



'''simple docstring'''




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							:Tuple				    =					{
    '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			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=False						,      lowerCamelCase__=True    ):
			'''simple docstring'''





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

			A_,    A_,    A_,    A_	:						List[str]	=     MODEL_CLASSES[model_type]

			# Initialise TF model
			if config_file in aws_config_map:
						A_	:						List[Any]	=     cached_file(a__						,      a__						,      force_download=not use_cached_models    )
			A_	:						int	=     config_class.from_json_file(a__    )
			A_	:						str	=     True
			A_	:						List[Any]	=     True
			print(f'Building TensorFlow model from configuration: {config}'    )
			A_	:						List[Any]	=     model_class(a__    )

			# Load weights from tf checkpoint
			if pytorch_checkpoint_path in aws_config_map.keys():
						A_	:						List[str]	=     cached_file(
						    a__						,      a__						,      force_download=not use_cached_models    )
			# Load PyTorch checkpoint in tf2 model:
			A_	:						Dict	=     load_pytorch_checkpoint_in_tfa_model(a__						,      a__    )

			if compare_with_pt_model:
						A_	:						List[Any]	=     tf_model(tf_model.dummy_inputs						,      training=a__    )  # build the network

						A_	:						int	=     torch.load(a__						,      map_location="""cpu"""    )
						A_	:						int	=     pt_model_class.from_pretrained(
						    pretrained_model_name_or_path=a__						,      config=a__						,      state_dict=a__    )

						with torch.no_grad():
									A_	:						Any	=     pt_model(**pt_model.dummy_inputs    )

						A_	:						Dict	=     pto[0].numpy()
						A_	:						Optional[int]	=     tfo[0].numpy()
						A_	:						List[Any]	=     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(a__						,      save_format="""h5"""    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=False						,      lowerCamelCase__=False						,      lowerCamelCase__=False						,      lowerCamelCase__=False						,      ):
			'''simple docstring'''





			if args_model_type is None:
						A_	:						Any	=     list(MODEL_CLASSES.keys()    )
			else:
						A_	:						Optional[Any]	=     [args_model_type]

			for j, model_type in enumerate(a__						,      start=1    ):
						print("""=""" * 1_00    )
						print(f' Converting model type {j}/{len(a__    )}: {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()    )}.'    )

						A_,    A_,    A_,    A_,    A_	:						int	=     MODEL_CLASSES[model_type]

						if model_shortcut_names_or_path is None:
									A_	:						Tuple	=     list(aws_model_maps.keys()    )
						if config_shortcut_names_or_path is None:
									A_	:						Optional[int]	=     model_shortcut_names_or_path

						for i, (model_shortcut_name, config_shortcut_name) in enumerate(
						    zip(a__						,      a__    )						,      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
												A_	:						Union[str, Any]	=     model_shortcut_name
									elif only_convert_finetuned_models:
												print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'    )
												continue
									print(
									    f'    Converting checkpoint {i}/{len(a__    )}: {model_shortcut_name} - model_type {model_type}'    )
									print("""-""" * 1_00    )

									if config_shortcut_name in aws_config_map:
												A_	:						Any	=     cached_file(a__						,      a__						,      force_download=not use_cached_models    )
									else:
												A_	:						int	=     config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												A_	:						List[Any]	=     cached_file(a__						,      a__						,      force_download=not use_cached_models    )
									else:
												A_	:						Optional[int]	=     model_shortcut_name

									if os.path.isfile(a__    ):
												A_	:						str	=     """converted_model"""

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


if __name__ == "__main__":
						lowerCamelCase							:List[str]				    =					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							:List[Any]				    =					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,
						)


'''simple docstring'''



def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)

     # Initialize Result
     __SCREAMING_SNAKE_CASE     =      []

     # Traverse through all denomination
     for denomination in reversed(a__						):
          # Find denominations
          while int(a__						) >= int(a__						):
               total_value -= int(a__						)
               answer.append(a__						)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  UpperCAmelCase :       Dict			   =     []
  UpperCAmelCase :       List[str]			   =     '0'

  if (
      input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
      == "y"
  ):
    UpperCAmelCase :       List[str]			   =     int(input('Enter the number of denominations you want to add: ').strip())

    for i in range(0, n):
      denominations.append(int(input(f"""Denomination {i}: """).strip()))
    UpperCAmelCase :       str			   =     input('Enter the change you want to make in Indian Currency: ').strip()
  else:
    # All denominations of Indian Currency if user does not enter
    UpperCAmelCase :       int			   =     [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
    UpperCAmelCase :       Any			   =     input('Enter the change you want to make: ').strip()

  if int(value) == 0 or int(value) < 0:
    print('The total value cannot be zero or negative.')

  else:
    print(f"""Following is minimal change for {value}: """)
    UpperCAmelCase :       Any			   =     find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=' ')






