




from typing import TYPE_CHECKING

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


UpperCamelCase								=					{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					['LxmertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					[
         'LxmertEncoder',
         'LxmertForPreTraining',
         'LxmertForQuestionAnswering',
         'LxmertModel',
         'LxmertPreTrainedModel',
         'LxmertVisualFeatureEncoder',
         'LxmertXLayer',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					[
         'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFLxmertForPreTraining',
         'TFLxmertMainLayer',
         'TFLxmertModel',
         'TFLxmertPreTrainedModel',
         'TFLxmertVisualFeatureEncoder',
     ]


if TYPE_CHECKING:
     from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
     from .tokenization_lxmert import LxmertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_lxmert_fast import LxmertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_lxmert import (
              LxmertEncoder,
              LxmertForPreTraining,
              LxmertForQuestionAnswering,
              LxmertModel,
              LxmertPreTrainedModel,
              LxmertVisualFeatureEncoder,
              LxmertXLayer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_lxmert import (
              TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLxmertForPreTraining,
              TFLxmertMainLayer,
              TFLxmertModel,
              TFLxmertPreTrainedModel,
              TFLxmertVisualFeatureEncoder,
          )

else:
     import sys

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






from statistics import mean, stdev



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			min(lowerCAmelCase_      )
     lowerCAmelCase__          =			max(lowerCAmelCase_      )
     # normalize data
     return [round((x - x_min) / (x_max - x_min)       ,    lowerCAmelCase_      ) for x in data]






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			mean(lowerCAmelCase_      )
     lowerCAmelCase__          =			stdev(lowerCAmelCase_      )
     # standardize data
     return [round((x - mu) / (sigma)       ,    lowerCAmelCase_      ) for x in data]
