





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 			__UpperCamelCase				(			lowercase__	:       str		,    lowercase__	:       str		,    lowercase__	:       bool		)	->      str:
    '''simple docstring'''





    lowerCAmelCase_		,		lowerCAmelCase_					:	Dict						=   os.path.split(lowercase__		)
    lowerCAmelCase_					:	Optional[Any]						=   DataaVecTextModel.from_pretrained(
        lowercase__		,    checkpoint_file=lowercase__		)
    dataavec.eval()  # disable dropout
    lowerCAmelCase_					:	str						=   dataavec.models[0]
    lowerCAmelCase_					:	Optional[int]						=   dataavec_model.encoder.sentence_encoder
    lowerCAmelCase_					:	Optional[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=514		,    type_vocab_size=1		,    layer_norm_eps=1E-5		,    )
    if classification_head:
        lowerCAmelCase_					:	List[str]						=   dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
    print("""Our BERT config:"""		,    lowercase__		)

    lowerCAmelCase_					:	Optional[Any]						=   DataaVecTextForSequenceClassification(lowercase__		) if classification_head else DataaVecTextForMaskedLM(lowercase__		)
    model.eval()

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

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

        # self attention
        lowerCAmelCase_					:	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)		)}'
        )

        lowerCAmelCase_					:	Union[str, Any]						=   dataavec_layer.self_attn.q_proj.weight
        lowerCAmelCase_					:	int						=   dataavec_layer.self_attn.q_proj.bias
        lowerCAmelCase_					:	List[str]						=   dataavec_layer.self_attn.k_proj.weight
        lowerCAmelCase_					:	int						=   dataavec_layer.self_attn.k_proj.bias
        lowerCAmelCase_					:	Dict						=   dataavec_layer.self_attn.v_proj.weight
        lowerCAmelCase_					:	Union[str, Any]						=   dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        lowerCAmelCase_					:	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}'
        lowerCAmelCase_					:	List[Any]						=   dataavec_layer.self_attn.out_proj.weight
        lowerCAmelCase_					:	List[Any]						=   dataavec_layer.self_attn.out_proj.bias
        lowerCAmelCase_					:	str						=   dataavec_layer.self_attn_layer_norm.weight
        lowerCAmelCase_					:	List[Any]						=   dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        lowerCAmelCase_					:	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}'
        lowerCAmelCase_					:	Optional[int]						=   dataavec_layer.fca.weight
        lowerCAmelCase_					:	Optional[Any]						=   dataavec_layer.fca.bias

        # output
        lowerCAmelCase_					:	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}'
        lowerCAmelCase_					:	int						=   dataavec_layer.fca.weight
        lowerCAmelCase_					:	Optional[Any]						=   dataavec_layer.fca.bias
        lowerCAmelCase_					:	str						=   dataavec_layer.final_layer_norm.weight
        lowerCAmelCase_					:	List[str]						=   dataavec_layer.final_layer_norm.bias
        # end of layer

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

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

    lowerCAmelCase_					:	Optional[Any]						=   model(lowercase__		)[0]
    if classification_head:
        lowerCAmelCase_					:	List[Any]						=   dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(lowercase__		)		)
    else:
        lowerCAmelCase_					:	Any						=   dataavec_model(lowercase__		)[0]
    print(our_output.shape		,    their_output.shape		)
    lowerCAmelCase_					:	Union[str, Any]						=   torch.max(torch.abs(our_output - their_output		)		).item()
    print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
    lowerCAmelCase_					:	str						=   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
  )









import math
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      list:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive integers have prime factors"""		)
    lowerCAmelCase_					:	str						=   []
    while n % 2 == 0:
        pf.append(2		)
        lowerCAmelCase_					:	str						=   int(n / 2		)
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        while n % i == 0:
            pf.append(lowercase__		)
            lowerCAmelCase_					:	List[Any]						=   int(n / i		)
    if n > 2:
        pf.append(lowercase__		)
    return pf
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive numbers are accepted"""		)
    lowerCAmelCase_					:	Union[str, Any]						=   1
    lowerCAmelCase_					:	str						=   1
    while n % 2 == 0:
        temp += 1
        lowerCAmelCase_					:	Optional[Any]						=   int(n / 2		)
    div *= temp
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        lowerCAmelCase_					:	Tuple						=   1
        while n % i == 0:
            temp += 1
            lowerCAmelCase_					:	Any						=   int(n / i		)
        div *= temp
    if n > 1:
        div *= 2
    return div
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive numbers are accepted"""		)
    lowerCAmelCase_					:	Tuple						=   1
    lowerCAmelCase_					:	str						=   1
    while n % 2 == 0:
        temp += 1
        lowerCAmelCase_					:	Optional[int]						=   int(n / 2		)
    if temp > 1:
        s *= (2**temp - 1) / (2 - 1)
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        lowerCAmelCase_					:	int						=   1
        while n % i == 0:
            temp += 1
            lowerCAmelCase_					:	Any						=   int(n / i		)
        if temp > 1:
            s *= (i**temp - 1) / (i - 1)
    return int(lowercase__		)
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    lowerCAmelCase_					:	str						=   n
    for x in set(prime_factors(lowercase__		)		):
        s *= (x - 1) / x
    return int(lowercase__		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


