




'''simple docstring'''





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()
__UpperCAmelCase       =		logging.get_logger(__name__)

__UpperCAmelCase       =		"Hello, World!"
__UpperCAmelCase       =		"en_XX"





def       lowerCAmelCase_     (					__A						:							Union[str, Any] ,			__A						:							List[str] ,			__A						:							Tuple ):




			'''simple docstring'''


			snake_case:       Any        =       Path('data_bin' )
			snake_case:       Tuple        =       FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(SCREAMING_SNAKE_CASE_ ).parent ) ,			checkpoint_file=Path(SCREAMING_SNAKE_CASE_ ).name ,			_name='xmod_base' ,			arch='xmod_base' ,			task='multilingual_masked_lm' ,			data_name_or_path=str(SCREAMING_SNAKE_CASE_ ) ,			bpe='sentencepiece' ,			sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE_ ).parent / 'sentencepiece.bpe.model' ) ,			src_dict=str(data_dir / 'dict.txt' ) ,			)
			xmod.eval()  # disable dropout
			print(SCREAMING_SNAKE_CASE_ )

			snake_case:       int        =       xmod.model.encoder.sentence_encoder
			snake_case:       Any        =       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=5_14 ,			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:
						snake_case:       Tuple        =       xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

			print('Our X-MOD config:' ,			SCREAMING_SNAKE_CASE_ )

			snake_case:       List[Any]        =       XmodForSequenceClassification(SCREAMING_SNAKE_CASE_ ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE_ )
			model.eval()

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

			snake_case:       str        =       xmod_sent_encoder.layernorm_embedding.weight
			snake_case:       str        =       xmod_sent_encoder.layernorm_embedding.bias

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

						# self attention
						snake_case:       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.' )

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

						# self-attention output
						snake_case:       Optional[Any]        =       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.' )
						snake_case:       str        =       xmod_layer.self_attn.out_proj.weight
						snake_case:       Optional[Any]        =       xmod_layer.self_attn.out_proj.bias
						snake_case:       List[str]        =       xmod_layer.self_attn_layer_norm.weight
						snake_case:       Union[str, Any]        =       xmod_layer.self_attn_layer_norm.bias

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

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

						# end of layer

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

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

			# Let's check that we get the same results.
			snake_case:       Tuple        =       xmod.encode(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 )  # batch of size 1
			model.roberta.set_default_language(SCREAMING_SNAKE_CASE_ )

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

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


if __name__ == "__main__":
			__UpperCAmelCase       =		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."
			)
			__UpperCAmelCase       =		parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)
'''simple docstring'''


import requests

__snake_case						  =		"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""

def   A_      (       SCREAMING_SNAKE_CASE_						)			->None:
  # fetching a list of articles in json format
  lowercase_     =       requests.get(_NEWS_API + bbc_news_api_key						).json()
  # each article in the list is a dict
  for i, article in enumerate(bbc_news_page["""articles"""] ,	1						):
    print(f"""{i}.) {article["title"]}"""						)


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")

