





"""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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						bool			):







      '''simple docstring'''




      lowerCAmelCase  						=       Path("""data_bin"""			)
      lowerCAmelCase  						=       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			)

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

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

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

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

      lowerCAmelCase  						=       xmod_sent_encoder.layernorm_embedding.weight
      lowerCAmelCase  						=       xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            lowerCAmelCase  						=       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."""			)

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

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

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

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

            # end of layer

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

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

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

      lowerCAmelCase  						=       model(SCREAMING_SNAKE_CASE			)[0]
      if classification_head:
            lowerCAmelCase  						=       xmod.model.classification_heads["""mnli"""](xmod.extract_features(SCREAMING_SNAKE_CASE			)			)
      else:
            lowerCAmelCase  						=       xmod.model(SCREAMING_SNAKE_CASE							,						lang_id=[SAMPLE_LANGUAGE]			)[0]
      print(our_output.shape							,						their_output.shape			)
      lowerCAmelCase  						=       torch.max(torch.abs(our_output - their_output			)			).item()
      print(F'max_absolute_diff = {max_absolute_diff}'			)  # ~ 1e-7
      lowerCAmelCase  						=       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
       )













"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'longformer'





      def __init__(       self      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 1      ,						lowercase = 0      ,						lowercase = 2      ,						lowercase = 30_522      ,						lowercase = 768      ,						lowercase = 12      ,						lowercase = 12      ,						lowercase = 3_072      ,						lowercase = "gelu"      ,						lowercase = 0.1      ,						lowercase = 0.1      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 0.02      ,						lowercase = 1e-12      ,						lowercase = False      ,						**lowercase      ,						)		->     Optional[int]:
            super().__init__(pad_token_id=lowercase      ,						**lowercase )

            lowerCAmelCase  						=       attention_window
            lowerCAmelCase  						=       sep_token_id
            lowerCAmelCase  						=       bos_token_id
            lowerCAmelCase  						=       eos_token_id
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       type_vocab_size
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       layer_norm_eps
            lowerCAmelCase  						=       onnx_export







class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase = "default"      ,						lowercase = None )		->     Tuple:
            super().__init__(lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       True





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                  lowerCAmelCase  						=       {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""global_attention_mask""", dynamic_axis),
                ] )





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            lowerCAmelCase  						=       super().outputs
            if self.task == "default":
                  lowerCAmelCase  						=       {0: """batch"""}
            return outputs





      @property
      def     _snake_case			(       self )		->     float:
            return 1e-4





      @property
      def     _snake_case			(       self )		->     int:
            # needs to be >= 14 to support tril operator
            return max(super().default_onnx_opset      ,						14 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = -1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       super().generate_dummy_inputs(
                preprocessor=lowercase      ,						batch_size=lowercase      ,						seq_length=lowercase      ,						is_pair=lowercase      ,						framework=lowercase )
            import torch

            # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
            # makes the export fail randomly
            lowerCAmelCase  						=       torch.zeros_like(inputs["""input_ids"""] )
            # make every second token global
            lowerCAmelCase  						=       1

            return inputs






