

"""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  :      Optional[Any]							      =		{
    '''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  (      A_						,     A_						,     A_						,     A_						,     A_=False						,     A_=True ):
	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__	:						List[str]  				=	MODEL_CLASSES[model_type]

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

	# Load weights from tf checkpoint
	if pytorch_checkpoint_path in aws_config_map.keys():
		lowerCAmelCase__	:						List[Any]  				=	cached_file(
		    A_						,     A_						,     force_download=not use_cached_models )
	# Load PyTorch checkpoint in tf2 model:
	lowerCAmelCase__	:						Dict  				=	load_pytorch_checkpoint_in_tfa_model(A_						,     A_ )

	if compare_with_pt_model:
		lowerCAmelCase__	:						Union[str, Any]  				=	tf_model(tf_model.dummy_inputs						,     training=A_ )  # build the network

		lowerCAmelCase__	:						str  				=	torch.load(A_						,     map_location='''cpu''' )
		lowerCAmelCase__	:						Dict  				=	pt_model_class.from_pretrained(
		    pretrained_model_name_or_path=A_						,     config=A_						,     state_dict=A_ )

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

		lowerCAmelCase__	:						Optional[int]  				=	pto[0].numpy()
		lowerCAmelCase__	:						Tuple  				=	tfo[0].numpy()
		lowerCAmelCase__	:						Optional[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(A_						,     save_format='''h5''' )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_=None						,     A_=None						,     A_=False						,     A_=False						,     A_=False						,     A_=False						,     ):
	if args_model_type is None:
		lowerCAmelCase__	:						Any  				=	list(MODEL_CLASSES.keys() )
	else:
		lowerCAmelCase__	:						Dict  				=	[args_model_type]

	for j, model_type in enumerate(A_						,     start=1 ):
		print('''=''' * 1_00 )
		print(f' Converting model type {j}/{len(A_ )}: {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__	:						str  				=	MODEL_CLASSES[model_type]

		if model_shortcut_names_or_path is None:
			lowerCAmelCase__	:						Any  				=	list(aws_model_maps.keys() )
		if config_shortcut_names_or_path is None:
			lowerCAmelCase__	:						Dict  				=	model_shortcut_names_or_path

		for i, (model_shortcut_name, config_shortcut_name) in enumerate(
		    zip(A_						,     A_ )						,     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__	:						int  				=	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('''-''' * 1_00 )

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

			if model_shortcut_name in aws_model_maps:
				lowerCAmelCase__	:						Optional[int]  				=	cached_file(A_						,     A_						,     force_download=not use_cached_models )
			else:
				lowerCAmelCase__	:						Optional[int]  				=	model_shortcut_name

			if os.path.isfile(A_ ):
				lowerCAmelCase__	:						Optional[int]  				=	'''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__":
							__UpperCamelCase  :      Union[str, Any]							      =		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  :      Tuple							      =		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"""





import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							__UpperCamelCase  :      Tuple							      =		'''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

							print('''Googling.....''')

							__UpperCamelCase  :      Optional[int]							      =		F'''https://www.google.com/search?q={query}&num=100'''

							__UpperCamelCase  :      Optional[Any]							      =		requests.get(
							    url,
							    headers={'''User-Agent''': str(UserAgent().random)},
							)

							try:
														__UpperCamelCase  :      Union[str, Any]							      =		(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''yuRUbf'''})
														    .find('''a''')
														    .get('''href''')
														)

							except AttributeError:
														__UpperCamelCase  :      str							      =		parse_qs(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''kCrYT'''})
														    .find('''a''')
														    .get('''href''')
														)['''url'''][0]

							webbrowser.open(link)




