

"""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			=					{
    "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    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=False				,     __UpperCamelCase=True   ):
 '''simple docstring'''





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

 UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       MODEL_CLASSES[model_type]

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

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

 if compare_with_pt_model:
  UpperCAmelCase__       :	int        =       tf_model(tf_model.dummy_inputs				,     training=UpperCamelCase__   )  # build the network

  UpperCAmelCase__       :	int        =       torch.load(UpperCamelCase__				,     map_location="""cpu"""   )
  UpperCAmelCase__       :	Dict        =       pt_model_class.from_pretrained(
      pretrained_model_name_or_path=UpperCamelCase__				,     config=UpperCamelCase__				,     state_dict=UpperCamelCase__   )

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

  UpperCAmelCase__       :	Union[str, Any]        =       pto[0].numpy()
  UpperCAmelCase__       :	str        =       tfo[0].numpy()
  UpperCAmelCase__       :	str        =       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(UpperCamelCase__				,     save_format="""h5"""   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=None				,     __UpperCamelCase=None				,     __UpperCamelCase=False				,     __UpperCamelCase=False				,     __UpperCamelCase=False				,     __UpperCamelCase=False				,     ):
 '''simple docstring'''





 if args_model_type is None:
  UpperCAmelCase__       :	Union[str, Any]        =       list(MODEL_CLASSES.keys()   )
 else:
  UpperCAmelCase__       :	Union[str, Any]        =       [args_model_type]

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

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

  if model_shortcut_names_or_path is None:
   UpperCAmelCase__       :	Tuple        =       list(aws_model_maps.keys()   )
  if config_shortcut_names_or_path is None:
   UpperCAmelCase__       :	Dict        =       model_shortcut_names_or_path

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(UpperCamelCase__				,     UpperCamelCase__   )				,     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
    UpperCAmelCase__       :	List[str]        =       model_shortcut_name
   elif only_convert_finetuned_models:
    print(F"    Skipping not finetuned checkpoint {model_shortcut_name}"   )
    continue
   print(
       F"    Converting checkpoint {i}/{len(UpperCamelCase__   )}: {model_shortcut_name} - model_type {model_type}"   )
   print("""-""" * 100   )

   if config_shortcut_name in aws_config_map:
    UpperCAmelCase__       :	Optional[Any]        =       cached_file(UpperCamelCase__				,     UpperCamelCase__				,     force_download=not use_cached_models   )
   else:
    UpperCAmelCase__       :	int        =       config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    UpperCAmelCase__       :	str        =       cached_file(UpperCamelCase__				,     UpperCamelCase__				,     force_download=not use_cached_models   )
   else:
    UpperCAmelCase__       :	Optional[Any]        =       model_shortcut_name

   if os.path.isfile(UpperCamelCase__   ):
    UpperCAmelCase__       :	Dict        =       """converted_model"""

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


if __name__ == "__main__":
      __UpperCAmelCase			=					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			=					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 __future__ import annotations

__lowerCAmelCase						:							List[str]    							=						8.988e9  # units = N * m^s * C^-2





def    lowerCAmelCase       (		UpperCamelCase__					: float     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: float							):


	"""simple docstring"""
	__UpperCAmelCase      					=      abs(chargea * chargea							)

	if (force, chargea, chargea, distance).count(0							) != 1:
		raise ValueError('''One and only one argument must be 0'''							)
	if distance < 0:
		raise ValueError('''Distance cannot be negative'''							)
	if force == 0:
		__UpperCAmelCase      					=      COULOMBS_CONSTANT * charge_product / (distance**2)
		return {"force": force}
	elif chargea == 0:
		__UpperCAmelCase      					=      abs(UpperCamelCase__							) * (distance**2) / (COULOMBS_CONSTANT * chargea)
		return {"charge1": chargea}
	elif chargea == 0:
		__UpperCAmelCase      					=      abs(UpperCamelCase__							) * (distance**2) / (COULOMBS_CONSTANT * chargea)
		return {"charge2": chargea}
	elif distance == 0:
		__UpperCAmelCase      					=      (COULOMBS_CONSTANT * charge_product / abs(UpperCamelCase__							)) ** 0.5
		return {"distance": distance}
	raise ValueError('''Exactly one argument must be 0'''							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
