
import numpy as np

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    Any							)			->							str:

				"""simple docstring"""







				A__	:						List[Any]								=				np.array(__lowerCamelCase							)
				A__	:						Tuple								=				np.array(__lowerCamelCase							)

				A__	:						Any								=				abs(predict - actual							)
				A__	:						int								=				difference.mean()

				return score

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    Optional[int]							)			->							Optional[int]:

				"""simple docstring"""







				A__	:						Optional[int]								=				np.array(__lowerCamelCase							)
				A__	:						List[str]								=				np.array(__lowerCamelCase							)

				A__	:						Any								=				predict - actual
				A__	:						Optional[int]								=				np.square(__lowerCamelCase							)

				A__	:						Optional[int]								=				square_diff.mean()
				return score

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[str]				,    __UpperCamelCase			:    Dict							)			->							Any:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				np.array(__lowerCamelCase							)
				A__	:						int								=				np.array(__lowerCamelCase							)

				A__	:						List[Any]								=				predict - actual
				A__	:						List[str]								=				np.square(__lowerCamelCase							)
				A__	:						Any								=				square_diff.mean()
				A__	:						Optional[Any]								=				np.sqrt(__lowerCamelCase							)
				return score

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[Any]				,    __UpperCamelCase			:    int							)			->							Optional[int]:

				"""simple docstring"""







				A__	:						Optional[int]								=				np.array(__lowerCamelCase							)
				A__	:						List[str]								=				np.array(__lowerCamelCase							)

				A__	:						Optional[int]								=				np.log(predict + 1							)
				A__	:						Optional[Any]								=				np.log(actual + 1							)

				A__	:						List[str]								=				log_predict - log_actual
				A__	:						List[Any]								=				np.square(__lowerCamelCase							)
				A__	:						int								=				square_diff.mean()

				A__	:						List[str]								=				np.sqrt(__lowerCamelCase							)

				return score

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Tuple				,    __UpperCamelCase			:    Union[str, Any]							)			->							Any:

				"""simple docstring"""







				A__	:						Dict								=				np.array(__lowerCamelCase							)
				A__	:						int								=				np.array(__lowerCamelCase							)

				A__	:						str								=				predict - actual
				A__	:						Optional[Any]								=				np.sum(__lowerCamelCase							) / len(__lowerCamelCase							)
				A__	:						Tuple								=				np.sum(__lowerCamelCase							) / len(__lowerCamelCase							)
				# print(numerator, denumerator)
				A__	:						Union[str, Any]								=				float(__lowerCamelCase							) / denumerator * 1_00

				return score



def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    Any							)			->							str:

				"""simple docstring"""







				return np.mean(np.array(__lowerCamelCase							) == np.array(__lowerCamelCase							)							)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       			=				{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				["""MarkupLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__       			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)

