





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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				str=False       , UpperCamelCase__:				str=True     ):







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

		SCREAMING_SNAKE_CASE__								=   MODEL_CLASSES[model_type]

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

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

		if compare_with_pt_model:
				SCREAMING_SNAKE_CASE__								=   tf_model(tf_model.dummy_inputs       , training=_A     )  # build the network

				SCREAMING_SNAKE_CASE__								=   torch.load(_A       , map_location="""cpu"""     )
				SCREAMING_SNAKE_CASE__								=   pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=_A       , config=_A       , state_dict=_A     )

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

				SCREAMING_SNAKE_CASE__								=   pto[0].numpy()
				SCREAMING_SNAKE_CASE__								=   tfo[0].numpy()
				SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Optional[Any]=None       , UpperCamelCase__:				Union[str, Any]=None       , UpperCamelCase__:				int=False       , UpperCamelCase__:				Optional[Any]=False       , UpperCamelCase__:				Optional[int]=False       , UpperCamelCase__:				Dict=False       , ):







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

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

				SCREAMING_SNAKE_CASE__								=   MODEL_CLASSES[model_type]

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

				for i, (model_shortcut_name, config_shortcut_name) in enumerate(
				    zip(_A       , _A     )       , 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
								SCREAMING_SNAKE_CASE__								=   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("""-""" * 100     )

						if config_shortcut_name in aws_config_map:
								SCREAMING_SNAKE_CASE__								=   cached_file(_A       , _A       , force_download=not use_cached_models     )
						else:
								SCREAMING_SNAKE_CASE__								=   config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								SCREAMING_SNAKE_CASE__								=   cached_file(_A       , _A       , force_download=not use_cached_models     )
						else:
								SCREAMING_SNAKE_CASE__								=   model_shortcut_name

						if os.path.isfile(_A     ):
								SCREAMING_SNAKE_CASE__								=   '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 					=      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,
	)



'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[Any]											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Tuple											=    [int(item) for item in user_input.split(""",""")]
       print(selection_sort(unsorted))

