





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

__UpperCAmelCase						=			'''Hello, World!'''
__UpperCAmelCase						=			'''en_XX'''






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			bool			)    ->			List[Any]:



			'''simple docstring'''
			a__  :	str		 =			Path("data_bin"			)
			a__  :	str		 =			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__			)

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

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

			a__  :	Union[str, Any]		 =			XmodForSequenceClassification(lowerCAmelCase__			) if classification_head else XmodForMaskedLM(lowerCAmelCase__			)
			model.eval()

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

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

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

						# self attention
						a__  :	List[str]		 =			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."			)

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

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

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

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

						# end of layer

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

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

			# Let's check that we get the same results.
			a__  :	List[str]		 =			xmod.encode(lowerCAmelCase__			).unsqueeze(0			)  # batch of size 1
			model.roberta.set_default_language(lowerCAmelCase__			)

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






"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase						=			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						=			[
		    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InformerForPrediction''',
		    '''InformerModel''',
		    '''InformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_informer import (
				    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InformerForPrediction,
				    InformerModel,
				    InformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)