



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
						raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
						raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
_snake_case	=						logging.get_logger(__name__)

_snake_case	=						"Hello, World!"
_snake_case	=						"en_XX"






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					Path("data_bin"						)
							_lowerCAmelCase  :							List[str]				     =					FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(_lowerCamelCase						).parent						)       ,				checkpoint_file=Path(_lowerCamelCase						).name       ,				_name="xmod_base"       ,				arch="xmod_base"       ,				task="multilingual_masked_lm"       ,				data_name_or_path=str(_lowerCamelCase						)       ,				bpe="sentencepiece"       ,				sentencepiece_model=str(Path(_lowerCamelCase						).parent / "sentencepiece.bpe.model"						)       ,				src_dict=str(data_dir / "dict.txt"						)       ,				)
							xmod.eval()  # disable dropout
							print(_lowerCamelCase						)

							_lowerCAmelCase  :							List[Any]				     =					xmod.model.encoder.sentence_encoder
							_lowerCAmelCase  :							Optional[int]				     =					XmodConfig(
							    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings       ,				hidden_size=xmod.cfg.model.encoder_embed_dim       ,				num_hidden_layers=xmod.cfg.model.encoder_layers       ,				num_attention_heads=xmod.cfg.model.encoder_attention_heads       ,				intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim       ,				max_position_embeddings=514       ,				type_vocab_size=1       ,				layer_norm_eps=1e-5       ,				pre_norm=xmod.cfg.model.encoder_normalize_before       ,				adapter_reduction_factor=getattr(xmod.cfg.model       ,				"bottleneck"       ,				2						)       ,				adapter_layer_norm=xmod.cfg.model.adapter_layer_norm       ,				adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm       ,				ln_before_adapter=xmod.cfg.model.ln_before_adapter       ,				languages=xmod.cfg.model.languages       ,				)
							if classification_head:
														_lowerCAmelCase  :							Any				     =					xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

							print("Our X-MOD config:"       ,				_lowerCamelCase						)

							_lowerCAmelCase  :							Any				     =					XmodForSequenceClassification(_lowerCamelCase						) if classification_head else XmodForMaskedLM(_lowerCamelCase						)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							_lowerCAmelCase  :							int				     =					xmod_sent_encoder.embed_tokens.weight
							_lowerCAmelCase  :							Dict				     =					xmod_sent_encoder.embed_positions.weight
							_lowerCAmelCase  :							int				     =					torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c xmod doesn't use them.

							_lowerCAmelCase  :							Optional[Any]				     =					xmod_sent_encoder.layernorm_embedding.weight
							_lowerCAmelCase  :							Optional[Any]				     =					xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers						):
														# Encoder: start of layer
														_lowerCAmelCase  :							int				     =					model.roberta.encoder.layer[i]
														_lowerCAmelCase  :							int				     =					xmod_sent_encoder.layers[i]

														# self attention
														_lowerCAmelCase  :							Optional[int]				     =					layer.attention.self
														if not (
														    xmod_layer.self_attn.k_proj.weight.data.shape
														    == xmod_layer.self_attn.q_proj.weight.data.shape
														    == xmod_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)						)
														):
																					raise AssertionError("Dimensions of self-attention weights do not match."						)

														_lowerCAmelCase  :							Optional[Any]				     =					xmod_layer.self_attn.q_proj.weight
														_lowerCAmelCase  :							Union[str, Any]				     =					xmod_layer.self_attn.q_proj.bias
														_lowerCAmelCase  :							Any				     =					xmod_layer.self_attn.k_proj.weight
														_lowerCAmelCase  :							Tuple				     =					xmod_layer.self_attn.k_proj.bias
														_lowerCAmelCase  :							str				     =					xmod_layer.self_attn.v_proj.weight
														_lowerCAmelCase  :							Optional[Any]				     =					xmod_layer.self_attn.v_proj.bias

														# self-attention output
														_lowerCAmelCase  :							str				     =					layer.attention.output
														if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
																					raise AssertionError("Dimensions of self-attention output weights do not match."						)
														_lowerCAmelCase  :							Optional[int]				     =					xmod_layer.self_attn.out_proj.weight
														_lowerCAmelCase  :							Union[str, Any]				     =					xmod_layer.self_attn.out_proj.bias
														_lowerCAmelCase  :							Any				     =					xmod_layer.self_attn_layer_norm.weight
														_lowerCAmelCase  :							Optional[int]				     =					xmod_layer.self_attn_layer_norm.bias

														# intermediate
														_lowerCAmelCase  :							List[str]				     =					layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("Dimensions of intermediate weights do not match."						)
														_lowerCAmelCase  :							Any				     =					xmod_layer.fca.weight
														_lowerCAmelCase  :							List[str]				     =					xmod_layer.fca.bias

														# output
														_lowerCAmelCase  :							int				     =					layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("Dimensions of feed-forward weights do not match."						)
														_lowerCAmelCase  :							List[str]				     =					xmod_layer.fca.weight
														_lowerCAmelCase  :							Dict				     =					xmod_layer.fca.bias
														_lowerCAmelCase  :							Optional[Any]				     =					xmod_layer.final_layer_norm.weight
														_lowerCAmelCase  :							Dict				     =					xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					_lowerCAmelCase  :							Any				     =					xmod_layer.adapter_layer_norm.weight
																					_lowerCAmelCase  :							List[Any]				     =					xmod_layer.adapter_layer_norm.bias

														if sorted(bert_output.adapter_modules.keys()						) != sorted(xmod_layer.adapter_modules.keys()						):
																					raise AssertionError("Lists of language adapters do not match."						)
														for lang_code, adapter in xmod_layer.adapter_modules.items():
																					_lowerCAmelCase  :							List[Any]				     =					bert_output.adapter_modules[lang_code]
																					_lowerCAmelCase  :							List[Any]				     =					xmod_layer.adapter_modules[lang_code]
																					_lowerCAmelCase  :							Dict				     =					from_adapter.fca.weight
																					_lowerCAmelCase  :							Dict				     =					from_adapter.fca.bias
																					_lowerCAmelCase  :							List[Any]				     =					from_adapter.fca.weight
																					_lowerCAmelCase  :							Optional[int]				     =					from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														_lowerCAmelCase  :							int				     =					xmod_sent_encoder.layer_norm.weight
														_lowerCAmelCase  :							str				     =					xmod_sent_encoder.layer_norm.bias

							if classification_head:
														_lowerCAmelCase  :							Optional[Any]				     =					xmod.model.classification_heads["mnli"].dense.weight
														_lowerCAmelCase  :							List[str]				     =					xmod.model.classification_heads["mnli"].dense.bias
														_lowerCAmelCase  :							int				     =					xmod.model.classification_heads["mnli"].out_proj.weight
														_lowerCAmelCase  :							Any				     =					xmod.model.classification_heads["mnli"].out_proj.bias
							else:
														# LM Head
														_lowerCAmelCase  :							Union[str, Any]				     =					xmod.model.encoder.lm_head.dense.weight
														_lowerCAmelCase  :							List[str]				     =					xmod.model.encoder.lm_head.dense.bias
														_lowerCAmelCase  :							List[Any]				     =					xmod.model.encoder.lm_head.layer_norm.weight
														_lowerCAmelCase  :							Optional[int]				     =					xmod.model.encoder.lm_head.layer_norm.bias
														_lowerCAmelCase  :							List[str]				     =					xmod.model.encoder.lm_head.weight
														_lowerCAmelCase  :							Tuple				     =					xmod.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							_lowerCAmelCase  :							List[Any]				     =					xmod.encode(_lowerCamelCase						).unsqueeze(0						)  # batch of size 1
							model.roberta.set_default_language(_lowerCamelCase						)

							_lowerCAmelCase  :							List[Any]				     =					model(_lowerCamelCase						)[0]
							if classification_head:
														_lowerCAmelCase  :							Dict				     =					xmod.model.classification_heads["mnli"](xmod.extract_features(_lowerCamelCase						)						)
							else:
														_lowerCAmelCase  :							Any				     =					xmod.model(_lowerCamelCase       ,				lang_id=[SAMPLE_LANGUAGE]						)[0]
							print(our_output.shape       ,				their_output.shape						)
							_lowerCAmelCase  :							int				     =					torch.max(torch.abs(our_output - their_output						)						).item()
							print(F"max_absolute_diff = {max_absolute_diff}"						)  # ~ 1e-7
							_lowerCAmelCase  :							List[Any]				     =					torch.allclose(_lowerCamelCase       ,				_lowerCamelCase       ,				atol=1e-3						)
							print("Do both models output the same tensors?"       ,				"🔥" if success else "💩"						)
							if not success:
														raise Exception("Something went wRoNg"						)

							Path(_lowerCamelCase						).mkdir(parents=_lowerCamelCase       ,				exist_ok=_lowerCamelCase						)
							print(F"Saving model to {pytorch_dump_folder_path}"						)
							model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
						_snake_case	=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--classification_head", action="store_true", help="Whether to convert a final classification head."
						)
						_snake_case	=						parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)





import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase = "https://www.worldometers.info/coronavirus"						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					BeautifulSoup(requests.get(_lowerCamelCase						).text       ,				"html.parser"						)
							_lowerCAmelCase  :							str				     =					soup.findAll("h1"						)
							_lowerCAmelCase  :							Optional[int]				     =					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(_lowerCamelCase       ,				_lowerCamelCase						)}


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''')
