





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()
lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				'''Hello, World!'''
lowerCAmelCase_    =				'''en_XX'''






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						List[str]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  Path('''data_bin''' )
				snake_case_						:    List[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 )

				snake_case_						:    List[str]				=  xmod.model.encoder.sentence_encoder
				snake_case_						:    Tuple				=  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:
								snake_case_						:    int				=  xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

				snake_case_						:    Optional[int]				=  XmodForSequenceClassification(__a ) if classification_head else XmodForMaskedLM(__a )
				model.eval()

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

				snake_case_						:    str				=  xmod_sent_encoder.layernorm_embedding.weight
				snake_case_						:    List[Any]				=  xmod_sent_encoder.layernorm_embedding.bias

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

								# self attention
								snake_case_						:    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.''' )

								snake_case_						:    Optional[Any]				=  xmod_layer.self_attn.q_proj.weight
								snake_case_						:    Dict				=  xmod_layer.self_attn.q_proj.bias
								snake_case_						:    Any				=  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_						:    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_						:    Any				=  xmod_layer.self_attn.out_proj.weight
								snake_case_						:    Union[str, Any]				=  xmod_layer.self_attn.out_proj.bias
								snake_case_						:    List[Any]				=  xmod_layer.self_attn_layer_norm.weight
								snake_case_						:    Any				=  xmod_layer.self_attn_layer_norm.bias

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

								# output
								snake_case_						:    Optional[Any]				=  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_						:    Dict				=  xmod_layer.fca.weight
								snake_case_						:    Any				=  xmod_layer.fca.bias
								snake_case_						:    Union[str, Any]				=  xmod_layer.final_layer_norm.weight
								snake_case_						:    List[str]				=  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_						:    Optional[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_						:    Optional[int]				=  bert_output.adapter_modules[lang_code]
												snake_case_						:    str				=  xmod_layer.adapter_modules[lang_code]
												snake_case_						:    Optional[int]				=  from_adapter.fca.weight
												snake_case_						:    Dict				=  from_adapter.fca.bias
												snake_case_						:    Tuple				=  from_adapter.fca.weight
												snake_case_						:    List[Any]				=  from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								snake_case_						:    Optional[Any]				=  xmod_sent_encoder.layer_norm.weight
								snake_case_						:    Optional[int]				=  xmod_sent_encoder.layer_norm.bias

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

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

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







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__A									=				"."


if __name__ == "__main__":
     __A									=				os.path.join(REPO_PATH, "utils/documentation_tests.txt")
     __A									=				[]
     __A									=				[]
     with open(doctest_file_path) as fp:
          for line in fp:
               __A									=				line.strip()
               __A									=				os.path.join(REPO_PATH, line)
               if not (os.path.isfile(path) or os.path.isdir(path)):
                    non_existent_paths.append(line)
               all_paths.append(path)
     if len(non_existent_paths) > 0:
          __A									=				"\n".join(non_existent_paths)
          raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
     if all_paths != sorted(all_paths):
          raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")

