






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 	UpperCamelCase	(							snake_case__  : str						,snake_case__  : Optional[Any]						,snake_case__  : Optional[int]						,snake_case__  : Union[str, Any]						,snake_case__  : Optional[Any]=False						,snake_case__  : str=True	):




     '''simple docstring'''






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

     __snake_case							,			__snake_case							,			__snake_case							,			__snake_case      :Dict 							=    MODEL_CLASSES[model_type]

     # Initialise TF model
     if config_file in aws_config_map:
          __snake_case      :List[str] 							=    cached_file(snake_case__						,snake_case__						,force_download=not use_cached_models	)
     __snake_case      :Any 							=    config_class.from_json_file(snake_case__	)
     __snake_case      :Union[str, Any] 							=    True
     __snake_case      :Dict 							=    True
     print(f'''Building TensorFlow model from configuration: {config}'''	)
     __snake_case      :Optional[Any] 							=    model_class(snake_case__	)

     # Load weights from tf checkpoint
     if pytorch_checkpoint_path in aws_config_map.keys():
          __snake_case      :int 							=    cached_file(
              snake_case__						,snake_case__						,force_download=not use_cached_models	)
     # Load PyTorch checkpoint in tf2 model:
     __snake_case      :Union[str, Any] 							=    load_pytorch_checkpoint_in_tfa_model(snake_case__						,snake_case__	)

     if compare_with_pt_model:
          __snake_case      :Tuple 							=    tf_model(tf_model.dummy_inputs						,training=snake_case__	)  # build the network

          __snake_case      :Optional[Any] 							=    torch.load(snake_case__						,map_location="""cpu"""	)
          __snake_case      :List[Any] 							=    pt_model_class.from_pretrained(
              pretrained_model_name_or_path=snake_case__						,config=snake_case__						,state_dict=snake_case__	)

          with torch.no_grad():
               __snake_case      :Optional[Any] 							=    pt_model(**pt_model.dummy_inputs	)

          __snake_case      :str 							=    pto[0].numpy()
          __snake_case      :Optional[int] 							=    tfo[0].numpy()
          __snake_case      :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(snake_case__						,save_format="""h5"""	)






def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : Optional[Any]						,snake_case__  : int=None						,snake_case__  : Dict=None						,snake_case__  : Tuple=False						,snake_case__  : Optional[Any]=False						,snake_case__  : Dict=False						,snake_case__  : Any=False						,):




     '''simple docstring'''






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

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

          __snake_case							,			__snake_case							,			__snake_case							,			__snake_case							,			__snake_case      :Tuple 							=    MODEL_CLASSES[model_type]

          if model_shortcut_names_or_path is None:
               __snake_case      :Optional[Any] 							=    list(aws_model_maps.keys()	)
          if config_shortcut_names_or_path is None:
               __snake_case      :Tuple 							=    model_shortcut_names_or_path

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(snake_case__						,snake_case__	)						,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
                    __snake_case      :Tuple 							=    model_shortcut_name
               elif only_convert_finetuned_models:
                    print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''	)
                    continue
               print(
                   f'''    Converting checkpoint {i}/{len(snake_case__	)}: {model_shortcut_name} - model_type {model_type}'''	)
               print("""-""" * 100	)

               if config_shortcut_name in aws_config_map:
                    __snake_case      :Dict 							=    cached_file(snake_case__						,snake_case__						,force_download=not use_cached_models	)
               else:
                    __snake_case      :List[Any] 							=    config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    __snake_case      :str 							=    cached_file(snake_case__						,snake_case__						,force_download=not use_cached_models	)
               else:
                    __snake_case      :Union[str, Any] 							=    model_shortcut_name

               if os.path.isfile(snake_case__	):
                    __snake_case      :str 							=    """converted_model"""

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


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,
     )










lowerCamelCase__       		=  8.3_1_4_4_5_9_8






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float	):




     '''simple docstring'''






     if temperature < 0:
          raise Exception("""Temperature cannot be less than 0 K"""	)
     if molar_mass <= 0:
          raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""	)
     else:
          return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # example
     lowerCamelCase__       		=  300
     lowerCamelCase__       		=  28
     lowerCamelCase__       		=  rms_speed_of_molecule(temperature, molar_mass)
     print(f'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


