



"""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    :		Dict      =			{
    "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       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase=False			,	__lowerCAmelCase=True				)			->    Optional[Any]:







  '''simple docstring'''


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

  lowercase_             =			MODEL_CLASSES[model_type]

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

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

  if compare_with_pt_model:
    lowercase_             =			tf_model(tf_model.dummy_inputs			,	training=lowerCamelCase_				)  # build the network

    lowercase_             =			torch.load(lowerCamelCase_			,	map_location="""cpu"""				)
    lowercase_             =			pt_model_class.from_pretrained(
        pretrained_model_name_or_path=lowerCamelCase_			,	config=lowerCamelCase_			,	state_dict=lowerCamelCase_				)

    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(lowerCamelCase_			,	save_format="""h5"""				)


def       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase=None			,	__lowerCAmelCase=None			,	__lowerCAmelCase=False			,	__lowerCAmelCase=False			,	__lowerCAmelCase=False			,	__lowerCAmelCase=False			,	)			->    Tuple:







  '''simple docstring'''


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

  for j, model_type in enumerate(lowerCamelCase_			,	start=1				):
    print("""=""" * 1_00				)
    print(F''' Converting model type {j}/{len(lowerCamelCase_				)}: {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()				)}.'''				)

    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(lowerCamelCase_			,	lowerCamelCase_				)			,	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
        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(lowerCamelCase_				)}: {model_shortcut_name} - model_type {model_type}'''				)
      print("""-""" * 1_00				)

      if config_shortcut_name in aws_config_map:
        lowercase_             =			cached_file(lowerCamelCase_			,	lowerCamelCase_			,	force_download=not use_cached_models				)
      else:
        lowercase_             =			config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        lowercase_             =			cached_file(lowerCamelCase_			,	lowerCamelCase_			,	force_download=not use_cached_models				)
      else:
        lowercase_             =			model_shortcut_name

      if os.path.isfile(lowerCamelCase_				):
        lowercase_             =			"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__":
       UpperCAmelCase    :		Union[str, Any]      =			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,
       )



'''simple docstring'''

import warnings
from functools import wraps
from typing import Callable


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 @wraps(lowerCamelCase_			)
 def _inner_fn(*lowerCamelCase_      ,			**lowerCamelCase_			):
  warnings.warn(
      (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,			lowerCamelCase_      ,			)
  return fn(*lowerCamelCase_      ,			**lowerCamelCase_			)

 return _inner_fn





