



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			int			,_UpperCamelCase      :			List[str]				):
   # Initialise PyTorch model
   __lowerCamelCase    				=     AlbertConfig.from_json_file(_UpperCamelCase				)
   print(F"""Building PyTorch model from configuration: {config}"""				)
   __lowerCamelCase    				=     AlbertForPreTraining(_UpperCamelCase				)

   # Load weights from tf checkpoint
   load_tf_weights_in_albert(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase				)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
   torch.save(model.state_dict()			,_UpperCamelCase				)


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--albert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained ALBERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  a_								=       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)











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_								=       logging.get_logger(__name__)

a_								=       """Hello, World!"""
a_								=       """en_XX"""





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			str			,_UpperCamelCase      :			bool				):
   __lowerCamelCase    				=     Path('''data_bin'''				)
   __lowerCamelCase    				=     FairseqXmodModel.from_pretrained(
       model_name_or_path=str(Path(_UpperCamelCase				).parent				)			,checkpoint_file=Path(_UpperCamelCase				).name			,_name='''xmod_base'''			,arch='''xmod_base'''			,task='''multilingual_masked_lm'''			,data_name_or_path=str(_UpperCamelCase				)			,bpe='''sentencepiece'''			,sentencepiece_model=str(Path(_UpperCamelCase				).parent / '''sentencepiece.bpe.model'''				)			,src_dict=str(data_dir / '''dict.txt'''				)			,)
   xmod.eval()  # disable dropout
   print(_UpperCamelCase				)

   __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:'''			,_UpperCamelCase				)

   __lowerCamelCase    				=     XmodForSequenceClassification(_UpperCamelCase				) if classification_head else XmodForMaskedLM(_UpperCamelCase				)
   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(_UpperCamelCase				).unsqueeze(0				)  # batch of size 1
   model.roberta.set_default_language(_UpperCamelCase				)

   __lowerCamelCase    				=     model(_UpperCamelCase				)[0]
   if classification_head:
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''](xmod.extract_features(_UpperCamelCase				)				)
   else:
      __lowerCamelCase    				=     xmod.model(_UpperCamelCase			,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(_UpperCamelCase			,_UpperCamelCase			,atol=1e-3				)
   print('''Do both models output the same tensors?'''			,'''🔥''' if success else '''💩'''				)
   if not success:
      raise Exception('''Something went wRoNg'''				)

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


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






