





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       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Dict:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE		=						xmod.model.encoder.sentence_encoder
      SCREAMING_SNAKE_CASE		=						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_14			,						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:
            SCREAMING_SNAKE_CASE		=						xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

      SCREAMING_SNAKE_CASE		=						XmodForSequenceClassification(_SCREAMING_SNAKE_CASE     ) if classification_head else XmodForMaskedLM(_SCREAMING_SNAKE_CASE     )
      model.eval()

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

      SCREAMING_SNAKE_CASE		=						xmod_sent_encoder.layernorm_embedding.weight
      SCREAMING_SNAKE_CASE		=						xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            SCREAMING_SNAKE_CASE		=						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."""     )

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

            # self-attention output
            SCREAMING_SNAKE_CASE		=						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."""     )
            SCREAMING_SNAKE_CASE		=						xmod_layer.self_attn.out_proj.weight
            SCREAMING_SNAKE_CASE		=						xmod_layer.self_attn.out_proj.bias
            SCREAMING_SNAKE_CASE		=						xmod_layer.self_attn_layer_norm.weight
            SCREAMING_SNAKE_CASE		=						xmod_layer.self_attn_layer_norm.bias

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

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

            # end of layer

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

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

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

      SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE     )[0]
      if classification_head:
            SCREAMING_SNAKE_CASE		=						xmod.model.classification_heads["""mnli"""](xmod.extract_features(_SCREAMING_SNAKE_CASE     )     )
      else:
            SCREAMING_SNAKE_CASE		=						xmod.model(_SCREAMING_SNAKE_CASE			,						lang_id=[SAMPLE_LANGUAGE]     )[0]
      print(our_output.shape			,						their_output.shape     )
      SCREAMING_SNAKE_CASE		=						torch.max(torch.abs(our_output - their_output     )     ).item()
      print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
      SCREAMING_SNAKE_CASE		=						torch.allclose(_SCREAMING_SNAKE_CASE			,						_SCREAMING_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(_SCREAMING_SNAKE_CASE     ).mkdir(parents=_SCREAMING_SNAKE_CASE			,						exist_ok=_SCREAMING_SNAKE_CASE     )
      print(F"""Saving model to {pytorch_dump_folder_path}"""     )
      model.save_pretrained(_SCREAMING_SNAKE_CASE     )


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
      )









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 10_00     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						0

      for divide_by_number in range(_SCREAMING_SNAKE_CASE			,						digit + 1     ):
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						numerator
            for _ in range(1			,						digit + 1     ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )
                              SCREAMING_SNAKE_CASE		=						divide_by_number
                  else:
                        has_been_divided.append(_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()


