


"""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()
__lowerCamelCase   :List[Any]						     =		logging.get_logger(__name__)

__lowerCamelCase   :Optional[int]						     =		'Hello, World!'
__lowerCamelCase   :Tuple						     =		'en_XX'




def 	snake_case		(  UpperCamelCase__							:    int      ,					UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    Union[str, Any]     ) ->   int:
      lowerCamelCase       :       Dict   =						Path("""data_bin"""     )
      lowerCamelCase       :       Dict   =						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     )

      lowerCamelCase       :       int   =						xmod.model.encoder.sentence_encoder
      lowerCamelCase       :       List[Any]   =						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:
            lowerCamelCase       :       List[Any]   =						xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

      lowerCamelCase       :       Any   =						XmodForSequenceClassification(__snake_case     ) if classification_head else XmodForMaskedLM(__snake_case     )
      model.eval()

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

      lowerCamelCase       :       List[str]   =						xmod_sent_encoder.layernorm_embedding.weight
      lowerCamelCase       :       Tuple   =						xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            lowerCamelCase       :       Optional[Any]   =						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       :       Optional[int]   =						xmod_layer.self_attn.q_proj.weight
            lowerCamelCase       :       int   =						xmod_layer.self_attn.q_proj.bias
            lowerCamelCase       :       Union[str, Any]   =						xmod_layer.self_attn.k_proj.weight
            lowerCamelCase       :       int   =						xmod_layer.self_attn.k_proj.bias
            lowerCamelCase       :       Union[str, Any]   =						xmod_layer.self_attn.v_proj.weight
            lowerCamelCase       :       str   =						xmod_layer.self_attn.v_proj.bias

            # self-attention output
            lowerCamelCase       :       str   =						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       :       List[Any]   =						xmod_layer.self_attn.out_proj.weight
            lowerCamelCase       :       Any   =						xmod_layer.self_attn.out_proj.bias
            lowerCamelCase       :       Any   =						xmod_layer.self_attn_layer_norm.weight
            lowerCamelCase       :       int   =						xmod_layer.self_attn_layer_norm.bias

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

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

            # end of layer

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

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

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

      lowerCamelCase       :       Dict   =						model(__snake_case     )[0]
      if classification_head:
            lowerCamelCase       :       Tuple   =						xmod.model.classification_heads["mnli"](xmod.extract_features(__snake_case     )     )
      else:
            lowerCamelCase       :       Union[str, Any]   =						xmod.model(__snake_case      ,					lang_id=[SAMPLE_LANGUAGE]     )[0]
      print(our_output.shape      ,					their_output.shape     )
      lowerCamelCase       :       List[str]   =						torch.max(torch.abs(our_output - their_output     )     ).item()
      print(F'max_absolute_diff = {max_absolute_diff}'     )  # ~ 1e-7
      lowerCamelCase       :       int   =						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   :Optional[int]						     =		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   :Union[str, Any]						     =		parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )









"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask