


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
  raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
a				:							Optional[Any]					=			logging.get_logger(__name__)

a				:							List[Any]					=			'Hello world! cécé herlolip'






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )		->List[Any]:


 '''simple docstring'''
 snake_case  ,					snake_case   : Optional[int]	  =      os.path.split(__lowerCamelCase       )
 snake_case   : List[str]	  =      DataaVecTextModel.from_pretrained(
     __lowerCamelCase ,     checkpoint_file=__lowerCamelCase       )
 dataavec.eval()  # disable dropout
 snake_case   : Optional[int]	  =      dataavec.models[0]
 snake_case   : Dict	  =      dataavec_model.encoder.sentence_encoder
 snake_case   : Union[str, Any]	  =      DataaVecTextConfig(
     vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings ,     hidden_size=dataavec_model.args.encoder_embed_dim ,     num_hidden_layers=dataavec_model.args.encoder_layers ,     num_attention_heads=dataavec_model.args.encoder_attention_heads ,     intermediate_size=dataavec_model.args.encoder_ffn_embed_dim ,     max_position_embeddings=514 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     )
 if classification_head:
  snake_case   : Any	  =      dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
 print('''Our BERT config:''' ,     __lowerCamelCase       )

 snake_case   : List[Any]	  =      DataaVecTextForSequenceClassification(__lowerCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__lowerCamelCase       )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 snake_case   : Any	  =      dataavec_sent_encoder.embed_tokens.weight
 snake_case   : Tuple	  =      dataavec_sent_encoder.embed_positions.weight
 snake_case   : Tuple	  =      torch.zeros_like(
     model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
 snake_case   : Any	  =      dataavec_sent_encoder.layernorm_embedding.weight
 snake_case   : List[Any]	  =      dataavec_sent_encoder.layernorm_embedding.bias

 for i in range(config.num_hidden_layers       ):
  # Encoder: start of layer
  snake_case   : BertLayer	  =      model.dataavec_text.encoder.layer[i]
  snake_case   : TransformerSentenceEncoderLayer	  =      dataavec_sent_encoder.layers[i]

  # self attention
  snake_case   : BertSelfAttention	  =      layer.attention.self
  assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
      (config.hidden_size, config.hidden_size)       ), (
      "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
      f''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
  )
  assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
      (config.hidden_size, config.hidden_size)       ), (
      "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
      f''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
  )
  assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
      (config.hidden_size, config.hidden_size)       ), (
      "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
      f''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
  )

  snake_case   : Optional[Any]	  =      dataavec_layer.self_attn.q_proj.weight
  snake_case   : Union[str, Any]	  =      dataavec_layer.self_attn.q_proj.bias
  snake_case   : str	  =      dataavec_layer.self_attn.k_proj.weight
  snake_case   : Any	  =      dataavec_layer.self_attn.k_proj.bias
  snake_case   : str	  =      dataavec_layer.self_attn.v_proj.weight
  snake_case   : Dict	  =      dataavec_layer.self_attn.v_proj.bias

  # self-attention output
  snake_case   : BertSelfOutput	  =      layer.attention.output
  assert (
      self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
  ), f'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
  snake_case   : List[str]	  =      dataavec_layer.self_attn.out_proj.weight
  snake_case   : int	  =      dataavec_layer.self_attn.out_proj.bias
  snake_case   : Optional[Any]	  =      dataavec_layer.self_attn_layer_norm.weight
  snake_case   : Union[str, Any]	  =      dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  snake_case   : BertIntermediate	  =      layer.intermediate
  assert (
      intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
  ), f'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
  snake_case   : int	  =      dataavec_layer.fca.weight
  snake_case   : List[Any]	  =      dataavec_layer.fca.bias

  # output
  snake_case   : BertOutput	  =      layer.output
  assert (
      bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
  ), f'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
  snake_case   : Tuple	  =      dataavec_layer.fca.weight
  snake_case   : Union[str, Any]	  =      dataavec_layer.fca.bias
  snake_case   : Union[str, Any]	  =      dataavec_layer.final_layer_norm.weight
  snake_case   : Tuple	  =      dataavec_layer.final_layer_norm.bias
  # end of layer

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

 # Let's check that we get the same results.
 snake_case   : torch.Tensor	  =      dataavec.encode(__lowerCamelCase       ).unsqueeze(0       )  # batch of size 1

 snake_case   : Any	  =      model(__lowerCamelCase       )[0]
 if classification_head:
  snake_case   : Union[str, Any]	  =      dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__lowerCamelCase       )       )
 else:
  snake_case   : Optional[int]	  =      dataavec_model(__lowerCamelCase       )[0]
 print(our_output.shape ,     their_output.shape       )
 snake_case   : Union[str, Any]	  =      torch.max(torch.abs(our_output - their_output       )       ).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
 snake_case   : Optional[int]	  =      torch.allclose(__lowerCamelCase ,     __lowerCamelCase ,     atol=1E-3       )
 print('''Do both models output the same tensors?''' ,     '''🔥''' if success else '''💩'''       )
 if not success:
  raise Exception('''Something went wRoNg'''       )

 pathlib.Path(__lowerCamelCase       ).mkdir(parents=__lowerCamelCase ,     exist_ok=__lowerCamelCase       )
 print(f'''Saving model to {pytorch_dump_folder_path}'''       )
 model.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
  a				:							Dict					=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--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				:							int					=			parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )










import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : List[str]	  =      ArgumentParser(
     description=(
         '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
     )       )

 # Optional arguments for the launch helper
 parser.add_argument('''--num_cores''' ,     type=__lowerCamelCase ,     default=1 ,     help='''Number of TPU cores to use (1 or 8).'''       )

 # positional
 parser.add_argument(
     '''training_script''' ,     type=__lowerCamelCase ,     help=(
         '''The full path to the single TPU training '''
         '''program/script to be launched in parallel, '''
         '''followed by all the arguments for the '''
         '''training script'''
     ) ,     )

 # rest from the training program
 parser.add_argument('''training_script_args''' ,     nargs=__lowerCamelCase       )

 return parser.parse_args()






def    _UpperCAmelCase	(   )		->Optional[int]:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      parse_args()

 # Import training_script as a module.
 snake_case   : str	  =      Path(args.training_script       )
 sys.path.append(str(script_fpath.parent.resolve()       )       )
 snake_case   : str	  =      script_fpath.stem
 snake_case   : Optional[int]	  =      importlib.import_module(__lowerCamelCase       )

 # Patch sys.argv
 snake_case   : int	  =      [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores       )]

 xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores       )


if __name__ == "__main__":
  main()






