
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()
_lowercase			:  Optional[Any]   							=	logging.get_logger(__name__)

_lowercase			:  Union[str, Any]   							=	"Hello, World!"
_lowercase			:  Optional[Any]   							=	"en_XX"
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			bool							):



						"""simple docstring"""





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

						lowercase_     :					List[Any]													=	xmod.model.encoder.sentence_encoder
						lowercase_     :					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=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_     :					Union[str, Any]													=	xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

						lowercase_     :					Any													=	XmodForSequenceClassification(__SCREAMING_SNAKE_CASE							) if classification_head else XmodForMaskedLM(__SCREAMING_SNAKE_CASE							)
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						lowercase_     :					List[Any]													=	xmod_sent_encoder.embed_tokens.weight
						lowercase_     :					Any													=	xmod_sent_encoder.embed_positions.weight
						lowercase_     :					Union[str, Any]													=	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_     :					str													=	model.roberta.encoder.layer[i]
												lowercase_     :					Union[str, Any]													=	xmod_sent_encoder.layers[i]

												# self attention
												lowercase_     :					Union[str, Any]													=	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_     :					Any													=	xmod_layer.self_attn.q_proj.bias
												lowercase_     :					Optional[int]													=	xmod_layer.self_attn.k_proj.weight
												lowercase_     :					int													=	xmod_layer.self_attn.k_proj.bias
												lowercase_     :					int													=	xmod_layer.self_attn.v_proj.weight
												lowercase_     :					int													=	xmod_layer.self_attn.v_proj.bias

												# self-attention output
												lowercase_     :					List[str]													=	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_     :					Optional[Any]													=	xmod_layer.self_attn.out_proj.weight
												lowercase_     :					Optional[Any]													=	xmod_layer.self_attn.out_proj.bias
												lowercase_     :					Union[str, Any]													=	xmod_layer.self_attn_layer_norm.weight
												lowercase_     :					Any													=	xmod_layer.self_attn_layer_norm.bias

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

												# output
												lowercase_     :					List[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_     :					Tuple													=	xmod_layer.fca.weight
												lowercase_     :					Dict													=	xmod_layer.fca.bias
												lowercase_     :					int													=	xmod_layer.final_layer_norm.weight
												lowercase_     :					str													=	xmod_layer.final_layer_norm.bias
												if bert_output.adapter_layer_norm is not None:
																		lowercase_     :					List[Any]													=	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_     :					Tuple													=	bert_output.adapter_modules[lang_code]
																		lowercase_     :					Optional[int]													=	xmod_layer.adapter_modules[lang_code]
																		lowercase_     :					Optional[int]													=	from_adapter.fca.weight
																		lowercase_     :					Dict													=	from_adapter.fca.bias
																		lowercase_     :					Optional[Any]													=	from_adapter.fca.weight
																		lowercase_     :					int													=	from_adapter.fca.bias

												# end of layer

						if xmod_sent_encoder.layer_norm is not None:
												lowercase_     :					Union[str, Any]													=	xmod_sent_encoder.layer_norm.weight
												lowercase_     :					List[str]													=	xmod_sent_encoder.layer_norm.bias

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

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

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

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


if __name__ == "__main__":
							_lowercase			:  Optional[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."
							)
							_lowercase			:  Any   							=	parser.parse_args()
							convert_xmod_checkpoint_to_pytorch(
							    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)





'''simple docstring'''







import sys

_lowercase			:  Union[str, Any]   							=	(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Any													=	1
						for digit in s:
												product *= int(__SCREAMING_SNAKE_CASE							)
						return product
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str = N							):



						"""simple docstring"""





						lowercase_     :					Any													=	-sys.maxsize - 1
						lowercase_     :					List[Any]													=	n[:13]
						lowercase_     :					List[str]													=	13
						while cur_index < len(__SCREAMING_SNAKE_CASE							) - 13:
												if int(n[cur_index]							) >= int(substr[0]							):
																		lowercase_     :					Tuple													=	substr[1:] + n[cur_index]
																		cur_index += 1
												else:
																		lowercase_     :					str													=	max(__SCREAMING_SNAKE_CASE				,				str_eval(__SCREAMING_SNAKE_CASE							)							)
																		lowercase_     :					List[Any]													=	n[cur_index : cur_index + 13]
																		cur_index += 13
						return largest_product


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



