import copy
import re
class  A_			:
  _SCREAMING_SNAKE_CASE     					=      """hp"""
  _SCREAMING_SNAKE_CASE     					=      {}
  _SCREAMING_SNAKE_CASE     					=      None

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Dict							):
   __a							=	prefix
   __a							=	defaults
   cls.build_naming_info()

  @staticmethod
  def 		_UpperCAmelCase (		__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict							):
   if len(__SCREAMING_SNAKE_CASE							) == 0:
    return ""
   __a							=	None
   if any(char.isdigit() for char in word							):
    raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number"""							)
   if word in info["short_word"]:
    return info["short_word"][word]
   for prefix_len in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							):
    __a							=	word[:prefix_len]
    if prefix in info["reverse_short_word"]:
     continue
    else:
     __a							=	prefix
     break

   if short_word is None:
    # Paranoid fallback
    def int_to_alphabetic(__SCREAMING_SNAKE_CASE			:   List[str]							):
     __a							=	""
     while integer != 0:
      __a							=	chr(ord("A"							) + integer % 10							) + s
      integer //= 10
     return s

    __a							=	0
    while True:
     __a							=	word + "#" + int_to_alphabetic(__SCREAMING_SNAKE_CASE							)
     if sword in info["reverse_short_word"]:
      continue
     else:
      __a							=	sword
      break

   __a							=	short_word
   __a							=	word
   return short_word

  @staticmethod
  def 		_UpperCAmelCase (		__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   __a							=	param_name.split("_"							)

   __a							=	[TrialShortNamer.shortname_for_word(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for word in words]

   # We try to create a separatorless short name, but if there is a collision we have to fallback
   # to a separated short name
   __a							=	["", "_"]

   for separator in separators:
    __a							=	separator.join(__SCREAMING_SNAKE_CASE							)
    if shortname not in info["reverse_short_param"]:
     __a							=	shortname
     __a							=	param_name
     return shortname

   return param_name

  @staticmethod
  def 		_UpperCAmelCase (		__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	TrialShortNamer.shortname_for_key(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	short_name
   __a							=	param_name

  @classmethod
  def 		_UpperCAmelCase (		cls			:   int							):
   if cls.NAMING_INFO is not None:
    return

   __a							=	{
       "short_word": {},
       "reverse_short_word": {},
       "short_param": {},
       "reverse_short_param": {},
   }

   __a							=	list(cls.DEFAULTS.keys()							)

   for k in field_keys:
    cls.add_new_param_name(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	info

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict							):
   cls.build_naming_info()
   assert cls.PREFIX is not None
   __a							=	[copy.copy(cls.PREFIX							)]

   for k, v in params.items():
    if k not in cls.DEFAULTS:
     raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""							)
    if v == cls.DEFAULTS[k]:
     # The default value is not added to the name
     continue

    __a							=	cls.NAMING_INFO["short_param"][k]

    if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
     __a							=	1 if v else 0

    __a							=	"" if isinstance(__SCREAMING_SNAKE_CASE  ,				(int, float)							) else "-"
    __a							=	f"""{key}{sep}{v}"""
    name.append(__SCREAMING_SNAKE_CASE							)

   return "_".join(__SCREAMING_SNAKE_CASE							)

  @classmethod
  def 		_UpperCAmelCase (		cls			:   str  ,				__SCREAMING_SNAKE_CASE			:   Dict							):
   __a							=	repr[len(cls.PREFIX							) + 1 :]
   if repr == "":
    __a							=	[]
   else:
    __a							=	repr.split("_"							)

   __a							=	{}

   for value in values:
    if "-" in value:
     __a							,  __a							=	value.split("-"							)
    else:
     __a							=	re.sub("[0-9.]"  ,				""  ,				__SCREAMING_SNAKE_CASE							)
     __a							=	float(re.sub("[^0-9.]"  ,				""  ,				__SCREAMING_SNAKE_CASE							)							)

    __a							=	cls.NAMING_INFO["reverse_short_param"][p_k]

    __a							=	p_v

   for k in cls.DEFAULTS:
    if k not in parameters:
     __a							=	cls.DEFAULTS[k]

   return parameters



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
      """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BertForMaskedLM""",
      """BertForMultipleChoice""",
      """BertForNextSentencePrediction""",
      """BertForPreTraining""",
      """BertForQuestionAnswering""",
      """BertForSequenceClassification""",
      """BertForTokenClassification""",
      """BertLayer""",
      """BertLMHeadModel""",
      """BertModel""",
      """BertPreTrainedModel""",
      """load_tf_weights_in_bert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		[
      """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBertEmbeddings""",
      """TFBertForMaskedLM""",
      """TFBertForMultipleChoice""",
      """TFBertForNextSentencePrediction""",
      """TFBertForPreTraining""",
      """TFBertForQuestionAnswering""",
      """TFBertForSequenceClassification""",
      """TFBertForTokenClassification""",
      """TFBertLMHeadModel""",
      """TFBertMainLayer""",
      """TFBertModel""",
      """TFBertPreTrainedModel""",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """FlaxBertForCausalLM""",
      """FlaxBertForMaskedLM""",
      """FlaxBertForMultipleChoice""",
      """FlaxBertForNextSentencePrediction""",
      """FlaxBertForPreTraining""",
      """FlaxBertForQuestionAnswering""",
      """FlaxBertForSequenceClassification""",
      """FlaxBertForTokenClassification""",
      """FlaxBertModel""",
      """FlaxBertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

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


