



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()

SCREAMING_SNAKE_CASE     				=						{
    """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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=False			,     __SCREAMING_SNAKE_CASE=True )		->							int:







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

			UpperCAmelCase_						=				MODEL_CLASSES[model_type]

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

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

			if compare_with_pt_model:
						UpperCAmelCase_						=				tf_model(tf_model.dummy_inputs			,     training=__SCREAMING_SNAKE_CASE )  # build the network

						UpperCAmelCase_						=				torch.load(__SCREAMING_SNAKE_CASE			,     map_location="cpu" )
						UpperCAmelCase_						=				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():
									UpperCAmelCase_						=				pt_model(**pt_model.dummy_inputs )

						UpperCAmelCase_						=				pto[0].numpy()
						UpperCAmelCase_						=				tfo[0].numpy()
						UpperCAmelCase_						=				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 snake_case__  (			__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			,     )		->							Any:







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

			for j, model_type in enumerate(__SCREAMING_SNAKE_CASE			,     start=1 ):
						print("=" * 100 )
						print(f''' Converting model type {j}/{len(__SCREAMING_SNAKE_CASE )}: {model_type}''' )
						print("=" * 100 )
						if model_type not in MODEL_CLASSES:
									raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.''' )

						UpperCAmelCase_						=				MODEL_CLASSES[model_type]

						if model_shortcut_names_or_path is None:
									UpperCAmelCase_						=				list(aws_model_maps.keys() )
						if config_shortcut_names_or_path is None:
									UpperCAmelCase_						=				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("-" * 100 )
									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
												UpperCAmelCase_						=				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("-" * 100 )

									if config_shortcut_name in aws_config_map:
												UpperCAmelCase_						=				cached_file(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     force_download=not use_cached_models )
									else:
												UpperCAmelCase_						=				config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												UpperCAmelCase_						=				cached_file(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     force_download=not use_cached_models )
									else:
												UpperCAmelCase_						=				model_shortcut_name

									if os.path.isfile(__SCREAMING_SNAKE_CASE ):
												UpperCAmelCase_						=				"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__":
			SCREAMING_SNAKE_CASE     				=						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.")
			SCREAMING_SNAKE_CASE     				=						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,
			)






def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				1
		for i in range(1			,     num + 1 ):
				fact *= i
		return fact







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				0
		while number > 0:
				UpperCAmelCase_						=				number % 10
				sum_of_digits += last_digit
				UpperCAmelCase_						=				number // 10  # Removing the last_digit from the given number
		return sum_of_digits







def snake_case__  (			__SCREAMING_SNAKE_CASE = 100 )		->							int:
		UpperCAmelCase_						=				factorial(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				split_and_add(__SCREAMING_SNAKE_CASE )
		return result


if __name__ == "__main__":
			print(solution(int(input("Enter the Number: ").strip())))

