from ..utils import DummyObject, requires_backends
class   _lowerCAmelCase		(     metaclass=__SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ['torch', 'transformers', 'onnx']







							def __init__(				self     :    Union[str, Any]	,					*__snake_case     :    Optional[Any]	,					**__snake_case     :    str		):
										requires_backends(self	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Union[str, Any]	,					*__snake_case     :    List[str]	,					**__snake_case     :    List[str]		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Tuple	,					*__snake_case     :    Dict	,					**__snake_case     :    Tuple		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)


class   _lowerCAmelCase		(     metaclass=__SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ['torch', 'transformers', 'onnx']







							def __init__(				self     :    List[Any]	,					*__snake_case     :    Any	,					**__snake_case     :    List[Any]		):
										requires_backends(self	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Any	,					*__snake_case     :    str	,					**__snake_case     :    int		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Any	,					*__snake_case     :    Any	,					**__snake_case     :    Tuple		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)


class   _lowerCAmelCase		(     metaclass=__SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ['torch', 'transformers', 'onnx']







							def __init__(				self     :    int	,					*__snake_case     :    Dict	,					**__snake_case     :    List[str]		):
										requires_backends(self	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    str	,					*__snake_case     :    Tuple	,					**__snake_case     :    Any		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Any	,					*__snake_case     :    str	,					**__snake_case     :    Any		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)


class   _lowerCAmelCase		(     metaclass=__SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ['torch', 'transformers', 'onnx']







							def __init__(				self     :    Tuple	,					*__snake_case     :    Any	,					**__snake_case     :    Union[str, Any]		):
										requires_backends(self	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Any	,					*__snake_case     :    Union[str, Any]	,					**__snake_case     :    Union[str, Any]		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    int	,					*__snake_case     :    Dict	,					**__snake_case     :    Union[str, Any]		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)


class   _lowerCAmelCase		(     metaclass=__SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ['torch', 'transformers', 'onnx']







							def __init__(				self     :    Any	,					*__snake_case     :    Any	,					**__snake_case     :    str		):
										requires_backends(self	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Any	,					*__snake_case     :    Tuple	,					**__snake_case     :    Dict		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Optional[Any]	,					*__snake_case     :    Dict	,					**__snake_case     :    str		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)


class   _lowerCAmelCase		(     metaclass=__SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ['torch', 'transformers', 'onnx']







							def __init__(				self     :    List[str]	,					*__snake_case     :    str	,					**__snake_case     :    int		):
										requires_backends(self	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    int	,					*__snake_case     :    Tuple	,					**__snake_case     :    Optional[Any]		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)







							@classmethod
							def 				snake_case  (				cls     :    Dict	,					*__snake_case     :    int	,					**__snake_case     :    str		):
										requires_backends(cls	,					['''torch''', '''transformers''', '''onnx''']		)




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  _lowerCamelCase					(   a_				: str						,			a_				: str						,			a_				: bool):
			lowerCamelCase						:int						  =     Path('''data_bin''')
			lowerCamelCase						:Union[str, Any]						  =     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						:Any						  =     xmod.model.encoder.sentence_encoder
			lowerCamelCase						:List[str]						  =     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_14						,			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						:Dict						  =     xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

			lowerCamelCase						:List[Any]						  =     XmodForSequenceClassification(a_) if classification_head else XmodForMaskedLM(a_)
			model.eval()

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

			lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layernorm_embedding.weight
			lowerCamelCase						:Optional[int]						  =     xmod_sent_encoder.layernorm_embedding.bias

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

						# self attention
						lowerCamelCase						: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.''')

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

						# self-attention output
						lowerCamelCase						: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.''')
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn.out_proj.weight
						lowerCamelCase						:Union[str, Any]						  =     xmod_layer.self_attn.out_proj.bias
						lowerCamelCase						:str						  =     xmod_layer.self_attn_layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn_layer_norm.bias

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

						# output
						lowerCamelCase						: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.''')
						lowerCamelCase						:str						  =     xmod_layer.fca.weight
						lowerCamelCase						:int						  =     xmod_layer.fca.bias
						lowerCamelCase						:List[Any]						  =     xmod_layer.final_layer_norm.weight
						lowerCamelCase						:List[str]						  =     xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									lowerCamelCase						:List[str]						  =     xmod_layer.adapter_layer_norm.weight
									lowerCamelCase						: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():
									lowerCamelCase						:Optional[int]						  =     bert_output.adapter_modules[lang_code]
									lowerCamelCase						:Dict						  =     xmod_layer.adapter_modules[lang_code]
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.weight
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.bias
									lowerCamelCase						:Dict						  =     from_adapter.fca.weight
									lowerCamelCase						:Optional[Any]						  =     from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						lowerCamelCase						:Dict						  =     xmod_sent_encoder.layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layer_norm.bias

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

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

			lowerCamelCase						:Any						  =     model(a_)[0]
			if classification_head:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''](xmod.extract_features(a_))
			else:
						lowerCamelCase						:int						  =     xmod.model(a_						,			lang_id=[SAMPLE_LANGUAGE])[0]
			print(our_output.shape						,			their_output.shape)
			lowerCamelCase						:List[str]						  =     torch.max(torch.abs(our_output - their_output)).item()
			print(F"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
			lowerCamelCase						:str						  =     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
			)



