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()
__lowercase									=   logging.get_logger(__name__)

__lowercase									=   """Hello, World!"""
__lowercase									=   """en_XX"""



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:str         =						Path('''data_bin'''      )
    __UpperCamelCase	:Optional[int]         =						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__      )

    __UpperCamelCase	:Dict         =						xmod.model.encoder.sentence_encoder
    __UpperCamelCase	:List[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:
        __UpperCamelCase	:int         =						xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

    __UpperCamelCase	:List[str]         =						XmodForSequenceClassification(UpperCamelCase__      ) if classification_head else XmodForMaskedLM(UpperCamelCase__      )
    model.eval()

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

    __UpperCamelCase	:Tuple         =						xmod_sent_encoder.layernorm_embedding.weight
    __UpperCamelCase	:int         =						xmod_sent_encoder.layernorm_embedding.bias

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

        # self attention
        __UpperCamelCase	: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.'''      )

        __UpperCamelCase	:List[str]         =						xmod_layer.self_attn.q_proj.weight
        __UpperCamelCase	:Any         =						xmod_layer.self_attn.q_proj.bias
        __UpperCamelCase	:List[str]         =						xmod_layer.self_attn.k_proj.weight
        __UpperCamelCase	:int         =						xmod_layer.self_attn.k_proj.bias
        __UpperCamelCase	:Optional[Any]         =						xmod_layer.self_attn.v_proj.weight
        __UpperCamelCase	:Union[str, Any]         =						xmod_layer.self_attn.v_proj.bias

        # self-attention output
        __UpperCamelCase	:str         =						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	:Dict         =						xmod_layer.self_attn.out_proj.weight
        __UpperCamelCase	:int         =						xmod_layer.self_attn.out_proj.bias
        __UpperCamelCase	:int         =						xmod_layer.self_attn_layer_norm.weight
        __UpperCamelCase	:Dict         =						xmod_layer.self_attn_layer_norm.bias

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

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

        # end of layer

    if xmod_sent_encoder.layer_norm is not None:
        __UpperCamelCase	:List[str]         =						xmod_sent_encoder.layer_norm.weight
        __UpperCamelCase	:Dict         =						xmod_sent_encoder.layer_norm.bias

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

    # Let's check that we get the same results.
    __UpperCamelCase	:List[Any]         =						xmod.encode(UpperCamelCase__      ).unsqueeze(0      )  # batch of size 1
    model.roberta.set_default_language(UpperCamelCase__      )

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












from ..utils import DummyObject, requires_backends





class 				a			(      metaclass=__lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     ['''torch''', '''scipy''']






				def __init__(  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(self			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)



