






'''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_					=				logging.get_logger(__name__)

UpperCamelCase_					=				"Hello world! cécé herlolip"






def  lowerCAmelCase__		(					a_  :     str			,						a_  :     str			,						a_  :     bool			)   -> str:
       UpperCAmelCase__							,							UpperCAmelCase__	:   Optional[Any]												=	os.path.split(a_			)
       UpperCAmelCase__	:   Optional[Any]												=	DataaVecTextModel.from_pretrained(
           a_			,						checkpoint_file=a_			)
       dataavec.eval()  # disable dropout
       UpperCAmelCase__	:   Optional[Any]												=	dataavec.models[0]
       UpperCAmelCase__	:   List[str]												=	dataavec_model.encoder.sentence_encoder
       UpperCAmelCase__	:   int												=	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=5_1_4			,						type_vocab_size=1			,						layer_norm_eps=1E-5			,						)
       if classification_head:
              UpperCAmelCase__	:   Tuple												=	dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
       print('''Our BERT config:'''			,						a_			)

       UpperCAmelCase__	:   List[str]												=	DataaVecTextForSequenceClassification(a_			) if classification_head else DataaVecTextForMaskedLM(a_			)
       model.eval()

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

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

              # self attention
              UpperCAmelCase__	:   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)			)}"""
              )

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

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

              # intermediate
              UpperCAmelCase__	:   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}"""
              UpperCAmelCase__	:   Optional[Any]												=	dataavec_layer.fca.weight
              UpperCAmelCase__	:   Optional[Any]												=	dataavec_layer.fca.bias

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

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

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

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

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


if __name__ == "__main__":
  UpperCamelCase_					=				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_					=				parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )







'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor