




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()
__a    :						Any						      =       logging.get_logger(__name__)

__a    :						Union[str, Any]						      =       '''Hello, World!'''
__a    :						Dict						      =       '''en_XX'''
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					List[Any]:
							lowercase__    :			str     =				Path("data_bin"							)
							lowercase__    :			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__							)

							lowercase__    :			Optional[int]     =				xmod.model.encoder.sentence_encoder
							lowercase__    :			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:
														lowercase__    :			Dict     =				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

							lowercase__    :			Union[str, Any]     =				XmodForSequenceClassification(lowerCAmelCase__							) if classification_head else XmodForMaskedLM(lowerCAmelCase__							)
							model.eval()

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

							lowercase__    :			Dict     =				xmod_sent_encoder.layernorm_embedding.weight
							lowercase__    :			Tuple     =				xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers							):
														# Encoder: start of layer
														lowercase__    :			Dict     =				model.roberta.encoder.layer[i]
														lowercase__    :			Union[str, Any]     =				xmod_sent_encoder.layers[i]

														# self attention
														lowercase__    :			List[str]     =				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."							)

														lowercase__    :			Optional[Any]     =				xmod_layer.self_attn.q_proj.weight
														lowercase__    :			int     =				xmod_layer.self_attn.q_proj.bias
														lowercase__    :			List[str]     =				xmod_layer.self_attn.k_proj.weight
														lowercase__    :			Any     =				xmod_layer.self_attn.k_proj.bias
														lowercase__    :			str     =				xmod_layer.self_attn.v_proj.weight
														lowercase__    :			Optional[int]     =				xmod_layer.self_attn.v_proj.bias

														# self-attention output
														lowercase__    :			Optional[int]     =				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."							)
														lowercase__    :			Dict     =				xmod_layer.self_attn.out_proj.weight
														lowercase__    :			List[Any]     =				xmod_layer.self_attn.out_proj.bias
														lowercase__    :			Optional[int]     =				xmod_layer.self_attn_layer_norm.weight
														lowercase__    :			Optional[Any]     =				xmod_layer.self_attn_layer_norm.bias

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

														# output
														lowercase__    :			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."							)
														lowercase__    :			str     =				xmod_layer.fca.weight
														lowercase__    :			int     =				xmod_layer.fca.bias
														lowercase__    :			Optional[Any]     =				xmod_layer.final_layer_norm.weight
														lowercase__    :			List[Any]     =				xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					lowercase__    :			Tuple     =				xmod_layer.adapter_layer_norm.weight
																					lowercase__    :			Tuple     =				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():
																					lowercase__    :			List[Any]     =				bert_output.adapter_modules[lang_code]
																					lowercase__    :			Tuple     =				xmod_layer.adapter_modules[lang_code]
																					lowercase__    :			Tuple     =				from_adapter.fca.weight
																					lowercase__    :			str     =				from_adapter.fca.bias
																					lowercase__    :			List[str]     =				from_adapter.fca.weight
																					lowercase__    :			Union[str, Any]     =				from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														lowercase__    :			Any     =				xmod_sent_encoder.layer_norm.weight
														lowercase__    :			Any     =				xmod_sent_encoder.layer_norm.bias

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

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

							lowercase__    :			str     =				model(lowerCAmelCase__							)[0]
							if classification_head:
														lowercase__    :			str     =				xmod.model.classification_heads["mnli"](xmod.extract_features(lowerCAmelCase__							)							)
							else:
														lowercase__    :			Any     =				xmod.model(lowerCAmelCase__							,lang_id=[SAMPLE_LANGUAGE]							)[0]
							print(our_output.shape							,their_output.shape							)
							lowercase__    :			Tuple     =				torch.max(torch.abs(our_output - their_output							)							).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""							)  # ~ 1e-7
							lowercase__    :			Tuple     =				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__":
						__a    :						Dict						      =       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.'''
						)
						__a    :						Union[str, Any]						      =       parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)






"""simple docstring"""






from __future__ import annotations






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			tuple[int, int]:



			'''simple docstring'''
			if b == 0:
						return (1, 0)
			((a__) ,	(a__))  :	List[Any]		 =			extended_euclid(lowerCAmelCase__							,			a % b			)
			a__  :	str		 =			a // b
			return (y, x - k * y)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			((a__) ,	(a__))  :	Tuple		 =			extended_euclid(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	List[str]		 =			na * na
			a__  :	Union[str, Any]		 =			ra * x * na + ra * y * na
			return (n % m + m) % m






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			((a__) ,	(a__))  :	Optional[Any]		 =			extended_euclid(lowerCAmelCase__							,			lowerCAmelCase__			)
			if b < 0:
						a__  :	Optional[int]		 =			(b % n + n) % n
			return b






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__ ,	a__  :	List[Any]		 =			invert_modulo(lowerCAmelCase__							,			lowerCAmelCase__			), invert_modulo(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	Dict		 =			na * na
			a__  :	Any		 =			ra * x * na + ra * y * na
			return (n % m + m) % m


if __name__ == "__main__":
		from doctest import testmod

		testmod(name='''chinese_remainder_theorem''', verbose=True)
		testmod(name='''chinese_remainder_theorem2''', verbose=True)
		testmod(name='''invert_modulo''', verbose=True)
		testmod(name='''extended_euclid''', verbose=True)