
'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase			:				int    		=			logging.get_logger(__name__)

lowercase			:				Tuple    		=			{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    for attribute in key.split('''.'''						):
        A       :   int					=						getattr(snake_case__		,				snake_case__						)

    if weight_type is not None:
        A       :   Optional[int]					=						getattr(snake_case__		,				snake_case__						).shape
    else:
        A       :   Union[str, Any]					=						hf_pointer.shape

    assert hf_shape == value.shape, (
        F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
        F' {value.shape} for {full_name}'
    )

    if weight_type == "weight":
        A       :   Optional[int]					=						value
    elif weight_type == "weight_g":
        A       :   str					=						value
    elif weight_type == "weight_v":
        A       :   str					=						value
    elif weight_type == "bias":
        A       :   List[str]					=						value
    else:
        A       :   Any					=						value

    logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						[]
    A       :   Optional[int]					=						fairseq_model.state_dict()

    A       :   Optional[Any]					=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        A       :   Tuple					=						False
        if "conv_layers" in name:
            load_conv_layer(
                snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				hf_model.config.feat_extract_norm == '''group'''		,				)
            A       :   str					=						True
        else:
            for key, mapped_key in MAPPING.items():
                A       :   str					=						'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                    A       :   Union[str, Any]					=						True
                    if "*" in mapped_key:
                        A       :   List[Any]					=						name.split(snake_case__						)[0].split('''.'''						)[-2]
                        A       :   Optional[int]					=						mapped_key.replace('''*'''		,				snake_case__						)
                    if "weight_g" in name:
                        A       :   List[str]					=						'''weight_g'''
                    elif "weight_v" in name:
                        A       :   Union[str, Any]					=						'''weight_v'''
                    elif "weight" in name:
                        A       :   Dict					=						'''weight'''
                    elif "bias" in name:
                        A       :   Dict					=						'''bias'''
                    else:
                        A       :   Optional[Any]					=						None
                    set_recursively(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)
                continue
        if not is_used:
            unused_weights.append(snake_case__						)

    logger.warning(F'Unused weights: {unused_weights}'						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						full_name.split('''conv_layers.'''						)[-1]
    A       :   List[Any]					=						name.split('''.'''						)
    A       :   Union[str, Any]					=						int(items[0]						)
    A       :   Any					=						int(items[1]						)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
            )
            A       :   Optional[Any]					=						value
            logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
            )
            A       :   Optional[int]					=						value
            logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                " found."
            )
            A       :   int					=						value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
            )
            A       :   int					=						value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
    else:
        unused_weights.append(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						SEWConfig()
    if is_finetuned:
        A       :   List[Any]					=						model.wav_encoder.wav_model.cfg
    else:
        A       :   str					=						model.cfg

    A       :   int					=						fs_config.conv_bias
    A       :   Optional[int]					=						eval(fs_config.conv_feature_layers						)
    A       :   Union[str, Any]					=						[x[0] for x in conv_layers]
    A       :   Dict					=						[x[1] for x in conv_layers]
    A       :   Dict					=						[x[2] for x in conv_layers]
    A       :   str					=						'''gelu'''
    A       :   Tuple					=						'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    A       :   Optional[Any]					=						0.0
    A       :   List[str]					=						fs_config.activation_fn.name
    A       :   List[str]					=						fs_config.encoder_embed_dim
    A       :   Optional[Any]					=						0.02
    A       :   Any					=						fs_config.encoder_ffn_embed_dim
    A       :   str					=						1E-5
    A       :   str					=						fs_config.encoder_layerdrop
    A       :   List[str]					=						fs_config.encoder_attention_heads
    A       :   List[str]					=						fs_config.conv_pos_groups
    A       :   str					=						fs_config.conv_pos
    A       :   Union[str, Any]					=						len(snake_case__						)
    A       :   str					=						fs_config.encoder_layers
    A       :   List[Any]					=						fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        A       :   Optional[int]					=						model.cfg
        A       :   Union[str, Any]					=						fs_config.final_dropout
        A       :   Optional[int]					=						fs_config.layerdrop
    A       :   str					=						fs_config.activation_dropout
    A       :   int					=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    A       :   Dict					=						fs_config.attention_dropout
    A       :   List[str]					=						fs_config.dropout_input
    A       :   Union[str, Any]					=						fs_config.dropout
    A       :   int					=						fs_config.mask_channel_length
    A       :   str					=						fs_config.mask_channel_prob
    A       :   List[str]					=						fs_config.mask_length
    A       :   Tuple					=						fs_config.mask_prob

    A       :   Optional[int]					=						'''Wav2Vec2FeatureExtractor'''
    A       :   Union[str, Any]					=						'''Wav2Vec2CTCTokenizer'''

    return config







@torch.no_grad()
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=True						):




    '''simple docstring'''



    if is_finetuned:
        A,  A,  A       :   Any					=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]		,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
    else:
        A,  A,  A       :   int					=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

    if config_path is not None:
        A       :   List[str]					=						SEWConfig.from_pretrained(snake_case__						)
    else:
        A       :   Dict					=						convert_config(model[0]		,				snake_case__						)
    A       :   str					=						model[0].eval()

    A       :   Union[str, Any]					=						True if config.feat_extract_norm == '''layer''' else False
    A       :   Optional[int]					=						WavaVecaFeatureExtractor(
        feature_size=1		,				sampling_rate=1_6000		,				padding_value=0		,				do_normalize=snake_case__		,				return_attention_mask=snake_case__		,				)

    if is_finetuned:
        if dict_path:
            A       :   Optional[Any]					=						Dictionary.load(snake_case__						)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            A       :   Optional[Any]					=						target_dict.pad_index
            A       :   Optional[Any]					=						target_dict.bos_index
            A       :   Union[str, Any]					=						target_dict.pad_index
            A       :   Union[str, Any]					=						target_dict.bos_index
            A       :   int					=						target_dict.eos_index
            A       :   Tuple					=						len(target_dict.symbols						)
            A       :   List[str]					=						os.path.join(snake_case__		,				'''vocab.json'''						)
            if not os.path.isdir(snake_case__						):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__						)						)
                return
            os.makedirs(snake_case__		,				exist_ok=snake_case__						)
            with open(snake_case__		,				'''w'''		,				encoding='''utf-8'''						) as vocab_handle:
                json.dump(target_dict.indices		,				snake_case__						)
            A       :   str					=						WavaVecaCTCTokenizer(
                snake_case__		,				unk_token=target_dict.unk_word		,				pad_token=target_dict.pad_word		,				bos_token=target_dict.bos_word		,				eos_token=target_dict.eos_word		,				word_delimiter_token='''|'''		,				do_lower_case=snake_case__		,				)
            A       :   List[str]					=						WavaVecaProcessor(feature_extractor=snake_case__		,				tokenizer=snake_case__						)
            processor.save_pretrained(snake_case__						)

        A       :   Tuple					=						SEWForCTC(snake_case__						)
    else:
        A       :   Dict					=						SEWModel(snake_case__						)
        feature_extractor.save_pretrained(snake_case__						)

    recursively_load_weights(snake_case__		,				snake_case__		,				snake_case__						)

    hf_model.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				Dict    		=			argparse.ArgumentParser()
      parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
      parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
      parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument(
          '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
      )
      lowercase			:				Any    		=			parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )






'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




