"""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()

SCREAMING_SNAKE_CASE__:str			=	{
    """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(					a ,  a ,  a ,  a ,  a=False ,  a=True							):
       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(a ,  a ,  force_download=not use_cached_models							)
       __a     				=  config_class.from_json_file(a							)
       __a     				=  True
       __a     				=  True
       print(F"Building TensorFlow model from configuration: {config}"							)
       __a     				=  model_class(a							)

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

       if compare_with_pt_model:
              __a     				=  tf_model(tf_model.dummy_inputs ,  training=a							)  # build the network

              __a     				=  torch.load(a ,  map_location="cpu"							)
              __a     				=  pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=a ,  config=a ,  state_dict=a							)

              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(a ,  save_format="h5"							)



def 					_lowerCamelCase(					a ,  a ,  a=None ,  a=None ,  a=False ,  a=False ,  a=False ,  a=False ,  ):
       if args_model_type is None:
              __a     				=  list(MODEL_CLASSES.keys()							)
       else:
              __a     				=  [args_model_type]

       for j, model_type in enumerate(a ,  start=1							):
              print("=" * 1_0_0							)
              print(F" Converting model type {j}/{len(a							)}: {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()							)}."							)

              __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(a ,  a							) ,  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
                            __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(a							)}: {model_shortcut_name} - model_type {model_type}"							)
                     print("-" * 1_0_0							)

                     if config_shortcut_name in aws_config_map:
                            __a     				=  cached_file(a ,  a ,  force_download=not use_cached_models							)
                     else:
                            __a     				=  config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            __a     				=  cached_file(a ,  a ,  force_download=not use_cached_models							)
                     else:
                            __a     				=  model_shortcut_name

                     if os.path.isfile(a							):
                            __a     				=  "converted_model"

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__: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.""")
   SCREAMING_SNAKE_CASE__:str			=	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"""



from ..utils import DummyObject, requires_backends






class 				snake_case__     (   metaclass=snake_case_   ):
 _snake_case   :			Union[str, Any]           =		["""onnx"""]


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(self  ,						["onnx"]		)


 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)






 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)
