

"""simple docstring"""




def 		A				(   snake_case__			):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									=					[]
			for data in source_data:
						for i, el in enumerate(snake_case__			):
									if len(snake_case__			) < i + 1:
												data_lists.append([]			)
									data_lists[i].append(float(snake_case__			)			)
			return data_lists

def 		A				(   snake_case__						, snake_case__			):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									=					[]
			for dlist, weight in zip(snake_case__						, snake_case__			):
						SCREAMING_SNAKE_CASE__									=					min(snake_case__			)
						SCREAMING_SNAKE_CASE__									=					max(snake_case__			)

						SCREAMING_SNAKE_CASE__									=					[]
						# 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__									=					f"""Invalid weight of {weight:f} provided"""
									raise ValueError(snake_case__			)

						score_lists.append(snake_case__			)

			return score_lists

def 		A				(   snake_case__			):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									=					[0 for i in range(len(score_lists[0]			)			)]

			for slist in score_lists:
						for j, ele in enumerate(snake_case__			):
									SCREAMING_SNAKE_CASE__									=					final_scores[j] + ele

			return final_scores




def 		A				(   snake_case__						, snake_case__			):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									=					get_data(snake_case__			)
			SCREAMING_SNAKE_CASE__									=					calculate_each_score(snake_case__						, snake_case__			)
			SCREAMING_SNAKE_CASE__									=					generate_final_scores(snake_case__			)

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

			return source_data








from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






