




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()
snake_case_ : List[Any]    		=       logging.get_logger(__name__)

snake_case_ : Any    		=       "Hello, World!"
snake_case_ : Tuple    		=       "en_XX"


def    A						(__A   :    str   ,				__A   :    str   ,				__A   :    bool       )   ->      Dict:







     """simple docstring"""


     UpperCAmelCase_          =							Path('''data_bin'''       )
     UpperCAmelCase_          =							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       )

     UpperCAmelCase_          =							xmod.model.encoder.sentence_encoder
     UpperCAmelCase_          =							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:
          UpperCAmelCase_          =							xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

     UpperCAmelCase_          =							XmodForSequenceClassification(__A       ) if classification_head else XmodForMaskedLM(__A       )
     model.eval()

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

     UpperCAmelCase_          =							xmod_sent_encoder.layernorm_embedding.weight
     UpperCAmelCase_          =							xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          UpperCAmelCase_          =							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.'''       )

          UpperCAmelCase_          =							xmod_layer.self_attn.q_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.q_proj.bias
          UpperCAmelCase_          =							xmod_layer.self_attn.k_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.k_proj.bias
          UpperCAmelCase_          =							xmod_layer.self_attn.v_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase_          =							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.'''       )
          UpperCAmelCase_          =							xmod_layer.self_attn.out_proj.weight
          UpperCAmelCase_          =							xmod_layer.self_attn.out_proj.bias
          UpperCAmelCase_          =							xmod_layer.self_attn_layer_norm.weight
          UpperCAmelCase_          =							xmod_layer.self_attn_layer_norm.bias

          # intermediate
          UpperCAmelCase_          =							layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('''Dimensions of intermediate weights do not match.'''       )
          UpperCAmelCase_          =							xmod_layer.fca.weight
          UpperCAmelCase_          =							xmod_layer.fca.bias

          # output
          UpperCAmelCase_          =							layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('''Dimensions of feed-forward weights do not match.'''       )
          UpperCAmelCase_          =							xmod_layer.fca.weight
          UpperCAmelCase_          =							xmod_layer.fca.bias
          UpperCAmelCase_          =							xmod_layer.final_layer_norm.weight
          UpperCAmelCase_          =							xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               UpperCAmelCase_          =							xmod_layer.adapter_layer_norm.weight
               UpperCAmelCase_          =							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():
               UpperCAmelCase_          =							bert_output.adapter_modules[lang_code]
               UpperCAmelCase_          =							xmod_layer.adapter_modules[lang_code]
               UpperCAmelCase_          =							from_adapter.fca.weight
               UpperCAmelCase_          =							from_adapter.fca.bias
               UpperCAmelCase_          =							from_adapter.fca.weight
               UpperCAmelCase_          =							from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          UpperCAmelCase_          =							xmod_sent_encoder.layer_norm.weight
          UpperCAmelCase_          =							xmod_sent_encoder.layer_norm.bias

     if classification_head:
          UpperCAmelCase_          =							xmod.model.classification_heads['''mnli'''].dense.weight
          UpperCAmelCase_          =							xmod.model.classification_heads['''mnli'''].dense.bias
          UpperCAmelCase_          =							xmod.model.classification_heads['''mnli'''].out_proj.weight
          UpperCAmelCase_          =							xmod.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase_          =							xmod.model.encoder.lm_head.dense.weight
          UpperCAmelCase_          =							xmod.model.encoder.lm_head.dense.bias
          UpperCAmelCase_          =							xmod.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase_          =							xmod.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase_          =							xmod.model.encoder.lm_head.weight
          UpperCAmelCase_          =							xmod.model.encoder.lm_head.bias

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

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













'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    int      	=			{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='gpt_bigcode'
				__a            =['past_key_values']
				__a            ={
				    'hidden_size': 'n_embd',
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(  self     :					Optional[Any]					,				__a     :					Tuple=5_02_57					,				__a     :					str=10_24					,				__a     :					Dict=7_68					,				__a     :					Tuple=12					,				__a     :					str=12					,				__a     :					Optional[int]=None					,				__a     :					Dict="gelu_pytorch_tanh"					,				__a     :					Tuple=0.1					,				__a     :					Tuple=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Tuple=1e-5					,				__a     :					str=0.02					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[int]=5_02_56					,				__a     :					Optional[int]=5_02_56					,				__a     :					Union[str, Any]=True					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				**__a     :					List[Any]					,				):
								_a								= vocab_size
								_a								= n_positions
								_a								= n_embd
								_a								= n_layer
								_a								= n_head
								_a								= n_inner
								_a								= activation_function
								_a								= resid_pdrop
								_a								= embd_pdrop
								_a								= attn_pdrop
								_a								= layer_norm_epsilon
								_a								= initializer_range
								_a								= scale_attn_weights
								_a								= use_cache
								_a								= attention_softmax_in_fpaa
								_a								= scale_attention_softmax_in_fpaa
								_a								= multi_query

								_a								= bos_token_id
								_a								= eos_token_id

								super().__init__(bos_token_id=__a					,				eos_token_id=__a					,				**__a					)
