






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()

a__       =     {
    """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   (	a						:	int  ,					a						:	Tuple  ,					a						:	Optional[Any]  ,					a						:	List[str]  ,					a						:	List[str]=False  ,					a						:	List[Any]=True ):
							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__	  =       MODEL_CLASSES[model_type]

							# Initialise TF model
							if config_file in aws_config_map:
														snake_case__	  =       cached_file(a  ,					a  ,					force_download=not use_cached_models )
							snake_case__	  =       config_class.from_json_file(a )
							snake_case__	  =       True
							snake_case__	  =       True
							print(F'''Building TensorFlow model from configuration: {config}''' )
							snake_case__	  =       model_class(a )

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

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

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

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

														snake_case__	  =       pto[0].numpy()
														snake_case__	  =       tfo[0].numpy()
														snake_case__	  =       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 _UpperCAmelCase   (	a						:	Dict  ,					a						:	int  ,					a						:	str=None  ,					a						:	int=None  ,					a						:	str=False  ,					a						:	Any=False  ,					a						:	str=False  ,					a						:	List[str]=False  ,					):
							if args_model_type is None:
														snake_case__	  =       list(MODEL_CLASSES.keys() )
							else:
														snake_case__	  =       [args_model_type]

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

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

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(a  ,					a )  ,					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__	  =       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("""-""" * 100 )

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

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

																					if os.path.isfile(a ):
																												snake_case__	  =       """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__":
	a__       =     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.""")
	a__       =     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,
	)








import requests
from bsa import BeautifulSoup

def _UpperCAmelCase   (	a						:	str  ,					a						:	dict ):
							snake_case__	  =       BeautifulSoup(requests.get(a  ,					params=a ).content  ,					"""html.parser""" )
							snake_case__	  =       soup.find("""div"""  ,					attrs={"""class""": """gs_ri"""} )
							snake_case__	  =       div.find("""div"""  ,					attrs={"""class""": """gs_fl"""} ).find_all("""a""" )
							return anchors[2].get_text()


if __name__ == "__main__":
	a__       =     {
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))
