



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()

_lowerCamelCase						    =  {
    """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 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Any		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Optional[int]=False		,	__lowerCamelCase   :     Tuple=True     ):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	MODEL_CLASSES[model_type]

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

 # Load weights from tf checkpoint
 if pytorch_checkpoint_path in aws_config_map.keys():
  __SCREAMING_SNAKE_CASE       :    Tuple       =	cached_file(
      __lowerCamelCase		,	__lowerCamelCase		,	force_download=not use_cached_models     )
 # Load PyTorch checkpoint in tf2 model:
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	load_pytorch_checkpoint_in_tfa_model(__lowerCamelCase		,	__lowerCamelCase     )

 if compare_with_pt_model:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tf_model(tf_model.dummy_inputs		,	training=__lowerCamelCase     )  # build the network

  __SCREAMING_SNAKE_CASE       :    int       =	torch.load(__lowerCamelCase		,	map_location="cpu"     )
  __SCREAMING_SNAKE_CASE       :    Any       =	pt_model_class.from_pretrained(
      pretrained_model_name_or_path=__lowerCamelCase		,	config=__lowerCamelCase		,	state_dict=__lowerCamelCase     )

  with torch.no_grad():
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	pt_model(**pt_model.dummy_inputs     )

  __SCREAMING_SNAKE_CASE       :    List[Any]       =	pto[0].numpy()
  __SCREAMING_SNAKE_CASE       :    Tuple       =	tfo[0].numpy()
  __SCREAMING_SNAKE_CASE       :    Tuple       =	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 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Union[str, Any]=None		,	__lowerCamelCase   :     Tuple=None		,	__lowerCamelCase   :     str=False		,	__lowerCamelCase   :     str=False		,	__lowerCamelCase   :     Union[str, Any]=False		,	__lowerCamelCase   :     Union[str, Any]=False		,	):



 """simple docstring"""





 if args_model_type is None:
  __SCREAMING_SNAKE_CASE       :    str       =	list(MODEL_CLASSES.keys()     )
 else:
  __SCREAMING_SNAKE_CASE       :    int       =	[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()     )}."""     )

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

  if model_shortcut_names_or_path is None:
   __SCREAMING_SNAKE_CASE       :    Tuple       =	list(aws_model_maps.keys()     )
  if config_shortcut_names_or_path is None:
   __SCREAMING_SNAKE_CASE       :    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
    __SCREAMING_SNAKE_CASE       :    Dict       =	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:
    __SCREAMING_SNAKE_CASE       :    Optional[int]       =	cached_file(__lowerCamelCase		,	__lowerCamelCase		,	force_download=not use_cached_models     )
   else:
    __SCREAMING_SNAKE_CASE       :    int       =	config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    __SCREAMING_SNAKE_CASE       :    Dict       =	cached_file(__lowerCamelCase		,	__lowerCamelCase		,	force_download=not use_cached_models     )
   else:
    __SCREAMING_SNAKE_CASE       :    int       =	model_shortcut_name

   if os.path.isfile(__lowerCamelCase     ):
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"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__":
      _lowerCamelCase						    =  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.""")
      _lowerCamelCase						    =  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 importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_lowerCamelCase						    =  importlib.util.find_spec("""s3fs""") is not None

if _has_safs:
      from .safilesystem import SaFileSystem  # noqa: F401

_lowerCamelCase						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
      if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
            warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
      fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 if "://" in dataset_path:
  __SCREAMING_SNAKE_CASE       :    Any       =	dataset_path.split("://"     )[1]
 return dataset_path






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem     ):



 """simple docstring"""





 if fs is not None and fs.protocol != "file":
  return True
 else:
  return False






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	not is_remote_filesystem(__lowerCamelCase     )
 if is_local:
  # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
  shutil.move(fs._strip_protocol(__lowerCamelCase     )		,	fs._strip_protocol(__lowerCamelCase     )     )
 else:
  fs.mv(__lowerCamelCase		,	__lowerCamelCase		,	recursive=__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 if hasattr(fsspec.asyn		,	"reset_lock"     ):
  # for future fsspec>2022.05.0
  fsspec.asyn.reset_lock()
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	None
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
  __SCREAMING_SNAKE_CASE       :    Dict       =	threading.Lock()






