


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()
_snake_case					:		int   =    logging.get_logger(__name__)

_snake_case					:		Optional[int]   =    "Hello, World!"
_snake_case					:		Tuple   =    "en_XX"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       Path("data_bin"     )
							__snake_case		:      Union[str, Any]										=       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     )

							__snake_case		:      Optional[Any]										=       xmod.model.encoder.sentence_encoder
							__snake_case		:      Optional[int]										=       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:
														__snake_case		:      List[Any]										=       xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

							__snake_case		:      Tuple										=       XmodForSequenceClassification(__lowerCamelCase     ) if classification_head else XmodForMaskedLM(__lowerCamelCase     )
							model.eval()

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

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

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

														# self attention
														__snake_case		:      Optional[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."     )

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

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

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

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

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														__snake_case		:      List[Any]										=       xmod_sent_encoder.layer_norm.weight
														__snake_case		:      Optional[Any]										=       xmod_sent_encoder.layer_norm.bias

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

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

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





from __future__ import annotations

from functools import lru_cache
from math import ceil

_snake_case					:		Tuple      =    100

_snake_case					:		int      =    set(range(3, NUM_PRIMES, 2))
primes.add(2)
_snake_case					:		int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
			if prime not in primes:
						continue
			primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))







@lru_cache(maxsize=1_0_0     )
def     lowerCAmelCase_					( __lowerCamelCase     ):
							if number_to_partition < 0:
														return set()
							elif number_to_partition == 0:
														return {1}

							__snake_case		:      set[int]										=       set()
							__snake_case		:      int
							__snake_case		:      int

							for prime in primes:
														if prime > number_to_partition:
																					continue
														for sub in partition(number_to_partition - prime     ):
																					ret.add(sub * prime     )

							return ret







def     lowerCAmelCase_					( __lowerCamelCase = 5_0_0_0     ):
							for number_to_partition in range(1							,     __lowerCamelCase     ):
														if len(partition(__lowerCamelCase     )     ) > number_unique_partitions:
																					return number_to_partition
							return None


if __name__ == "__main__":
			print(f'''{solution() = }''')

