"""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()
SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE									=						'''Hello, World!'''
SCREAMING_SNAKE_CASE									=						'''en_XX'''


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






							"""simple docstring"""





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

							_lowercase :    List[str]							=							xmod.model.encoder.sentence_encoder
							_lowercase :    List[str]							=							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:
														_lowercase :    Dict							=							xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

							_lowercase :    Optional[Any]							=							XmodForSequenceClassification(_A			) if classification_head else XmodForMaskedLM(_A			)
							model.eval()

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

							_lowercase :    List[Any]							=							xmod_sent_encoder.layernorm_embedding.weight
							_lowercase :    List[Any]							=							xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														_lowercase :    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.'			)

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

														# self-attention output
														_lowercase :    Tuple							=							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 :    Any							=							xmod_layer.self_attn.out_proj.weight
														_lowercase :    Optional[int]							=							xmod_layer.self_attn.out_proj.bias
														_lowercase :    List[Any]							=							xmod_layer.self_attn_layer_norm.weight
														_lowercase :    Optional[Any]							=							xmod_layer.self_attn_layer_norm.bias

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

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

														# end of layer

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

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

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

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

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


if __name__ == "__main__":
						SCREAMING_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.'
						)
						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if index == len(__UpperCAmelCase			):
		return True

	# Recursive Step
	for i in range(__UpperCAmelCase			):
		if valid_coloring(graph[index]     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			# Color current vertex
			_lowercase :    str							=							i
			# Validate coloring
			if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,index + 1			):
				return True
			# Backtrack
			_lowercase :    List[str]							=							-1
	return False







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[-1] * len(__UpperCAmelCase			)

	if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,0			):
		return colored_vertices

	return []



