
"""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()
a					:	str							=		logging.get_logger(__name__)

a					:	Tuple							=		'''Hello, World!'''
a					:	Optional[Any]							=		'''en_XX'''


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Optional[Any]:





    """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=5_1_4					,      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__":
 a					:	List[str]							=		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.'''
 )
 a					:	List[Any]							=		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


def  _UpperCamelCase	(						_A      )    ->  None:





    """simple docstring"""

    create_state_space_tree(_A					,      []					,      0					,      [0 for i in range(len(_A      )      )]      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      )    ->  None:





    """simple docstring"""

    if index == len(_A      ):
        print(_A      )
        return

    for i in range(len(_A      )      ):
        if not index_used[i]:
            current_sequence.append(sequence[i]      )
            _UpperCAmelCase          =   True
            create_state_space_tree(_A					,      _A					,      index + 1					,      _A      )
            current_sequence.pop()
            _UpperCAmelCase          =   False
a					:	list[int | str]							=		[3, 1, 2, 4]
generate_all_permutations(sequence)

a					:	list[int | str]							=		["A", "B", "C"]







generate_all_permutations(sequence_a)