



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()
__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			'''Hello, World!'''
__lowerCamelCase         =			'''en_XX'''







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[Any]				      =		Path("data_bin"    )
      UpperCAmelCase_  :	Union[str, Any]				      =		FairseqXmodModel.from_pretrained(
          model_name_or_path=str(Path(__snake_case    ).parent    )					,  checkpoint_file=Path(__snake_case    ).name					,  _name="xmod_base"					,  arch="xmod_base"					,  task="multilingual_masked_lm"					,  data_name_or_path=str(__snake_case    )					,  bpe="sentencepiece"					,  sentencepiece_model=str(Path(__snake_case    ).parent / "sentencepiece.bpe.model"    )					,  src_dict=str(data_dir / "dict.txt"    )					,  )
      xmod.eval()  # disable dropout
      print(__snake_case    )

      UpperCAmelCase_  :	Tuple				      =		xmod.model.encoder.sentence_encoder
      UpperCAmelCase_  :	Tuple				      =		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_  :	Optional[int]				      =		xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

      print("Our X-MOD config:"					,  __snake_case    )

      UpperCAmelCase_  :	Optional[int]				      =		XmodForSequenceClassification(__snake_case    ) if classification_head else XmodForMaskedLM(__snake_case    )
      model.eval()

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

      UpperCAmelCase_  :	Dict				      =		xmod_sent_encoder.layernorm_embedding.weight
      UpperCAmelCase_  :	Optional[Any]				      =		xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            UpperCAmelCase_  :	List[str]				      =		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_  :	Tuple				      =		xmod_layer.self_attn.q_proj.weight
            UpperCAmelCase_  :	str				      =		xmod_layer.self_attn.q_proj.bias
            UpperCAmelCase_  :	List[str]				      =		xmod_layer.self_attn.k_proj.weight
            UpperCAmelCase_  :	List[Any]				      =		xmod_layer.self_attn.k_proj.bias
            UpperCAmelCase_  :	Union[str, Any]				      =		xmod_layer.self_attn.v_proj.weight
            UpperCAmelCase_  :	int				      =		xmod_layer.self_attn.v_proj.bias

            # self-attention output
            UpperCAmelCase_  :	Tuple				      =		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_  :	int				      =		xmod_layer.self_attn.out_proj.weight
            UpperCAmelCase_  :	Union[str, Any]				      =		xmod_layer.self_attn.out_proj.bias
            UpperCAmelCase_  :	List[str]				      =		xmod_layer.self_attn_layer_norm.weight
            UpperCAmelCase_  :	Dict				      =		xmod_layer.self_attn_layer_norm.bias

            # intermediate
            UpperCAmelCase_  :	Union[str, Any]				      =		layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("Dimensions of intermediate weights do not match."    )
            UpperCAmelCase_  :	Dict				      =		xmod_layer.fca.weight
            UpperCAmelCase_  :	Tuple				      =		xmod_layer.fca.bias

            # output
            UpperCAmelCase_  :	List[Any]				      =		layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("Dimensions of feed-forward weights do not match."    )
            UpperCAmelCase_  :	int				      =		xmod_layer.fca.weight
            UpperCAmelCase_  :	Dict				      =		xmod_layer.fca.bias
            UpperCAmelCase_  :	List[str]				      =		xmod_layer.final_layer_norm.weight
            UpperCAmelCase_  :	List[Any]				      =		xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  UpperCAmelCase_  :	Optional[int]				      =		xmod_layer.adapter_layer_norm.weight
                  UpperCAmelCase_  :	Optional[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_  :	Any				      =		bert_output.adapter_modules[lang_code]
                  UpperCAmelCase_  :	int				      =		xmod_layer.adapter_modules[lang_code]
                  UpperCAmelCase_  :	Optional[Any]				      =		from_adapter.fca.weight
                  UpperCAmelCase_  :	Dict				      =		from_adapter.fca.bias
                  UpperCAmelCase_  :	str				      =		from_adapter.fca.weight
                  UpperCAmelCase_  :	Union[str, Any]				      =		from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            UpperCAmelCase_  :	Optional[int]				      =		xmod_sent_encoder.layer_norm.weight
            UpperCAmelCase_  :	Tuple				      =		xmod_sent_encoder.layer_norm.bias

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

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

      UpperCAmelCase_  :	int				      =		model(__snake_case    )[0]
      if classification_head:
            UpperCAmelCase_  :	Any				      =		xmod.model.classification_heads["mnli"](xmod.extract_features(__snake_case    )    )
      else:
            UpperCAmelCase_  :	int				      =		xmod.model(__snake_case					,  lang_id=[SAMPLE_LANGUAGE]    )[0]
      print(our_output.shape					,  their_output.shape    )
      UpperCAmelCase_  :	List[str]				      =		torch.max(torch.abs(our_output - their_output    )    ).item()
      print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
      UpperCAmelCase_  :	List[str]				      =		torch.allclose(__snake_case					,  __snake_case					,  atol=1E-3    )
      print("Do both models output the same tensors?"					,  "🔥" if success else "💩"    )
      if not success:
            raise Exception("Something went wRoNg"    )

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


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





import operator







def    _snake_case (       __snake_case					,  __snake_case = False					,  __snake_case = None    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		operator.lt if reverse else operator.gt
      UpperCAmelCase_  :	int				      =		solution or []

      if not arr:
            return solution

      UpperCAmelCase_  :	Union[str, Any]				      =		[arr.pop(0    )]
      for i, item in enumerate(__snake_case    ):
            if _operator(__snake_case					,  sublist[-1]    ):
                  sublist.append(__snake_case    )
                  arr.pop(__snake_case    )

    #  merging sublist into solution list
      if not solution:
            solution.extend(__snake_case    )
      else:
            while sublist:
                  UpperCAmelCase_  :	Optional[int]				      =		sublist.pop(0    )
                  for i, xx in enumerate(__snake_case    ):
                        if not _operator(__snake_case					,  __snake_case    ):
                              solution.insert(__snake_case					,  __snake_case    )
                              break
                  else:
                        solution.append(__snake_case    )

      strand_sort(__snake_case					,  __snake_case					,  __snake_case    )
      return solution


if __name__ == "__main__":
   assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
   assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
