


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					  =     logging.get_logger(__name__)

a					  =     'Hello, World!'
a					  =     'en_XX'


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			Path("""data_bin"""  )
					lowercase_  							=			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__  )

					lowercase_  							=			xmod.model.encoder.sentence_encoder
					lowercase_  							=			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_1_4						,							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_  							=			xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

					lowercase_  							=			XmodForSequenceClassification(UpperCAmelCase__  ) if classification_head else XmodForMaskedLM(UpperCAmelCase__  )
					model.eval()

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

					lowercase_  							=			xmod_sent_encoder.layernorm_embedding.weight
					lowercase_  							=			xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										lowercase_  							=			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_  							=			xmod_layer.self_attn.q_proj.weight
										lowercase_  							=			xmod_layer.self_attn.q_proj.bias
										lowercase_  							=			xmod_layer.self_attn.k_proj.weight
										lowercase_  							=			xmod_layer.self_attn.k_proj.bias
										lowercase_  							=			xmod_layer.self_attn.v_proj.weight
										lowercase_  							=			xmod_layer.self_attn.v_proj.bias

										# self-attention output
										lowercase_  							=			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_  							=			xmod_layer.self_attn.out_proj.weight
										lowercase_  							=			xmod_layer.self_attn.out_proj.bias
										lowercase_  							=			xmod_layer.self_attn_layer_norm.weight
										lowercase_  							=			xmod_layer.self_attn_layer_norm.bias

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

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

										# end of layer

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

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

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

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



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										raise TypeError("""Input value must be an 'int' type"""  )

					lowercase_  							=			0
					while number:
										position += 1
										number >>= 1

					return position


if __name__ == "__main__":
							import doctest

							doctest.testmod()


