
"""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()
SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    '''Hello, World!'''
SCREAMING_SNAKE_CASE_            =    '''en_XX'''




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       str			,					lowerCAmelCase					:       bool			)			->   Optional[Any]:






    """simple docstring"""




    UpperCAmelCase          =       Path('data_bin'			)
    UpperCAmelCase          =       FairseqXmodModel.from_pretrained(
        model_name_or_path=str(Path(lowerCAmelCase			).parent			)			,					checkpoint_file=Path(lowerCAmelCase			).name			,					_name='xmod_base'			,					arch='xmod_base'			,					task='multilingual_masked_lm'			,					data_name_or_path=str(lowerCAmelCase			)			,					bpe='sentencepiece'			,					sentencepiece_model=str(Path(lowerCAmelCase			).parent / 'sentencepiece.bpe.model'			)			,					src_dict=str(data_dir / 'dict.txt'			)			,					)
    xmod.eval()  # disable dropout
    print(lowerCAmelCase			)

    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:'			,					lowerCAmelCase			)

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

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    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.'''
   )
   SCREAMING_SNAKE_CASE_            =    parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )





"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



