


"""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						:				List[Any]             =      logging.get_logger(__name__)

UpperCamelCase						:				Dict             =      "Hello, World!"
UpperCamelCase						:				Optional[int]             =      "en_XX"







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





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

		A					       = xmod.model.encoder.sentence_encoder
		A					       = 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:
				A					       = xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

		A					       = XmodForSequenceClassification(UpperCamelCase__	) if classification_head else XmodForMaskedLM(UpperCamelCase__	)
		model.eval()

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

		A					       = xmod_sent_encoder.layernorm_embedding.weight
		A					       = xmod_sent_encoder.layernorm_embedding.bias

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

				# self attention
				A					       = 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.'	)

				A					       = xmod_layer.self_attn.q_proj.weight
				A					       = xmod_layer.self_attn.q_proj.bias
				A					       = xmod_layer.self_attn.k_proj.weight
				A					       = xmod_layer.self_attn.k_proj.bias
				A					       = xmod_layer.self_attn.v_proj.weight
				A					       = xmod_layer.self_attn.v_proj.bias

				# self-attention output
				A					       = 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.'	)
				A					       = xmod_layer.self_attn.out_proj.weight
				A					       = xmod_layer.self_attn.out_proj.bias
				A					       = xmod_layer.self_attn_layer_norm.weight
				A					       = xmod_layer.self_attn_layer_norm.bias

				# intermediate
				A					       = layer.intermediate
				if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError('Dimensions of intermediate weights do not match.'	)
				A					       = xmod_layer.fca.weight
				A					       = xmod_layer.fca.bias

				# output
				A					       = layer.output
				if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError('Dimensions of feed-forward weights do not match.'	)
				A					       = xmod_layer.fca.weight
				A					       = xmod_layer.fca.bias
				A					       = xmod_layer.final_layer_norm.weight
				A					       = xmod_layer.final_layer_norm.bias
				if bert_output.adapter_layer_norm is not None:
						A					       = xmod_layer.adapter_layer_norm.weight
						A					       = 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():
						A					       = bert_output.adapter_modules[lang_code]
						A					       = xmod_layer.adapter_modules[lang_code]
						A					       = from_adapter.fca.weight
						A					       = from_adapter.fca.bias
						A					       = from_adapter.fca.weight
						A					       = from_adapter.fca.bias

				# end of layer

		if xmod_sent_encoder.layer_norm is not None:
				A					       = xmod_sent_encoder.layer_norm.weight
				A					       = xmod_sent_encoder.layer_norm.bias

		if classification_head:
				A					       = xmod.model.classification_heads['mnli'].dense.weight
				A					       = xmod.model.classification_heads['mnli'].dense.bias
				A					       = xmod.model.classification_heads['mnli'].out_proj.weight
				A					       = xmod.model.classification_heads['mnli'].out_proj.bias
		else:
				# LM Head
				A					       = xmod.model.encoder.lm_head.dense.weight
				A					       = xmod.model.encoder.lm_head.dense.bias
				A					       = xmod.model.encoder.lm_head.layer_norm.weight
				A					       = xmod.model.encoder.lm_head.layer_norm.bias
				A					       = xmod.model.encoder.lm_head.weight
				A					       = xmod.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
	UpperCamelCase						:				Any             =      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						:				str             =      parser.parse_args()
	convert_xmod_checkpoint_to_pytorch(
	    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)









"""simple docstring"""


from datetime import datetime as dt
import os

from github import Github


UpperCamelCase						:				Union[str, Any]             =      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def  __snake_case       (   )      ->		int:







		"""simple docstring"""





		A					       = Github(os.environ['GITHUB_TOKEN']	)
		A					       = g.get_repo('huggingface/transformers'	)
		A					       = repo.get_issues(state='open'	)

		for issue in open_issues:
				A					       = sorted([comment for comment in issue.get_comments()] ,			key=lambda UpperCamelCase__	: i.created_at ,			reverse=UpperCamelCase__	)
				A					       = comments[0] if len(UpperCamelCase__	) > 0 else None
				if (
				    last_comment is not None
				    and last_comment.user.login == "github-actions[bot]"
				    and (dt.utcnow() - issue.updated_at).days > 7
				    and (dt.utcnow() - issue.created_at).days >= 30
				    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
				):
						# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
						issue.edit(state='closed'	)
				elif (
				    (dt.utcnow() - issue.updated_at).days > 23
				    and (dt.utcnow() - issue.created_at).days >= 30
				    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
				):
						# print(f"Would add stale comment to {issue.number}")
						issue.create_comment(
						    'This issue has been automatically marked as stale because it has not had '
						    'recent activity. If you think this still needs to be addressed '
						    'please comment on this thread.\n\nPlease note that issues that do not follow the '
						    '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
						    'are likely to be ignored.'	)


if __name__ == "__main__":
	main()





