




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    :		Dict			   =       {
    "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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]     ,							lowercase     :	int     ,							lowercase     :	List[Any]     ,							lowercase     :	int     ,							lowercase     :	List[Any]=False     ,							lowercase     :	Optional[int]=True					):


      '''simple docstring'''



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

      lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   MODEL_CLASSES[model_type]

      # Initialise TF model
      if config_file in aws_config_map:
            lowerCamelCase_							=   cached_file(lowercase     ,							lowercase     ,							force_download=not use_cached_models					)
      lowerCamelCase_							=   config_class.from_json_file(lowercase					)
      lowerCamelCase_							=   True
      lowerCamelCase_							=   True
      print(f"""Building TensorFlow model from configuration: {config}"""					)
      lowerCamelCase_							=   model_class(lowercase					)

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

      if compare_with_pt_model:
            lowerCamelCase_							=   tf_model(tf_model.dummy_inputs     ,							training=lowercase					)  # build the network

            lowerCamelCase_							=   torch.load(lowercase     ,							map_location='cpu'					)
            lowerCamelCase_							=   pt_model_class.from_pretrained(
                pretrained_model_name_or_path=lowercase     ,							config=lowercase     ,							state_dict=lowercase					)

            with torch.no_grad():
                  lowerCamelCase_							=   pt_model(**pt_model.dummy_inputs					)

            lowerCamelCase_							=   pto[0].numpy()
            lowerCamelCase_							=   tfo[0].numpy()
            lowerCamelCase_							=   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(lowercase     ,							save_format='h5'					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]     ,							lowercase     :	List[Any]     ,							lowercase     :	Optional[Any]=None     ,							lowercase     :	Optional[Any]=None     ,							lowercase     :	Tuple=False     ,							lowercase     :	List[str]=False     ,							lowercase     :	Tuple=False     ,							lowercase     :	Dict=False     ,							):


      '''simple docstring'''



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

      for j, model_type in enumerate(lowercase     ,							start=1					):
            print('=' * 1_00					)
            print(f""" Converting model type {j}/{len(lowercase					)}: {model_type}"""					)
            print('=' * 1_00					)
            if model_type not in MODEL_CLASSES:
                  raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()					)}."""					)

            lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  lowerCamelCase_							=   list(aws_model_maps.keys()					)
            if config_shortcut_names_or_path is None:
                  lowerCamelCase_							=   model_shortcut_names_or_path

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(lowercase     ,							lowercase					)     ,							start=1					):
                  print('-' * 1_00					)
                  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
                        lowerCamelCase_							=   model_shortcut_name
                  elif only_convert_finetuned_models:
                        print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""					)
                        continue
                  print(
                      f"""    Converting checkpoint {i}/{len(lowercase					)}: {model_shortcut_name} - model_type {model_type}"""					)
                  print('-' * 1_00					)

                  if config_shortcut_name in aws_config_map:
                        lowerCamelCase_							=   cached_file(lowercase     ,							lowercase     ,							force_download=not use_cached_models					)
                  else:
                        lowerCamelCase_							=   config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        lowerCamelCase_							=   cached_file(lowercase     ,							lowercase     ,							force_download=not use_cached_models					)
                  else:
                        lowerCamelCase_							=   model_shortcut_name

                  if os.path.isfile(lowercase					):
                        lowerCamelCase_							=   'converted_model'

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


if __name__ == "__main__":
 lowerCamelCase    :		Dict			   =       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    :		List[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,
 )









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]					):
                  if adj not in explored:
                        stack.append(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(depth_first_search(G, "A"))



