





"""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__  :     List[str]   			=logging.get_logger(__name__)

UpperCAmelCase__  :     Tuple   			='''Hello, World!'''
UpperCAmelCase__  :     Any   			='''en_XX'''





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				lowerCamelCase			=Path("""data_bin"""			)
				lowerCamelCase			=FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(_UpperCAmelCase			).parent			)     , checkpoint_file=Path(_UpperCAmelCase			).name     , _name="""xmod_base"""     , arch="""xmod_base"""     , task="""multilingual_masked_lm"""     , data_name_or_path=str(_UpperCAmelCase			)     , bpe="""sentencepiece"""     , sentencepiece_model=str(Path(_UpperCAmelCase			).parent / """sentencepiece.bpe.model"""			)     , src_dict=str(data_dir / """dict.txt"""			)     , )
				xmod.eval()  # disable dropout
				print(_UpperCAmelCase			)

				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=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			=xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

				lowerCamelCase			=XmodForSequenceClassification(_UpperCAmelCase			) if classification_head else XmodForMaskedLM(_UpperCAmelCase			)
				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(_UpperCAmelCase			).unsqueeze(0			)  # batch of size 1
				model.roberta.set_default_language(_UpperCAmelCase			)

				lowerCamelCase			=model(_UpperCAmelCase			)[0]
				if classification_head:
								lowerCamelCase			=xmod.model.classification_heads["""mnli"""](xmod.extract_features(_UpperCAmelCase			)			)
				else:
								lowerCamelCase			=xmod.model(_UpperCAmelCase     , 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(_UpperCAmelCase     , _UpperCAmelCase     , atol=1e-3			)
				print("""Do both models output the same tensors?"""     , """🔥""" if success else """💩"""			)
				if not success:
								raise Exception("""Something went wRoNg"""			)

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


if __name__ == "__main__":
							UpperCAmelCase__  :     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.'''
							)
							UpperCAmelCase__  :     Union[str, 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 typing import Dict

from ...configuration_utils import PretrainedConfig


UpperCAmelCase__  :     Optional[Any]   			={
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}

class  __A       (  a      ):
				__A								=			"""ernie_m"""
				__A								=			{"dropout": "classifier_dropout", "num_classes": "num_labels"}


				def __init__(						self				,	UpperCAmelCase_ = 250002				,	UpperCAmelCase_ = 768				,	UpperCAmelCase_ = 12				,	UpperCAmelCase_ = 12				,	UpperCAmelCase_ = 3072				,	UpperCAmelCase_ = "gelu"				,	UpperCAmelCase_ = 0.1				,	UpperCAmelCase_ = 0.1				,	UpperCAmelCase_ = 514				,	UpperCAmelCase_ = 0.0_2				,	UpperCAmelCase_ = 1				,	UpperCAmelCase_ = 1E-05				,	UpperCAmelCase_=None				,	UpperCAmelCase_=False				,	UpperCAmelCase_=0.0				,	**UpperCAmelCase_				,	):
								super().__init__(pad_token_id=UpperCAmelCase_				,	**UpperCAmelCase_							)
								lowerCamelCase			=vocab_size
								lowerCamelCase			=hidden_size
								lowerCamelCase			=num_hidden_layers
								lowerCamelCase			=num_attention_heads
								lowerCamelCase			=intermediate_size
								lowerCamelCase			=hidden_act
								lowerCamelCase			=hidden_dropout_prob
								lowerCamelCase			=attention_probs_dropout_prob
								lowerCamelCase			=max_position_embeddings
								lowerCamelCase			=initializer_range
								lowerCamelCase			=layer_norm_eps
								lowerCamelCase			=classifier_dropout
								lowerCamelCase			=is_decoder
								lowerCamelCase			=act_dropout





