

import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowercase_             =     logging.getLogger(__name__)





class 				__lowerCAmelCase  (      __lowerCamelCase							):
		_a		    =						"""token-classification"""




		def __init__(       self							,     lowerCAmelCase      )						->				Any:
								'''simple docstring'''






								if type(UpperCAmelCase_      ) == dict:
														_lowercase			 =Namespace(**UpperCAmelCase_      )
								_lowercase			 =import_module('tasks'      )
								try:
														_lowercase			 =getattr(UpperCAmelCase_							,     hparams.task_type      )
														_lowercase			 =token_classification_task_clazz()
								except AttributeError:
														raise ValueError(
														    F'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
														    F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''      )
								_lowercase			 =self.token_classification_task.get_labels(hparams.labels      )
								_lowercase			 =CrossEntropyLoss().ignore_index
								super().__init__(UpperCAmelCase_							,     len(self.labels      )							,     self.mode      )




		def 						A__  (       self							,     **lowerCAmelCase      )						->				Dict:
								'''simple docstring'''






								return self.model(**UpperCAmelCase_      )




		def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				str:
								'''simple docstring'''






								_lowercase			 ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
								if self.config.model_type != "distilbert":
														_lowercase			 =(
														    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
														)  # XLM and RoBERTa don"t use token_type_ids

								_lowercase			 =self(**UpperCAmelCase_      )
								_lowercase			 =outputs[0]
								# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss}




		def 						A__  (       self      )						->				Tuple:
								'''simple docstring'''






								_lowercase			 =self.hparams
								for mode in ["train", "dev", "test"]:
														_lowercase			 =self._feature_file(UpperCAmelCase_      )
														if os.path.exists(UpperCAmelCase_      ) and not args.overwrite_cache:
																				logger.info('Loading features from cached file %s'							,     UpperCAmelCase_      )
																				_lowercase			 =torch.load(UpperCAmelCase_      )
														else:
																				logger.info('Creating features from dataset file at %s'							,     args.data_dir      )
																				_lowercase			 =self.token_classification_task.read_examples_from_file(args.data_dir							,     UpperCAmelCase_      )
																				_lowercase			 =self.token_classification_task.convert_examples_to_features(
																				    UpperCAmelCase_							,     self.labels							,     args.max_seq_length							,     self.tokenizer							,     cls_token_at_end=bool(self.config.model_type in ['xlnet']      )							,     cls_token=self.tokenizer.cls_token							,     cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0							,     sep_token=self.tokenizer.sep_token							,     sep_token_extra=UpperCAmelCase_							,     pad_on_left=bool(self.config.model_type in ['xlnet']      )							,     pad_token=self.tokenizer.pad_token_id							,     pad_token_segment_id=self.tokenizer.pad_token_type_id							,     pad_token_label_id=self.pad_token_label_id							,     )
																				logger.info('Saving features into cached file %s'							,     UpperCAmelCase_      )
																				torch.save(UpperCAmelCase_							,     UpperCAmelCase_      )




		def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = False      )						->				Tuple:
								'''simple docstring'''






								_lowercase			 =self._feature_file(UpperCAmelCase_      )
								logger.info('Loading features from cached file %s'							,     UpperCAmelCase_      )
								_lowercase			 =torch.load(UpperCAmelCase_      )
								_lowercase			 =torch.tensor([f.input_ids for f in features]							,     dtype=torch.long      )
								_lowercase			 =torch.tensor([f.attention_mask for f in features]							,     dtype=torch.long      )
								if features[0].token_type_ids is not None:
														_lowercase			 =torch.tensor([f.token_type_ids for f in features]							,     dtype=torch.long      )
								else:
														_lowercase			 =torch.tensor([0 for f in features]							,     dtype=torch.long      )
														# HACK(we will not use this anymore soon)
								_lowercase			 =torch.tensor([f.label_ids for f in features]							,     dtype=torch.long      )
								return DataLoader(
								    TensorDataset(UpperCAmelCase_							,     UpperCAmelCase_							,     UpperCAmelCase_							,     UpperCAmelCase_      )							,     batch_size=UpperCAmelCase_      )




		def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				int:
								'''simple docstring'''






								"""Compute validation""" ""
								_lowercase			 ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
								if self.config.model_type != "distilbert":
														_lowercase			 =(
														    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
														)  # XLM and RoBERTa don"t use token_type_ids
								_lowercase			 =self(**UpperCAmelCase_      )
								_lowercase			 =outputs[:2]
								_lowercase			 =logits.detach().cpu().numpy()
								_lowercase			 =inputs['labels'].detach().cpu().numpy()
								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}




		def 						A__  (       self							,     lowerCAmelCase      )						->				Union[str, Any]:
								'''simple docstring'''






								_lowercase			 =torch.stack([x['val_loss'] for x in outputs]      ).mean()
								_lowercase			 =np.concatenate([x['pred'] for x in outputs]							,     axis=0      )
								_lowercase			 =np.argmax(UpperCAmelCase_							,     axis=2      )
								_lowercase			 =np.concatenate([x['target'] for x in outputs]							,     axis=0      )

								_lowercase			 =dict(enumerate(self.labels      )      )
								_lowercase			 =[[] for _ in range(out_label_ids.shape[0]      )]
								_lowercase			 =[[] for _ in range(out_label_ids.shape[0]      )]

								for i in range(out_label_ids.shape[0]      ):
														for j in range(out_label_ids.shape[1]      ):
																				if out_label_ids[i, j] != self.pad_token_label_id:
																										out_label_list[i].append(label_map[out_label_ids[i][j]]      )
																										preds_list[i].append(label_map[preds[i][j]]      )

								_lowercase			 ={
								    'val_loss': val_loss_mean,
								    'accuracy_score': accuracy_score(UpperCAmelCase_							,     UpperCAmelCase_      ),
								    'precision': precision_score(UpperCAmelCase_							,     UpperCAmelCase_      ),
								    'recall': recall_score(UpperCAmelCase_							,     UpperCAmelCase_      ),
								    'f1': fa_score(UpperCAmelCase_							,     UpperCAmelCase_      ),
								}

								_lowercase			 =dict(results.items()      )
								_lowercase			 =results
								return ret, preds_list, out_label_list




		def 						A__  (       self							,     lowerCAmelCase      )						->				Optional[int]:
								'''simple docstring'''






								_lowercase			 =self._eval_end(UpperCAmelCase_      )
								_lowercase			 =ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




		def 						A__  (       self							,     lowerCAmelCase      )						->				List[str]:
								'''simple docstring'''






								_lowercase			 =self._eval_end(UpperCAmelCase_      )

								# Converting to the dict required by pl
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
								# pytorch_lightning/trainer/logging.py#L139
								_lowercase			 =ret['log']
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




		@staticmethod
		def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				Tuple:
								'''simple docstring'''






								BaseTransformer.add_model_specific_args(UpperCAmelCase_							,     UpperCAmelCase_      )
								parser.add_argument(
								    '--task_type'							,     default='NER'							,     type=UpperCAmelCase_							,     help='Task type to fine tune in training (e.g. NER, POS, etc)'      )
								parser.add_argument(
								    '--max_seq_length'							,     default=128							,     type=UpperCAmelCase_							,     help=(
								        'The maximum total input sequence length after tokenization. Sequences longer '
								        'than this will be truncated, sequences shorter will be padded.'
								    )							,     )

								parser.add_argument(
								    '--labels'							,     default=''							,     type=UpperCAmelCase_							,     help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'							,     )
								parser.add_argument(
								    '--gpus'							,     default=0							,     type=UpperCAmelCase_							,     help='The number of GPUs allocated for this, it is by default 0 meaning none'							,     )

								parser.add_argument(
								    '--overwrite_cache'							,     action='store_true'							,     help='Overwrite the cached training and evaluation sets'      )

								return parser


if __name__ == "__main__":
		lowercase_             =     argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		lowercase_             =     NERTransformer.add_model_specific_args(parser, os.getcwd())
		lowercase_             =     parser.parse_args()
		lowercase_             =     NERTransformer(args)
		lowercase_             =     generic_train(model, args)

		if args.do_predict:
				# See https://github.com/huggingface/transformers/issues/3159
				# pl use this default format to create a checkpoint:
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
				# /pytorch_lightning/callbacks/model_checkpoint.py#L322
				lowercase_             =     sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
				lowercase_             =     model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)





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   a   (    A__			:     Tuple   ,     A__			:     List[Any]   ,     A__			:     Optional[int]   ,     A__			:     Dict   ,     A__			:     Any=False   ,     A__			:     str=True  )					->   str:





      """simple docstring"""



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

      _lowercase , _lowercase , _lowercase , _lowercase			 =MODEL_CLASSES[model_type]

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

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

      if compare_with_pt_model:
            _lowercase			 =tf_model(tf_model.dummy_inputs   ,     training=A__  )  # build the network

            _lowercase			 =torch.load(A__   ,     map_location='cpu'  )
            _lowercase			 =pt_model_class.from_pretrained(
                pretrained_model_name_or_path=A__   ,     config=A__   ,     state_dict=A__  )

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

            _lowercase			 =pto[0].numpy()
            _lowercase			 =tfo[0].numpy()
            _lowercase			 =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__			:     str   ,     A__			:     str   ,     A__			:     Optional[Any]=None   ,     A__			:     Any=None   ,     A__			:     Optional[int]=False   ,     A__			:     Optional[int]=False   ,     A__			:     int=False   ,     A__			:     str=False   ,     )					->   List[Any]:





      """simple docstring"""



      if args_model_type is None:
            _lowercase			 =list(MODEL_CLASSES.keys()  )
      else:
            _lowercase			 =[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()  )}.'''  )

            _lowercase , _lowercase , _lowercase , _lowercase , _lowercase			 =MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  _lowercase			 =list(aws_model_maps.keys()  )
            if config_shortcut_names_or_path is None:
                  _lowercase			 =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
                        _lowercase			 =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:
                        _lowercase			 =cached_file(A__   ,     A__   ,     force_download=not use_cached_models  )
                  else:
                        _lowercase			 =config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        _lowercase			 =cached_file(A__   ,     A__   ,     force_download=not use_cached_models  )
                  else:
                        _lowercase			 =model_shortcut_name

                  if os.path.isfile(A__  ):
                        _lowercase			 ='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__":
  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,
  )


