


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   				=      logging.get_logger(__name__)

__a   				=      '''Hello world! cécé herlolip'''




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->str:







      """simple docstring"""

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

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

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

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

            # self attention
            lowercase			:   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)     )}"""
            )

            lowercase			:   Dict					   =		dataavec_layer.self_attn.q_proj.weight
            lowercase			:   str					   =		dataavec_layer.self_attn.q_proj.bias
            lowercase			:   List[str]					   =		dataavec_layer.self_attn.k_proj.weight
            lowercase			:   Any					   =		dataavec_layer.self_attn.k_proj.bias
            lowercase			:   Optional[int]					   =		dataavec_layer.self_attn.v_proj.weight
            lowercase			:   List[Any]					   =		dataavec_layer.self_attn.v_proj.bias

            # self-attention output
            lowercase			:   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}"""
            lowercase			:   int					   =		dataavec_layer.self_attn.out_proj.weight
            lowercase			:   List[Any]					   =		dataavec_layer.self_attn.out_proj.bias
            lowercase			:   Union[str, Any]					   =		dataavec_layer.self_attn_layer_norm.weight
            lowercase			:   Tuple					   =		dataavec_layer.self_attn_layer_norm.bias

            # intermediate
            lowercase			:   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}"""
            lowercase			:   List[Any]					   =		dataavec_layer.fca.weight
            lowercase			:   str					   =		dataavec_layer.fca.bias

            # output
            lowercase			:   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}"""
            lowercase			:   Tuple					   =		dataavec_layer.fca.weight
            lowercase			:   Union[str, Any]					   =		dataavec_layer.fca.bias
            lowercase			:   List[Any]					   =		dataavec_layer.final_layer_norm.weight
            lowercase			:   str					   =		dataavec_layer.final_layer_norm.bias
            # end of layer

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

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

      lowercase			:   Optional[Any]					   =		model(_UpperCamelCase     )[0]
      if classification_head:
            lowercase			:   Tuple					   =		dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(_UpperCamelCase     )     )
      else:
            lowercase			:   Tuple					   =		dataavec_model(_UpperCamelCase     )[0]
      print(our_output.shape,    their_output.shape     )
      lowercase			:   Optional[Any]					   =		torch.max(torch.abs(our_output - their_output     )     ).item()
      print(f"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
      lowercase			:   Optional[Any]					   =		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__":
  __a   				=      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   				=      parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a   				=      {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   				=      [
      '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''YosoForMaskedLM''',
      '''YosoForMultipleChoice''',
      '''YosoForQuestionAnswering''',
      '''YosoForSequenceClassification''',
      '''YosoForTokenClassification''',
      '''YosoLayer''',
      '''YosoModel''',
      '''YosoPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yoso import (
        YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
        YosoForMaskedLM,
        YosoForMultipleChoice,
        YosoForQuestionAnswering,
        YosoForSequenceClassification,
        YosoForTokenClassification,
        YosoLayer,
        YosoModel,
        YosoPreTrainedModel,
    )


else:
  import sys

  __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)

