





"""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()

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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_=False		,lowercase_=True			)					->		List[str]:




      """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      :	str						=       MODEL_CLASSES[model_type]

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

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

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

            _UpperCamelCase      :	Union[str, Any]						=       torch.load(lowercase_		,map_location="cpu"			)
            _UpperCamelCase      :	Optional[Any]						=       pt_model_class.from_pretrained(
                pretrained_model_name_or_path=lowercase_		,config=lowercase_		,state_dict=lowercase_			)

            with torch.no_grad():
                  _UpperCamelCase      :	str						=       pt_model(**pt_model.dummy_inputs			)

            _UpperCamelCase      :	Union[str, Any]						=       pto[0].numpy()
            _UpperCamelCase      :	List[Any]						=       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(lowercase_		,save_format="h5"			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_=False		,lowercase_=False		,lowercase_=False		,lowercase_=False		,)					->		Optional[int]:




      """simple docstring"""

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

      for j, model_type in enumerate(lowercase_		,start=1			):
            print("=" * 100			)
            print(F''' Converting model type {j}/{len(lowercase_			)}: {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      :	List[Any]						=       MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  _UpperCamelCase      :	Any						=       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(lowercase_		,lowercase_			)		,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[Any]						=       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("-" * 100			)

                  if config_shortcut_name in aws_config_map:
                        _UpperCamelCase      :	List[Any]						=       cached_file(lowercase_		,lowercase_		,force_download=not use_cached_models			)
                  else:
                        _UpperCamelCase      :	Any						=       config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        _UpperCamelCase      :	List[str]						=       cached_file(lowercase_		,lowercase_		,force_download=not use_cached_models			)
                  else:
                        _UpperCamelCase      :	Optional[Any]						=       model_shortcut_name

                  if os.path.isfile(lowercase_			):
                        _UpperCamelCase      :	Optional[Any]						=       "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__				  =   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,
  )







"""simple docstring"""


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
  lowerCamelCase__				  =   input("Enter image url: ").strip()
  print(f"""Downloading image from {url} ...""")
  lowerCamelCase__				  =   BeautifulSoup(requests.get(url).content, "html.parser")
  # The image URL is in the content field of the first meta tag with property og:image
  lowerCamelCase__				  =   soup.find("meta", {"property": "og:image"})["content"]
  lowerCamelCase__				  =   requests.get(image_url).content
  lowerCamelCase__				  =   f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
  with open(file_name, "wb") as fp:
    fp.write(image_data)
  print(f"""Done. Image saved to disk as {file_name}.""")
