
"""simple docstring"""




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 						a__		:
   def __init__(     self      ,       _a      ,       _a=sys.maxsize							):
          lowercase       :							Optional[int]									=				"bilinear"
          lowercase       :							int									=				max_size
          lowercase       :							Optional[Any]									=				short_edge_length

   def __call__(     self      ,       _a							):
          lowercase       :							Union[str, Any]									=				[]
          for img in imgs:
                 lowercase						,       lowercase       :							Optional[Any]									=				img.shape[:2]
                 # later: provide list and randomly choose index for resize
                 lowercase       :							int									=				np.random.randint(self.short_edge_length[0]      ,       self.short_edge_length[1] + 1							)
                 if size == 0:
                        return img
                 lowercase       :							Optional[Any]									=				size * 1.0 / min(_a      ,       _a							)
                 if h < w:
                        lowercase						,       lowercase       :							List[Any]									=				size, scale * w
                 else:
                        lowercase						,       lowercase       :							List[str]									=				scale * h, size
                 if max(_a      ,       _a							) > self.max_size:
                        lowercase       :							Dict									=				self.max_size * 1.0 / max(_a      ,       _a							)
                        lowercase       :							int									=				newh * scale
                        lowercase       :							List[Any]									=				neww * scale
                 lowercase       :							Optional[Any]									=				int(neww + 0.5							)
                 lowercase       :							Any									=				int(newh + 0.5							)

                 if img.dtype == np.uinta:
                        lowercase       :							List[str]									=				Image.fromarray(_a							)
                        lowercase       :							str									=				pil_image.resize((neww, newh)      ,       PILImageResampling.BILINEAR							)
                        lowercase       :							List[str]									=				np.asarray(_a							)
                 else:
                        lowercase       :							Union[str, Any]									=				img.permute(2      ,       0      ,       1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
                        lowercase       :							Any									=				nn.functional.interpolate(
                            _a      ,       (newh, neww)      ,       mode=self.interp_method      ,       align_corners=_a							).squeeze(0							)
                 img_augs.append(_a							)

          return img_augs


class 						a__		:
   def __init__(     self      ,       _a							):
          lowercase       :							Optional[Any]									=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,       cfg.INPUT.MAX_SIZE_TEST							)
          lowercase       :							Union[str, Any]									=				cfg.INPUT.FORMAT
          lowercase       :							Any									=				cfg.SIZE_DIVISIBILITY
          lowercase       :							Optional[Any]									=				cfg.PAD_VALUE
          lowercase       :							Optional[Any]									=				cfg.INPUT.MAX_SIZE_TEST
          lowercase       :							Optional[Any]									=				cfg.MODEL.DEVICE
          lowercase       :							Tuple									=				torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)      ,       1      ,       1							)
          lowercase       :							Tuple									=				torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)      ,       1      ,       1							)
          lowercase       :							Dict									=				lambda _a							: (x - self.pixel_mean) / self.pixel_std
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							List[Any]									=				tuple(max(_a							) for s in zip(*[img.shape for img in images]							)							)
          lowercase       :							List[Any]									=				[im.shape[-2:] for im in images]
          lowercase       :							List[str]									=				[
              nn.functional.pad(
                  _a      ,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,       value=self.pad_value      ,       )
              for size, im in zip(_a      ,       _a							)
          ]

          return torch.stack(_a							), torch.tensor(_a							)

   def __call__(     self      ,       _a      ,       _a=False							):
          with torch.no_grad():
                 if not isinstance(_a      ,       _a							):
                        lowercase       :							int									=				[images]
                 if single_image:
                        assert len(_a							) == 1
                 for i in range(len(_a							)							):
                        if isinstance(images[i]      ,       torch.Tensor							):
                               images.insert(_a      ,       images.pop(_a							).to(self.device							).float()							)
                        elif not isinstance(images[i]      ,       torch.Tensor							):
                               images.insert(
                                   _a      ,       torch.as_tensor(img_tensorize(images.pop(_a							)      ,       input_format=self.input_format							)							)
                                   .to(self.device							)
                                   .float()      ,       )
            # resize smallest edge
                 lowercase       :							Union[str, Any]									=				torch.tensor([im.shape[:2] for im in images]							)
                 lowercase       :							List[str]									=				self.aug(_a							)
                 # transpose images and convert to torch tensors
                 # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                 # now normalize before pad to avoid useless arithmetic
                 lowercase       :							Optional[Any]									=				[self.normalizer(_a							) for x in images]
                 # now pad them to do the following operations
                 lowercase						,       lowercase       :							Dict									=				self.pad(_a							)
                 # Normalize

                 if self.size_divisibility > 0:
                        raise NotImplementedError()
                 # pad
                 lowercase       :							Dict									=				torch.true_divide(_a      ,       _a							)
                 if single_image:
                        return images[0], sizes[0], scales_yx[0]
                 else:
                        return images, sizes, scales_yx


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			List[Any]    )					->							List[Any]:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes


def     __magic_name__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			Tuple[int, int]    )					->							List[Any]:
       assert torch.isfinite(__snake_case    ).all(), "Box tensor contains infinite or NaN!"
       lowercase						,       lowercase       :							Any									=				box_size
       tensor[:, 0].clamp_(min=0	,					max=__snake_case    )
       tensor[:, 1].clamp_(min=0	,					max=__snake_case    )
       tensor[:, 2].clamp_(min=0	,					max=__snake_case    )
       tensor[:, 3].clamp_(min=0	,					max=__snake_case    )




"""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()

_A     :			Optional[int]  						=			{
    """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     __magic_name__      ( __snake_case							:			Optional[int]	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Dict	,					__snake_case							:			str	,					__snake_case							:			Dict=False	,					__snake_case							:			Optional[Any]=True    )					->							Dict:
       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       :							Tuple									=				MODEL_CLASSES[model_type]

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

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

       if compare_with_pt_model:
              lowercase       :							Optional[int]									=				tf_model(tf_model.dummy_inputs	,					training=__snake_case    )  # build the network

              lowercase       :							Optional[Any]									=				torch.load(__snake_case	,					map_location="cpu"    )
              lowercase       :							Tuple									=				pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=__snake_case	,					config=__snake_case	,					state_dict=__snake_case    )

              with torch.no_grad():
                     lowercase       :							Dict									=				pt_model(**pt_model.dummy_inputs    )

              lowercase       :							List[str]									=				pto[0].numpy()
              lowercase       :							List[Any]									=				tfo[0].numpy()
              lowercase       :							List[str]									=				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(__snake_case	,					save_format="h5"    )


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

       for j, model_type in enumerate(__snake_case	,					start=1    ):
              print("=" * 100    )
              print(f""" Converting model type {j}/{len(__snake_case    )}: {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       :							int									=				MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     lowercase       :							Optional[int]									=				list(aws_model_maps.keys()    )
              if config_shortcut_names_or_path is None:
                     lowercase       :							Union[str, Any]									=				model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(__snake_case	,					__snake_case    )	,					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       :							Optional[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(__snake_case    )}: {model_shortcut_name} - model_type {model_type}"""    )
                     print("-" * 100    )

                     if config_shortcut_name in aws_config_map:
                            lowercase       :							List[Any]									=				cached_file(__snake_case	,					__snake_case	,					force_download=not use_cached_models    )
                     else:
                            lowercase       :							Optional[int]									=				config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            lowercase       :							int									=				cached_file(__snake_case	,					__snake_case	,					force_download=not use_cached_models    )
                     else:
                            lowercase       :							Union[str, Any]									=				model_shortcut_name

                     if os.path.isfile(__snake_case    ):
                            lowercase       :							Union[str, Any]									=				"converted_model"

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


if __name__ == "__main__":
   _A     :			Tuple  						=			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.""")
   _A     :			str  						=			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,
   )


