




import os
import string
import sys


lowerCamelCase	:	Dict				=   1 << 8

lowerCamelCase	:	Tuple				=   {
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 2_7,
    'up': 6_5 + ARROW_KEY_FLAG,
    'down': 6_6 + ARROW_KEY_FLAG,
    'right': 6_7 + ARROW_KEY_FLAG,
    'left': 6_8 + ARROW_KEY_FLAG,
    'mod_int': 9_1,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 5_0,
    'delete': 5_1,
    'pg_up': 5_3,
    'pg_down': 5_4,
}

lowerCamelCase	:	str				=   KEYMAP['up']
lowerCamelCase	:	int				=   KEYMAP['left']

if sys.platform == "win32":
       lowerCamelCase	:	Optional[Any]				=   []
       lowerCamelCase	:	Union[str, Any]				=   {
           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(1_0):
       lowerCamelCase	:	List[str]				=   ord(str(i))




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Optional[int]:
   if os.name == "nt":
      import msvcrt

      snake_case				:    List[Any]           =	"""mbcs"""
      # Flush the keyboard buffer
      while msvcrt.kbhit():
         msvcrt.getch()
      if len(lowercase					) == 0:
         # Read the keystroke
         snake_case				:    Tuple           =	msvcrt.getch()

         # If it is a prefix char, get second part
         if ch in (b"\x00", b"\xe0"):
            snake_case				:    Optional[Any]           =	ch + msvcrt.getch()
            # Translate actual Win chars to bullet char types
            try:
               snake_case				:    int           =	chr(WIN_KEYMAP[cha]					)
               WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]					)					)
               WIN_CH_BUFFER.append(lowercase					)
               if ord(lowercase					) in (
                   KEYMAP["insert"] - 1 << 9,
                   KEYMAP["delete"] - 1 << 9,
                   KEYMAP["pg_up"] - 1 << 9,
                   KEYMAP["pg_down"] - 1 << 9,
               ):
                  WIN_CH_BUFFER.append(chr(126					)					)
               snake_case				:    Union[str, Any]           =	chr(KEYMAP["""esc"""]					)
            except KeyError:
               snake_case				:    Union[str, Any]           =	cha[1]
         else:
            snake_case				:    Tuple           =	ch.decode(lowercase					)
      else:
         snake_case				:    str           =	WIN_CH_BUFFER.pop(0					)
   elif os.name == "posix":
      import termios
      import tty

      snake_case				:    str           =	sys.stdin.fileno()
      snake_case				:    int           =	termios.tcgetattr(lowercase					)
      try:
         tty.setraw(lowercase					)
         snake_case				:    Union[str, Any]           =	sys.stdin.read(1					)
      finally:
         termios.tcsetattr(lowercase						,termios.TCSADRAIN						,lowercase					)
   return ch




def 						SCREAMING_SNAKE_CASE__ (   )  ->       str:
   snake_case				:    Tuple           =	get_raw_chars()
   if ord(lowercase					) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
      return char

   elif ord(lowercase					) == KEYMAP["esc"]:
      snake_case				:    Tuple           =	get_raw_chars()
      if ord(lowercase					) == KEYMAP["mod_int"]:
         snake_case				:    List[str]           =	get_raw_chars()
         if ord(lowercase					) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowercase					) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
            return chr(ord(lowercase					) + 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_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	str				=   {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[Any]				=   ['RoFormerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RoFormerForCausalLM',
           'RoFormerForMaskedLM',
           'RoFormerForMultipleChoice',
           'RoFormerForQuestionAnswering',
           'RoFormerForSequenceClassification',
           'RoFormerForTokenClassification',
           'RoFormerLayer',
           'RoFormerModel',
           'RoFormerPreTrainedModel',
           'load_tf_weights_in_roformer',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRoFormerForCausalLM',
           'TFRoFormerForMaskedLM',
           'TFRoFormerForMultipleChoice',
           'TFRoFormerForQuestionAnswering',
           'TFRoFormerForSequenceClassification',
           'TFRoFormerForTokenClassification',
           'TFRoFormerLayer',
           'TFRoFormerModel',
           'TFRoFormerPreTrainedModel',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Union[str, Any]				=   [
           'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxRoFormerForMaskedLM',
           'FlaxRoFormerForMultipleChoice',
           'FlaxRoFormerForQuestionAnswering',
           'FlaxRoFormerForSequenceClassification',
           'FlaxRoFormerForTokenClassification',
           'FlaxRoFormerModel',
           'FlaxRoFormerPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





