
'''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      A (       A_							:					Optional[int]    ,							A_							:					Optional[Any]    ,							A_							:					List[Any]       ):
						snake_case       :     List[Any] =    Path('''data_bin'''       )
						snake_case       :     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       :     Dict =    xmod.model.encoder.sentence_encoder
						snake_case       :     Union[str, 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=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       :     List[str] =    xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

						snake_case       :     Optional[Any] =    XmodForSequenceClassification(_A       ) if classification_head else XmodForMaskedLM(_A       )
						model.eval()

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

						snake_case       :     List[Any] =    xmod_sent_encoder.layernorm_embedding.weight
						snake_case       :     Any =    xmod_sent_encoder.layernorm_embedding.bias

						for i in range(config.num_hidden_layers       ):
												# Encoder: start of layer
												snake_case       :     Tuple =    model.roberta.encoder.layer[i]
												snake_case       :     Tuple =    xmod_sent_encoder.layers[i]

												# self attention
												snake_case       :     Tuple =    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       :     Tuple =    xmod_layer.self_attn.q_proj.weight
												snake_case       :     Tuple =    xmod_layer.self_attn.q_proj.bias
												snake_case       :     List[Any] =    xmod_layer.self_attn.k_proj.weight
												snake_case       :     List[Any] =    xmod_layer.self_attn.k_proj.bias
												snake_case       :     Dict =    xmod_layer.self_attn.v_proj.weight
												snake_case       :     Optional[int] =    xmod_layer.self_attn.v_proj.bias

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

												# output
												snake_case       :     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       :     Tuple =    xmod_layer.fca.weight
												snake_case       :     Any =    xmod_layer.fca.bias
												snake_case       :     Union[str, Any] =    xmod_layer.final_layer_norm.weight
												snake_case       :     Union[str, Any] =    xmod_layer.final_layer_norm.bias
												if bert_output.adapter_layer_norm is not None:
																		snake_case       :     Dict =    xmod_layer.adapter_layer_norm.weight
																		snake_case       :     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       :     Tuple =    bert_output.adapter_modules[lang_code]
																		snake_case       :     Optional[Any] =    xmod_layer.adapter_modules[lang_code]
																		snake_case       :     Tuple =    from_adapter.fca.weight
																		snake_case       :     Union[str, Any] =    from_adapter.fca.bias
																		snake_case       :     Any =    from_adapter.fca.weight
																		snake_case       :     Union[str, Any] =    from_adapter.fca.bias

												# end of layer

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

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

						# Let's check that we get the same results.
						snake_case       :     str =    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       :     List[str] =    xmod.model(_A    ,							lang_id=[SAMPLE_LANGUAGE]       )[0]
						print(our_output.shape    ,							their_output.shape       )
						snake_case       :     List[str] =    torch.max(torch.abs(our_output - their_output       )       ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
						snake_case       :     List[Any] =    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__":
						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'''





import os
import sys

UpperCAmelCase    					=   os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCAmelCase    					=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]

@add_start_docstrings(AutoConfig.__doc__       )
def      A (       *A_							:					Optional[Any]    ,							**A_							:					List[str]       ):
						return AutoConfig.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoTokenizer.__doc__       )
def      A (       *A_							:					Dict    ,							**A_							:					str       ):
						return AutoTokenizer.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModel.__doc__       )
def      A (       *A_							:					Union[str, Any]    ,							**A_							:					Optional[Any]       ):
						return AutoModel.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForCausalLM.__doc__       )
def      A (       *A_							:					str    ,							**A_							:					Optional[Any]       ):
						return AutoModelForCausalLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForMaskedLM.__doc__       )
def      A (       *A_							:					List[str]    ,							**A_							:					Optional[Any]       ):
						return AutoModelForMaskedLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForSequenceClassification.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					List[str]       ):
						return AutoModelForSequenceClassification.from_pretrained(*A_    ,							**A_       )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					Any       ):
						return AutoModelForQuestionAnswering.from_pretrained(*A_    ,							**A_       )

