()
	if ord(UpperCamelCase__    ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
		return char

	elif ord(UpperCamelCase__    ) == KEYMAP["esc"]:
		A             =					get_raw_chars()
		if ord(UpperCamelCase__    ) == KEYMAP["mod_int"]:
			A             =					get_raw_chars()
			if ord(UpperCamelCase__    ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(UpperCamelCase__    ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
				return chr(ord(UpperCamelCase__    ) + ARROW_KEY_FLAG    )
			else:
				return KEYMAP["undefined"]
		else:
			return get_raw_chars()

	else:
		if char in string.printable:
			return char
		else:
			return KEYMAP["undefined"]







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							:   int						=						{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   List[Any]						=						[
				    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RobertaPreLayerNormForCausalLM",
				    "RobertaPreLayerNormForMaskedLM",
				    "RobertaPreLayerNormForMultipleChoice",
				    "RobertaPreLayerNormForQuestionAnswering",
				    "RobertaPreLayerNormForSequenceClassification",
				    "RobertaPreLayerNormForTokenClassification",
				    "RobertaPreLayerNormModel",
				    "RobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Optional[int]						=						[
				    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRobertaPreLayerNormForCausalLM",
				    "TFRobertaPreLayerNormForMaskedLM",
				    "TFRobertaPreLayerNormForMultipleChoice",
				    "TFRobertaPreLayerNormForQuestionAnswering",
				    "TFRobertaPreLayerNormForSequenceClassification",
				    "TFRobertaPreLayerNormForTokenClassification",
				    "TFRobertaPreLayerNormMainLayer",
				    "TFRobertaPreLayerNormModel",
				    "TFRobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   str						=						[
				    "FlaxRobertaPreLayerNormForCausalLM",
				    "FlaxRobertaPreLayerNormForMaskedLM",
				    "FlaxRobertaPreLayerNormForMultipleChoice",
				    "FlaxRobertaPreLayerNormForQuestionAnswering",
				    "FlaxRobertaPreLayerNormForSequenceClassification",
				    "FlaxRobertaPreLayerNormForTokenClassification",
				    "FlaxRobertaPreLayerNormModel",
				    "FlaxRobertaPreLayerNormPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    RobertaPreLayerNormConfig,
				    RobertaPreLayerNormOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta_prelayernorm import (
								    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaPreLayerNormForCausalLM,
								    RobertaPreLayerNormForMaskedLM,
								    RobertaPreLayerNormForMultipleChoice,
								    RobertaPreLayerNormForQuestionAnswering,
								    RobertaPreLayerNormForSequenceClassification,
								    RobertaPreLayerNormForTokenClassification,
								    RobertaPreLayerNormModel,
								    RobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta_prelayernorm import (
								    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaPreLayerNormForCausalLM,
								    TFRobertaPreLayerNormForMaskedLM,
								    TFRobertaPreLayerNormForMultipleChoice,
								    TFRobertaPreLayerNormForQuestionAnswering,
								    TFRobertaPreLayerNormForSequenceClassification,
								    TFRobertaPreLayerNormForTokenClassification,
								    TFRobertaPreLayerNormMainLayer,
								    TFRobertaPreLayerNormModel,
								    TFRobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta_prelayernorm import (
								    FlaxRobertaPreLayerNormForCausalLM,
								    FlaxRobertaPreLayerNormForMaskedLM,
								    FlaxRobertaPreLayerNormForMultipleChoice,
								    FlaxRobertaPreLayerNormForQuestionAnswering,
								    FlaxRobertaPreLayerNormForSequenceClassification,
								    FlaxRobertaPreLayerNormForTokenClassification,
								    FlaxRobertaPreLayerNormModel,
								    FlaxRobertaPreLayerNormPreTrainedModel,
								)

else:
				import sys

				_lowercase							:   List[Any]						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



