





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      (    a,     a,     a) ->Tuple:
					lowerCamelCase__       =       Path("data_bin")
					lowerCamelCase__       =       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)

					lowerCamelCase__       =       xmod.model.encoder.sentence_encoder
					lowerCamelCase__       =       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:
										lowerCamelCase__       =       xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

					lowerCamelCase__       =       XmodForSequenceClassification(a) if classification_head else XmodForMaskedLM(a)
					model.eval()

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

					lowerCamelCase__       =       xmod_sent_encoder.layernorm_embedding.weight
					lowerCamelCase__       =       xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										lowerCamelCase__       =       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.")

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

										# self-attention output
										lowerCamelCase__       =       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.")
										lowerCamelCase__       =       xmod_layer.self_attn.out_proj.weight
										lowerCamelCase__       =       xmod_layer.self_attn.out_proj.bias
										lowerCamelCase__       =       xmod_layer.self_attn_layer_norm.weight
										lowerCamelCase__       =       xmod_layer.self_attn_layer_norm.bias

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

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

										# end of layer

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

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

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

					lowerCamelCase__       =       model(a)[0]
					if classification_head:
										lowerCamelCase__       =       xmod.model.classification_heads["mnli"](xmod.extract_features(a))
					else:
										lowerCamelCase__       =       xmod.model(a,     lang_id=[SAMPLE_LANGUAGE])[0]
					print(our_output.shape,     their_output.shape)
					lowerCamelCase__       =       torch.max(torch.abs(our_output - their_output)).item()
					print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
					lowerCamelCase__       =       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__":
						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
						)








A_												=     {str(digit): digit**5 for digit in range(1_0)}





def    __UpperCamelCase      (    a) ->int:
					return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a))





def    __UpperCamelCase      (    ) ->int:
					return sum(
					    number
					    for number in range(1000,     1000000)
					    if number == digits_fifth_powers_sum(a))


if __name__ == "__main__":
						print(solution())

