


'''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()

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 __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: List[str]=False      ,  __lowerCamelCase		: List[Any]=True				)      ->     int:







     '''simple docstring'''






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

     _a			,			_a			,			_a			,			_a						=		MODEL_CLASSES[model_type]

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

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

     if compare_with_pt_model:
          _a						=		tf_model(tf_model.dummy_inputs      ,  training=__lowerCamelCase				)  # build the network

          _a						=		torch.load(__lowerCamelCase      ,  map_location="cpu"				)
          _a						=		pt_model_class.from_pretrained(
              pretrained_model_name_or_path=__lowerCamelCase      ,  config=__lowerCamelCase      ,  state_dict=__lowerCamelCase				)

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

          _a						=		pto[0].numpy()
          _a						=		tfo[0].numpy()
          _a						=		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(__lowerCamelCase      ,  save_format="h5"				)






def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: str=None      ,  __lowerCamelCase		: Dict=None      ,  __lowerCamelCase		: Dict=False      ,  __lowerCamelCase		: Optional[int]=False      ,  __lowerCamelCase		: List[Any]=False      ,  __lowerCamelCase		: Dict=False      ,  )      ->     Optional[Any]:







     '''simple docstring'''






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

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

          _a			,			_a			,			_a			,			_a			,			_a						=		MODEL_CLASSES[model_type]

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

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(__lowerCamelCase      ,  __lowerCamelCase				)      ,  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
                    _a						=		model_shortcut_name
               elif only_convert_finetuned_models:
                    print(F"    Skipping not finetuned checkpoint {model_shortcut_name}"				)
                    continue
               print(
                   F"    Converting checkpoint {i}/{len(__lowerCamelCase				)}: {model_shortcut_name} - model_type {model_type}"				)
               print("-" * 100				)

               if config_shortcut_name in aws_config_map:
                    _a						=		cached_file(__lowerCamelCase      ,  __lowerCamelCase      ,  force_download=not use_cached_models				)
               else:
                    _a						=		config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    _a						=		cached_file(__lowerCamelCase      ,  __lowerCamelCase      ,  force_download=not use_cached_models				)
               else:
                    _a						=		model_shortcut_name

               if os.path.isfile(__lowerCamelCase				):
                    _a						=		"converted_model"

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


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,
   )





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("layer.", "layer_"),
         ("word_embeddings.weight", "word_embeddings"),
         ("position_embeddings.weight", "position_embeddings"),
         ("token_type_embeddings.weight", "token_type_embeddings"),
         (".", "/"),
         ("LayerNorm/weight", "LayerNorm/gamma"),
         ("LayerNorm/bias", "LayerNorm/beta"),
         ("weight", "kernel"),
     )

     if not os.path.isdir(__lowerCamelCase				):
          os.makedirs(__lowerCamelCase				)

     _a						=		model.state_dict()

     def to_tf_var_name(__lowerCamelCase		: str				):
          for patt, repl in iter(__lowerCamelCase				):
               _a						=		name.replace(__lowerCamelCase      ,  __lowerCamelCase				)
          return F"bert/{name}"

     def create_tf_var(__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: tf.Session				):
          _a						=		tf.dtypes.as_dtype(tensor.dtype				)
          _a						=		tf.get_variable(dtype=__lowerCamelCase      ,  shape=tensor.shape      ,  name=__lowerCamelCase      ,  initializer=tf.zeros_initializer()				)
          session.run(tf.variables_initializer([tf_var]				)				)
          session.run(__lowerCamelCase				)
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               _a						=		to_tf_var_name(__lowerCamelCase				)
               _a						=		state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose				):
                    _a						=		torch_tensor.T
               _a						=		create_tf_var(tensor=__lowerCamelCase      ,  name=__lowerCamelCase      ,  session=__lowerCamelCase				)
               tf.keras.backend.set_value(__lowerCamelCase      ,  __lowerCamelCase				)
               _a						=		session.run(__lowerCamelCase				)
               print(F"Successfully created {tf_name}: {np.allclose(__lowerCamelCase      ,  __lowerCamelCase				)}"				)

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--model_name"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="model name e.g. bert-base-uncased"				)
     parser.add_argument(
         "--cache_dir"      ,  type=__lowerCamelCase      ,  default=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory containing pytorch model"				)
     parser.add_argument("--pytorch_model_path"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="/path/to/<pytorch-model-name>.bin"				)
     parser.add_argument("--tf_cache_dir"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory in which to save tensorflow model"				)
     _a						=		parser.parse_args(__lowerCamelCase				)

     _a						=		BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name      ,  state_dict=torch.load(args.pytorch_model_path				)      ,  cache_dir=args.cache_dir      ,  )

     convert_pytorch_checkpoint_to_tf(model=__lowerCamelCase      ,  ckpt_dir=args.tf_cache_dir      ,  model_name=args.model_name				)


if __name__ == "__main__":
   main()

