


'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       list[float]		)					->	float:







     """simple docstring"""
     if discount_rate < 0:
          raise ValueError('Discount rate cannot be negative'		)
     if not cash_flows:
          raise ValueError('Cash flows list cannot be empty'		)
     _A				=							sum(
         cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(__lowerCamelCase		)		)
     return round(__lowerCamelCase							,    ndigits=2		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()










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()

lowercase_													=      {
    '''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        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Dict=False					, __lowerCamelCase						:     int=True						)	->    Dict:
					if model_type not in MODEL_CLASSES:
										raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()						)}."""						)

					__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =MODEL_CLASSES[model_type]

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

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

					if compare_with_pt_model:
										__lowerCAmelCase						 =tf_model(tf_model.dummy_inputs					, training=__lowerCamelCase						)  # build the network

										__lowerCAmelCase						 =torch.load(__lowerCamelCase					, map_location="""cpu"""						)
										__lowerCAmelCase						 =pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=__lowerCamelCase					, config=__lowerCamelCase					, state_dict=__lowerCamelCase						)

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

										__lowerCAmelCase						 =pto[0].numpy()
										__lowerCAmelCase						 =tfo[0].numpy()
										__lowerCAmelCase						 =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(__lowerCamelCase					, save_format="""h5"""						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Tuple=None					, __lowerCamelCase						:     Any=None					, __lowerCamelCase						:     Any=False					, __lowerCamelCase						:     str=False					, __lowerCamelCase						:     List[str]=False					, __lowerCamelCase						:     Tuple=False					, )	->    Dict:
					if args_model_type is None:
										__lowerCAmelCase						 =list(MODEL_CLASSES.keys()						)
					else:
										__lowerCAmelCase						 =[args_model_type]

					for j, model_type in enumerate(__lowerCamelCase					, start=1						):
										print("""=""" * 100						)
										print(f""" Converting model type {j}/{len(__lowerCamelCase						)}: {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()						)}."""						)

										__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =MODEL_CLASSES[model_type]

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

										for i, (model_shortcut_name, config_shortcut_name) in enumerate(
										    zip(__lowerCamelCase					, __lowerCamelCase						)					, 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
																				__lowerCAmelCase						 =model_shortcut_name
															elif only_convert_finetuned_models:
																				print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""						)
																				continue
															print(
															    f"""    Converting checkpoint {i}/{len(__lowerCamelCase						)}: {model_shortcut_name} - model_type {model_type}"""						)
															print("""-""" * 100						)

															if config_shortcut_name in aws_config_map:
																				__lowerCAmelCase						 =cached_file(__lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
															else:
																				__lowerCAmelCase						 =config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				__lowerCAmelCase						 =cached_file(__lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
															else:
																				__lowerCAmelCase						 =model_shortcut_name

															if os.path.isfile(__lowerCamelCase						):
																				__lowerCAmelCase						 ="""converted_model"""

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


if __name__ == "__main__":
				lowercase_													=      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.''')
				lowercase_													=      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,
				)




