





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  :   List[Any]						      ={
    """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__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__=False  ,    lowercase__=True  ):






   '''simple docstring'''






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

   UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=MODEL_CLASSES[model_type]

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

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

   if compare_with_pt_model:
      UpperCAmelCase_											=tf_model(tf_model.dummy_inputs  ,    training=SCREAMING_SNAKE_CASE_  )  # build the network

      UpperCAmelCase_											=torch.load(SCREAMING_SNAKE_CASE_  ,    map_location="cpu"  )
      UpperCAmelCase_											=pt_model_class.from_pretrained(
          pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_  ,    config=SCREAMING_SNAKE_CASE_  ,    state_dict=SCREAMING_SNAKE_CASE_  )

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

      UpperCAmelCase_											=pto[0].numpy()
      UpperCAmelCase_											=tfo[0].numpy()
      UpperCAmelCase_											=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(SCREAMING_SNAKE_CASE_  ,    save_format="h5"  )




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__=None  ,    lowercase__=None  ,    lowercase__=False  ,    lowercase__=False  ,    lowercase__=False  ,    lowercase__=False  ,    ):






   '''simple docstring'''






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

   for j, model_type in enumerate(SCREAMING_SNAKE_CASE_  ,    start=1  ):
      print("=" * 1_0_0  )
      print(F' Converting model type {j}/{len(SCREAMING_SNAKE_CASE_  )}: {model_type}'  )
      print("=" * 1_0_0  )
      if model_type not in MODEL_CLASSES:
         raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()  )}.'  )

      UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=MODEL_CLASSES[model_type]

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

      for i, (model_shortcut_name, config_shortcut_name) in enumerate(
          zip(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  )  ,    start=1  ):
         print("-" * 1_0_0  )
         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
            UpperCAmelCase_											=model_shortcut_name
         elif only_convert_finetuned_models:
            print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'  )
            continue
         print(
             F'    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE_  )}: {model_shortcut_name} - model_type {model_type}'  )
         print("-" * 1_0_0  )

         if config_shortcut_name in aws_config_map:
            UpperCAmelCase_											=cached_file(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    force_download=not use_cached_models  )
         else:
            UpperCAmelCase_											=config_shortcut_name

         if model_shortcut_name in aws_model_maps:
            UpperCAmelCase_											=cached_file(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    force_download=not use_cached_models  )
         else:
            UpperCAmelCase_											=model_shortcut_name

         if os.path.isfile(SCREAMING_SNAKE_CASE_  ):
            UpperCAmelCase_											="converted_model"

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


if __name__ == "__main__":
     __lowercase  :   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.""")
     __lowercase  :   Tuple						      =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,
     )











from ....configuration_utils import PretrainedConfig
from ....utils import logging


__lowercase  :   Optional[int]						      =logging.get_logger(__name__)

__lowercase  :   Union[str, Any]						      ={
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}

class      A       (	__lowercase						):
      _snake_case							     ='''van'''



      def __init__(	self:						str		,  _lowerCAmelCase:						List[Any]=224		,  _lowerCAmelCase:						Any=3		,  _lowerCAmelCase:						Tuple=[7, 3, 3, 3]		,  _lowerCAmelCase:						Union[str, Any]=[4, 2, 2, 2]		,  _lowerCAmelCase:						List[str]=[64, 128, 320, 512]		,  _lowerCAmelCase:						Tuple=[3, 3, 12, 3]		,  _lowerCAmelCase:						Union[str, Any]=[8, 8, 4, 4]		,  _lowerCAmelCase:						Any="gelu"		,  _lowerCAmelCase:						Optional[int]=0.02		,  _lowerCAmelCase:						List[str]=1e-6		,  _lowerCAmelCase:						Any=1e-2		,  _lowerCAmelCase:						Any=0.0		,  _lowerCAmelCase:						Optional[Any]=0.0		,  **_lowerCAmelCase:						List[str]		,  )						->    Dict:
         '''simple docstring'''

         super().__init__(**_lowerCAmelCase       )
         UpperCAmelCase_											=image_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=patch_sizes
         UpperCAmelCase_											=strides
         UpperCAmelCase_											=hidden_sizes
         UpperCAmelCase_											=depths
         UpperCAmelCase_											=mlp_ratios
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=layer_norm_eps
         UpperCAmelCase_											=layer_scale_init_value
         UpperCAmelCase_											=drop_path_rate
         UpperCAmelCase_											=dropout_rate




