

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

__snake_case							:	int											=       {
    """post_extract_proj""": """feature_projection.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.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
__snake_case							:	Optional[int]											=       [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__   ,     a__   ,     a__):

			'''simple docstring'''



			for attribute in key.split("""."""):
						if is_finetuned:
									if attribute in ["quantizer", "project_q", "project_hid"]:
												# those layers are only relevant for pretraining and should be dropped
												return

									if attribute == "ctc_proj":
												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
												a_		:		str      				=      """lm_head"""

						a_		:		Dict      				=      getattr(__snake_case   ,     __snake_case)

			if weight_type is not None:
						a_		:		Any      				=      getattr(__snake_case   ,     __snake_case).shape
			else:
						a_		:		int      				=      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_		:		Tuple      				=      value
			elif weight_type == "weight_g":
						a_		:		Union[str, Any]      				=      value
			elif weight_type == "weight_v":
						a_		:		int      				=      value
			elif weight_type == "bias":
						a_		:		Union[str, Any]      				=      value
			else:
						a_		:		Any      				=      value

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


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Optional[Any]      				=      []
			a_		:		Any      				=      fairseq_model.state_dict()

			a_		:		List[Any]      				=      hf_model.unispeech.feature_extractor

			for name, value in fairseq_dict.items():
						a_		:		Dict      				=      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_		:		Dict      				=      """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												if key in name or key.split("""w2v_model.""")[-1] == name.split(""".""")[0]:
															a_		:		Any      				=      True
															if "*" in mapped_key:
																		a_		:		Tuple      				=      name.split(__snake_case)[0].split(""".""")[-2]
																		a_		:		Any      				=      mapped_key.replace("""*"""   ,     __snake_case)
															if "weight_g" in name:
																		a_		:		Union[str, Any]      				=      """weight_g"""
															elif "weight_v" in name:
																		a_		:		List[str]      				=      """weight_v"""
															elif "bias" in name:
																		a_		:		Union[str, Any]      				=      """bias"""
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a_		:		Optional[int]      				=      """weight"""
															else:
																		a_		:		Union[str, Any]      				=      None
															set_recursively(__snake_case   ,     __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  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		List[str]      				=      full_name.split("""conv_layers.""")[-1]
			a_		:		Tuple      				=      name.split(""".""")
			a_		:		Optional[int]      				=      int(items[0])
			a_		:		List[str]      				=      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[int]      				=      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_		:		Union[str, Any]      				=      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_		:		Optional[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_		:		Any      				=      value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
			else:
						unused_weights.append(__snake_case)


@torch.no_grad()
def  _UpperCAmelCase							(		a__   ,     a__   ,     a__=None   ,     a__=None   ,     a__=True):

			'''simple docstring'''



			if config_path is not None:
						a_		:		int      				=      UniSpeechConfig.from_pretrained(__snake_case)
			else:
						a_		:		Optional[Any]      				=      UniSpeechConfig()

			if is_finetuned:
						if dict_path:
									a_		:		int      				=      Dictionary.load_from_json(__snake_case)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									a_		:		Union[str, Any]      				=      target_dict.pad_index
									a_		:		List[Any]      				=      target_dict.bos_index
									a_		:		Optional[int]      				=      target_dict.eos_index
									a_		:		List[str]      				=      len(target_dict.symbols)
									a_		:		Dict      				=      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)
									a_		:		int      				=      target_dict.indices

									# fairseq has the <pad> and <s> switched
									a_		:		int      				=      4_2
									a_		:		Optional[Any]      				=      4_3
									with open(__snake_case   ,     """w"""   ,     encoding="""utf-8""") as vocab_handle:
												json.dump(__snake_case   ,     __snake_case)
									a_		:		Tuple      				=      WavaVecaPhonemeCTCTokenizer(
									    __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_		:		Union[str, Any]      				=      True if config.feat_extract_norm == """layer""" else False
									a_		:		Optional[Any]      				=      WavaVecaFeatureExtractor(
									    feature_size=1   ,     sampling_rate=1_6_0_0_0   ,     padding_value=0   ,     do_normalize=__snake_case   ,     return_attention_mask=__snake_case   ,     )
									a_		:		Optional[Any]      				=      WavaVecaProcessor(feature_extractor=__snake_case   ,     tokenizer=__snake_case)
									processor.save_pretrained(__snake_case)

						a_		:		Dict      				=      UniSpeechForCTC(__snake_case)
			else:
						a_		:		Any      				=      UniSpeechForPreTraining(__snake_case)

			if is_finetuned:
						a_				, a_				, a_		:		Tuple      				=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]   ,     arg_overrides={"""data""": """/""".join(dict_path.split("""/""")[:-1]), """w2v_path""": checkpoint_path})
			else:
						a_				, a_				, a_		:		str      				=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

			a_		:		int      				=      model[0].eval()

			recursively_load_weights(__snake_case   ,     __snake_case   ,     __snake_case)

			hf_unispeech.save_pretrained(__snake_case)


if __name__ == "__main__":
	__snake_case							:	str											=       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(
	    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	__snake_case							:	Tuple											=       parser.parse_args()
	convert_unispeech_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)






import requests
from bsa import BeautifulSoup
def 				UpperCAmelCase_					(							__snake_case = "https://www.worldometers.info/coronavirus"						)     ->			dict:







 """simple docstring"""


 _lowercase							      =BeautifulSoup(requests.get(__snake_case						).text     ,						'''html.parser'''						)
 _lowercase							      =soup.findAll('''h1'''						)
 _lowercase							      =soup.findAll('''div'''     ,						{'''class''': '''maincounter-number'''}						)
 keys += soup.findAll('''span'''     ,						{'''class''': '''panel-title'''}						)
 values += soup.findAll('''div'''     ,						{'''class''': '''number-table-main'''}						)
 return {key.text.strip(): value.text.strip() for key, value in zip(__snake_case     ,						__snake_case						)}


if __name__ == "__main__":
  print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
  for key, value in world_covidaa_stats().items():
    print(f'''{key}\n{value}\n''')

