

'''simple docstring'''



def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      len(a__						)
     for i in range(a__						):
          for j in range(i + 1      ,    a__						):
               if numbers[j] < numbers[i]:
                    __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      numbers[j], numbers[i]
     return numbers


if __name__ == "__main__":
  UpperCAmelCase :       Union[str, Any]			   =     input('Enter numbers separated by a comma:\n').strip()
  UpperCAmelCase :       int			   =     [int(item) for item in user_input.split(',')]
  print(exchange_sort(unsorted))









'''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()

UpperCAmelCase :       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__       (  a__      ,    a__      ,    a__      ,    a__      ,    a__=False      ,    a__=True						):




     """simple docstring"""


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

     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__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    a__       (  a__      ,    a__      ,    a__=None      ,    a__=None      ,    a__=False      ,    a__=False      ,    a__=False      ,    a__=False      ,    ):




     """simple docstring"""


     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("""=""" * 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()						)}.'						)

          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__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("""-""" * 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
                    __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("""-""" * 1_00						)

               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__":
  UpperCAmelCase :       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.')
  UpperCAmelCase :       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,
  )






