




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		:  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 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=False,	lowerCamelCase=True      ):
  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			:Any							=  MODEL_CLASSES[model_type]

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

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

  if compare_with_pt_model:
    lowercase			:List[str]							=  tf_model(tf_model.dummy_inputs,	training=lowerCamelCase      )  # build the network

    lowercase			:Optional[int]							=  torch.load(lowerCamelCase,	map_location="cpu"      )
    lowercase			:Dict							=  pt_model_class.from_pretrained(
        pretrained_model_name_or_path=lowerCamelCase,	config=lowerCamelCase,	state_dict=lowerCamelCase      )

    with torch.no_grad():
      lowercase			:List[Any]							=  pt_model(**pt_model.dummy_inputs      )

    lowercase			:Optional[Any]							=  pto[0].numpy()
    lowercase			:Tuple							=  tfo[0].numpy()
    lowercase			:Optional[int]							=  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,	lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=False,	lowerCamelCase=False,	lowerCamelCase=False,	lowerCamelCase=False,	):
  if args_model_type is None:
    lowercase			:Optional[Any]							=  list(MODEL_CLASSES.keys()      )
  else:
    lowercase			:Optional[Any]							=  [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()      )}."      )

    lowercase		,			lowercase		,			lowercase		,			lowercase		,			lowercase			:Union[str, Any]							=  MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      lowercase			:Union[str, Any]							=  list(aws_model_maps.keys()      )
    if config_shortcut_names_or_path is None:
      lowercase			:Optional[int]							=  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
        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(lowerCamelCase      )}: {model_shortcut_name} - model_type {model_type}"      )
      print("-" * 100      )

      if config_shortcut_name in aws_config_map:
        lowercase			:Optional[int]							=  cached_file(lowerCamelCase,	lowerCamelCase,	force_download=not use_cached_models      )
      else:
        lowercase			:str							=  config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        lowercase			:Optional[int]							=  cached_file(lowerCamelCase,	lowerCamelCase,	force_download=not use_cached_models      )
      else:
        lowercase			:Any							=  model_shortcut_name

      if os.path.isfile(lowerCamelCase      ):
        lowercase			:Tuple							=  "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		:  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.")
     _UpperCAmelCase		:  Dict	=   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,
     )









import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



