

'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase            =		{"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase            =		[
          "OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OPTForCausalLM",
          "OPTModel",
          "OPTPreTrainedModel",
          "OPTForSequenceClassification",
          "OPTForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase            =		["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase            =		[
          "FlaxOPTForCausalLM",
          "FlaxOPTModel",
          "FlaxOPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_opt import (
                OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                OPTForCausalLM,
                OPTForQuestionAnswering,
                OPTForSequenceClassification,
                OPTModel,
                OPTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
      import sys

      _lowerCAmelCase            =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""


from statistics import mean, stdev



def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






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





def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






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




