




'''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()
A_ 			=		logging.get_logger(__name__)

A_ 			=		"Hello world! cécé herlolip"






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__			,       snake_case__					:      Optional[Any]  			=						os.path.split(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      int  			=						DataaVecTextModel.from_pretrained(
          __SCREAMING_SNAKE_CASE							,			checkpoint_file=__SCREAMING_SNAKE_CASE		)
      dataavec.eval()  # disable dropout
      snake_case__					:      List[str]  			=						dataavec.models[0]
      snake_case__					:      List[str]  			=						dataavec_model.encoder.sentence_encoder
      snake_case__					:      str  			=						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:'							,			__SCREAMING_SNAKE_CASE		)

      snake_case__					:      Any  			=						DataaVecTextForSequenceClassification(__SCREAMING_SNAKE_CASE		) if classification_head else DataaVecTextForMaskedLM(__SCREAMING_SNAKE_CASE		)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      snake_case__					:      Union[str, Any]  			=						dataavec_sent_encoder.embed_tokens.weight
      snake_case__					:      int  			=						dataavec_sent_encoder.embed_positions.weight
      snake_case__					:      Dict  			=						torch.zeros_like(
          model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
      snake_case__					:      Optional[int]  			=						dataavec_sent_encoder.layernorm_embedding.weight
      snake_case__					:      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[int]  			=						dataavec_layer.self_attn.q_proj.weight
            snake_case__					:      Optional[Any]  			=						dataavec_layer.self_attn.q_proj.bias
            snake_case__					:      Tuple  			=						dataavec_layer.self_attn.k_proj.weight
            snake_case__					:      Tuple  			=						dataavec_layer.self_attn.k_proj.bias
            snake_case__					:      Dict  			=						dataavec_layer.self_attn.v_proj.weight
            snake_case__					:      Optional[int]  			=						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__					:      Dict  			=						dataavec_layer.self_attn.out_proj.weight
            snake_case__					:      Dict  			=						dataavec_layer.self_attn.out_proj.bias
            snake_case__					:      Tuple  			=						dataavec_layer.self_attn_layer_norm.weight
            snake_case__					:      int  			=						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__					:      List[Any]  			=						dataavec_layer.fca.weight
            snake_case__					:      Dict  			=						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__					:      Optional[Any]  			=						dataavec_layer.fca.weight
            snake_case__					:      Union[str, Any]  			=						dataavec_layer.fca.bias
            snake_case__					:      Any  			=						dataavec_layer.final_layer_norm.weight
            snake_case__					:      List[Any]  			=						dataavec_layer.final_layer_norm.bias
            # end of layer

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

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

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

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


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
      )






'''simple docstring'''






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 1000		)						->      int:
      snake_case__					:      List[str]  			=						0
      snake_case__					:      int  			=						(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      snake_case__					:      Optional[Any]  			=						(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      snake_case__					:      Tuple  			=						(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total


if __name__ == "__main__":
      print(F'{solution() = }')
