





"""simple docstring"""
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()
__UpperCamelCase			:		Dict		    =   logging.get_logger(__name__)

__UpperCamelCase			:		Union[str, Any]		    =   '''Hello world! cécé herlolip'''

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  str   ,			_UpperCAmelCase :  str   ,			_UpperCAmelCase :  bool       ):
 lowerCAmelCase							,lowerCAmelCase  							=      os.path.split(_UpperCAmelCase       )
 lowerCAmelCase  							=      DataaVecTextModel.from_pretrained(
     _UpperCAmelCase   ,			checkpoint_file=_UpperCAmelCase       )
 dataavec.eval()  # disable dropout
 lowerCAmelCase  							=      dataavec.models[0]
 lowerCAmelCase  							=      dataavec_model.encoder.sentence_encoder
 lowerCAmelCase  							=      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:
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'   ,			_UpperCAmelCase       )

 lowerCAmelCase  							=      DataaVecTextForSequenceClassification(_UpperCAmelCase       ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase       )
 model.eval()

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

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

  # self attention
  lowerCAmelCase  							=      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)       )}'
  )

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

  # self-attention output
  lowerCAmelCase  							=      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}'
  lowerCAmelCase  							=      dataavec_layer.self_attn.out_proj.weight
  lowerCAmelCase  							=      dataavec_layer.self_attn.out_proj.bias
  lowerCAmelCase  							=      dataavec_layer.self_attn_layer_norm.weight
  lowerCAmelCase  							=      dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  lowerCAmelCase  							=      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}'
  lowerCAmelCase  							=      dataavec_layer.fca.weight
  lowerCAmelCase  							=      dataavec_layer.fca.bias

  # output
  lowerCAmelCase  							=      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}'
  lowerCAmelCase  							=      dataavec_layer.fca.weight
  lowerCAmelCase  							=      dataavec_layer.fca.bias
  lowerCAmelCase  							=      dataavec_layer.final_layer_norm.weight
  lowerCAmelCase  							=      dataavec_layer.final_layer_norm.bias
  # end of layer

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

 # Let's check that we get the same results.
 lowerCAmelCase  							=      dataavec.encode(_UpperCAmelCase       ).unsqueeze(0       )  # batch of size 1

 lowerCAmelCase  							=      model(_UpperCAmelCase       )[0]
 if classification_head:
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'](dataavec.extract_features(_UpperCAmelCase       )       )
 else:
  lowerCAmelCase  							=      dataavec_model(_UpperCAmelCase       )[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'       )

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


if __name__ == "__main__":
       __UpperCamelCase			:		int		    =   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.'''
       )
       __UpperCamelCase			:		List[Any]		    =   parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCamelCase__   = {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__   = ['''ErnieMTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__   = [
       '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ErnieMForMultipleChoice''',
       '''ErnieMForQuestionAnswering''',
       '''ErnieMForSequenceClassification''',
       '''ErnieMForTokenClassification''',
       '''ErnieMModel''',
       '''ErnieMPreTrainedModel''',
       '''ErnieMForInformationExtraction''',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__   = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




