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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: Any ,	lowerCAmelCase_: int     ):
      snake_case_     :			Optional[Any]       =       os.path.split(lowercase_     )
      snake_case_     :			Any       =       DataaVecTextModel.from_pretrained(
          lowercase_ ,	checkpoint_file=lowercase_     )
      dataavec.eval()  # disable dropout
      snake_case_     :			int       =       dataavec.models[0]
      snake_case_     :			int       =       dataavec_model.encoder.sentence_encoder
      snake_case_     :			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=5_1_4 ,	type_vocab_size=1 ,	layer_norm_eps=1e-5 ,	)
      if classification_head:
            snake_case_     :			Union[str, Any]       =       dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
      print("Our BERT config:" ,	lowercase_     )

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

      # Now let's copy all the weights.
      # Embeddings
      snake_case_     :			Tuple       =       dataavec_sent_encoder.embed_tokens.weight
      snake_case_     :			Optional[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_     :			Dict       =       dataavec_sent_encoder.layernorm_embedding.weight
      snake_case_     :			Optional[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_     :			Union[str, Any]       =       dataavec_layer.self_attn.q_proj.bias
            snake_case_     :			Union[str, Any]       =       dataavec_layer.self_attn.k_proj.weight
            snake_case_     :			str       =       dataavec_layer.self_attn.k_proj.bias
            snake_case_     :			Union[str, Any]       =       dataavec_layer.self_attn.v_proj.weight
            snake_case_     :			List[Any]       =       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_     :			str       =       dataavec_layer.self_attn.out_proj.bias
            snake_case_     :			Any       =       dataavec_layer.self_attn_layer_norm.weight
            snake_case_     :			Optional[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_     :			Optional[int]       =       dataavec_layer.fca.weight
            snake_case_     :			List[str]       =       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_     :			List[Any]       =       dataavec_layer.fca.weight
            snake_case_     :			Any       =       dataavec_layer.fca.bias
            snake_case_     :			Optional[Any]       =       dataavec_layer.final_layer_norm.weight
            snake_case_     :			str       =       dataavec_layer.final_layer_norm.bias
            # end of layer

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

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

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

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


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
     )




from __future__ import annotations

UpperCAmelCase							=       "Alexander Joslin"






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int     ):

      if number <= 0:
            snake_case_     :			Dict       =       f"n must be greater than 0. Got n = {number}"
            raise ValueError(lowerCAmelCase_     )

      snake_case_     :			Tuple       =       [number + 1] * (number + 1)

      # starting position
      snake_case_     :			Union[str, Any]       =       0
      for i in range(1 ,	lowerCAmelCase_     ):
            snake_case_     :			int       =       min(table[i + 1] ,	table[i] + 1     )
            # check if out of bounds
            if i * 2 <= number:
                  snake_case_     :			Tuple       =       min(table[i * 2] ,	table[i] + 1     )
            # check if out of bounds
            if i * 3 <= number:
                  snake_case_     :			Dict       =       min(table[i * 3] ,	table[i] + 1     )
      return table[number]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



