






def __A    (      __lowerCAmelCase = 1_000	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			2**power
						_UpperCAmelCase									=			str(__lowerCAmelCase	)
						_UpperCAmelCase									=			list(__lowerCAmelCase	)
						_UpperCAmelCase									=			0

						for i in list_num:
												sum_of_num += int(__lowerCAmelCase	)

						return sum_of_num


if __name__ == "__main__":
		_a      	=  int(input('''Enter the power of 2: ''').strip())
		print('''2 ^ ''', power, ''' = ''', 2**power)
		_a      	=  solution(power)
		print('''Sum of the digits is: ''', result)




import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
			import numpy as np
			import torch

			from . import (
			    AlbertForPreTraining,
			    BartForConditionalGeneration,
			    BertForPreTraining,
			    BertForQuestionAnswering,
			    BertForSequenceClassification,
			    CamembertForMaskedLM,
			    CTRLLMHeadModel,
			    DistilBertForMaskedLM,
			    DistilBertForQuestionAnswering,
			    DPRContextEncoder,
			    DPRQuestionEncoder,
			    DPRReader,
			    ElectraForPreTraining,
			    FlaubertWithLMHeadModel,
			    GPTaLMHeadModel,
			    LayoutLMForMaskedLM,
			    LxmertForPreTraining,
			    LxmertVisualFeatureEncoder,
			    OpenAIGPTLMHeadModel,
			    RobertaForMaskedLM,
			    RobertaForSequenceClassification,
			    TaForConditionalGeneration,
			    TransfoXLLMHeadModel,
			    XLMRobertaForMaskedLM,
			    XLMWithLMHeadModel,
			    XLNetLMHeadModel,
			)


logging.set_verbosity_info()

lowerCAmelCase       =							{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=True     ):


		"""simple docstring"""




		if model_type not in MODEL_CLASSES:
				raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()     )}.'     )

		lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      MODEL_CLASSES[model_type]

		# Initialise TF model
		if config_file in aws_config_map:
				lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
		lowercase__            =      config_class.from_json_file(SCREAMING_SNAKE_CASE     )
		lowercase__            =      True
		lowercase__            =      True
		print(f'Building TensorFlow model from configuration: {config}'     )
		lowercase__            =      model_class(SCREAMING_SNAKE_CASE     )

		# Load weights from tf checkpoint
		if pytorch_checkpoint_path in aws_config_map.keys():
				lowercase__            =      cached_file(
				    SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
		# Load PyTorch checkpoint in tf2 model:
		lowercase__            =      load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		if compare_with_pt_model:
				lowercase__            =      tf_model(tf_model.dummy_inputs						,				training=SCREAMING_SNAKE_CASE     )  # build the network

				lowercase__            =      torch.load(SCREAMING_SNAKE_CASE						,				map_location='''cpu'''     )
				lowercase__            =      pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )

				with torch.no_grad():
						lowercase__            =      pt_model(**pt_model.dummy_inputs     )

				lowercase__            =      pto[0].numpy()
				lowercase__            =      tfo[0].numpy()
				lowercase__            =      np.amax(np.abs(np_pt - np_tf     )     )
				print(f'Max absolute difference between models outputs {diff}'     )
				assert diff <= 2E-2, f'Error, model absolute difference is >2e-2: {diff}'

		# Save pytorch-model
		print(f'Save TensorFlow model to {tf_dump_path}'     )
		tf_model.save_weights(SCREAMING_SNAKE_CASE						,				save_format='''h5'''     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				):


		"""simple docstring"""




		if args_model_type is None:
				lowercase__            =      list(MODEL_CLASSES.keys()     )
		else:
				lowercase__            =      [args_model_type]

		for j, model_type in enumerate(SCREAMING_SNAKE_CASE						,				start=1     ):
				print('''=''' * 1_00     )
				print(f' Converting model type {j}/{len(SCREAMING_SNAKE_CASE     )}: {model_type}'     )
				print('''=''' * 1_00     )
				if model_type not in MODEL_CLASSES:
						raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()     )}.'     )

				lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						lowercase__            =      list(aws_model_maps.keys()     )
				if config_shortcut_names_or_path is None:
						lowercase__            =      model_shortcut_names_or_path

				for i, (model_shortcut_name, config_shortcut_name) in enumerate(
				    zip(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )						,				start=1     ):
						print('''-''' * 1_00     )
						if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
								if not only_convert_finetuned_models:
										print(f'    Skipping finetuned checkpoint {model_shortcut_name}'     )
										continue
								lowercase__            =      model_shortcut_name
						elif only_convert_finetuned_models:
								print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'     )
								continue
						print(
						    f'    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE     )}: {model_shortcut_name} - model_type {model_type}'     )
						print('''-''' * 1_00     )

						if config_shortcut_name in aws_config_map:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      model_shortcut_name

						if os.path.isfile(SCREAMING_SNAKE_CASE     ):
								lowercase__            =      '''converted_model'''

						convert_pt_checkpoint_to_tf(
						    model_type=SCREAMING_SNAKE_CASE						,				pytorch_checkpoint_path=SCREAMING_SNAKE_CASE						,				config_file=SCREAMING_SNAKE_CASE						,				tf_dump_path=os.path.join(SCREAMING_SNAKE_CASE						,				model_shortcut_name + '''-tf_model.h5'''     )						,				compare_with_pt_model=SCREAMING_SNAKE_CASE						,				)
						if remove_cached_files:
								os.remove(SCREAMING_SNAKE_CASE     )
								os.remove(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
			)
			parser.add_argument(
			    '--model_type',
			    default=None,
			    type=str,
			    help=(
			        f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
			        'convert all the models from AWS.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_checkpoint_path',
			    default=None,
			    type=str,
			    help=(
			        'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
			        'If not given, will download and convert all the checkpoints from AWS.'
			    ),
			)
			parser.add_argument(
			    '--config_file',
			    default=None,
			    type=str,
			    help=(
			        'The config json file corresponding to the pre-trained model. \n'
			        'This specifies the model architecture. If not given and '
			        '--pytorch_checkpoint_path is not given or is a shortcut name '
			        'use the configuration associated to the shortcut name on the AWS'
			    ),
			)
			parser.add_argument(
			    '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
			)
			parser.add_argument(
			    '--use_cached_models',
			    action='store_true',
			    help='Use cached models if possible instead of updating to latest checkpoint versions.',
			)
			parser.add_argument(
			    '--remove_cached_files',
			    action='store_true',
			    help='Remove pytorch models after conversion (save memory when converting in batches).',
			)
			parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
			lowerCAmelCase       =							parser.parse_args()

			# if args.pytorch_checkpoint_path is not None:
			#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
			#                                 args.pytorch_checkpoint_path,
			#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
			#                                 args.tf_dump_path,
			#                                 compare_with_pt_model=args.compare_with_pt_model,
			#                                 use_cached_models=args.use_cached_models)
			# else:
			convert_all_pt_checkpoints_to_tf(
			    args.model_type.lower() if args.model_type is not None else None,
			    args.tf_dump_path,
			    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
			    if args.pytorch_checkpoint_path is not None
			    else None,
			    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
			    compare_with_pt_model=args.compare_with_pt_model,
			    use_cached_models=args.use_cached_models,
			    remove_cached_files=args.remove_cached_files,
			    only_convert_finetuned_models=args.only_convert_finetuned_models,
			)


