



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase    :		List[Any]		      =    {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase    :		Optional[Any]		      =    [
			    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XmodForCausalLM""",
			    """XmodForMaskedLM""",
			    """XmodForMultipleChoice""",
			    """XmodForQuestionAnswering""",
			    """XmodForSequenceClassification""",
			    """XmodForTokenClassification""",
			    """XmodModel""",
			    """XmodPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xmod import (
						    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XmodForCausalLM,
						    XmodForMaskedLM,
						    XmodForMultipleChoice,
						    XmodForQuestionAnswering,
						    XmodForSequenceClassification,
						    XmodForTokenClassification,
						    XmodModel,
						    XmodPreTrainedModel,
						)

else:
			import sys

			_lowerCamelCase    :		Tuple		      =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




def        __a			(   __lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for data in source_data:
										for i, el in enumerate(__lowerCAmelCase   ):
															if len(__lowerCAmelCase   ) < i + 1:
																				data_lists.append([]   )
															data_lists[i].append(float(__lowerCAmelCase   )   )
					return data_lists

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for dlist, weight in zip(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     str						=				min(__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     int						=				max(__lowerCAmelCase   )

										SCREAMING_SNAKE_CASE							:     list[float]						=				[]
										# for weight 0 score is 1 - actual score
										if weight == 0:
															for item in dlist:
																				try:
																									score.append(1 - ((item - mind) / (maxd - mind))   )
																				except ZeroDivisionError:
																									score.append(1   )

										elif weight == 1:
															for item in dlist:
																				try:
																									score.append((item - mind) / (maxd - mind)   )
																				except ZeroDivisionError:
																									score.append(0   )

        # weight not 0 or 1
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				F'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowerCAmelCase   )

										score_lists.append(__lowerCAmelCase   )

					return score_lists

def        __a			(   __lowerCAmelCase   )     ->     list[float]:
					SCREAMING_SNAKE_CASE							:     list[float]						=				[0 for i in range(len(score_lists[0]   )   )]

					for slist in score_lists:
										for j, ele in enumerate(__lowerCAmelCase   ):
															SCREAMING_SNAKE_CASE							:     str						=				final_scores[j] + ele

					return final_scores



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_data(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				calculate_each_score(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				generate_final_scores(__lowerCAmelCase   )

					# append scores to source data
					for i, ele in enumerate(__lowerCAmelCase   ):
										source_data[i].append(__lowerCAmelCase   )

					return source_data