
import numpy as np







def 				UpperCAmelCase_(    a__ ,						a__="utf-8" ,						a__="surrogatepass"   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = bin(int.from_bytes(text.encode(a__ ,						a__   ) ,						'''big'''   )   )[2:]
    return bits.zfill(8 * ((len(a__   ) + 7) // 8)   )







def 				UpperCAmelCase_(    a__ ,						a__="utf-8" ,						a__="surrogatepass"   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = int(a__ ,						2   )
    return n.to_bytes((n.bit_length() + 7) // 8 ,						'''big'''   ).decode(a__ ,						a__   ) or "\0"







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    if size_par + len(a__   ) <= 2**size_par - (len(a__   ) - 1):
        raise ValueError('''size of parity don\'t match with size of data'''   )

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
    SCREAMING_SNAKE_CASE				:				List[str]				      = []
    SCREAMING_SNAKE_CASE				:				str				      = [bin(a__   )[2:] for x in range(1 ,						size_par + len(a__   ) + 1   )]

    # sorted information data for the size of the output data
    SCREAMING_SNAKE_CASE				:				List[Any]				      = []
    # data position template + parity
    SCREAMING_SNAKE_CASE				:				List[str]				      = []
    # parity bit counter
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 0
    # counter position of data bits
    SCREAMING_SNAKE_CASE				:				int				      = 0

    for x in range(1 ,						size_par + len(a__   ) + 1   ):
        # Performs a template of bit positions - who should be given,
        # and who should be parity
        if qtd_bp < size_par:
            if (np.log(a__   ) / np.log(2   )).is_integer():
                data_out_gab.append('''P'''   )
                SCREAMING_SNAKE_CASE				:				Dict				      = qtd_bp + 1
            else:
                data_out_gab.append('''D'''   )
        else:
            data_out_gab.append('''D'''   )

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_ord.append(data[cont_data]   )
            cont_data += 1
        else:
            data_ord.append(a__   )

    # Calculates parity
    SCREAMING_SNAKE_CASE				:				Tuple				      = 0  # parity bit counter
    for bp in range(1 ,						size_par + 1   ):
        # Bit counter one for a given parity
        SCREAMING_SNAKE_CASE				:				Tuple				      = 0
        # counter to control the loop reading
        SCREAMING_SNAKE_CASE				:				str				      = 0
        for x in data_ord:
            if x is not None:
                try:
                    SCREAMING_SNAKE_CASE				:				str				      = (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''0'''
                if aux == "1" and x == "1":
                    cont_bo += 1
            cont_loop += 1
        parity.append(cont_bo % 2   )

        qtd_bp += 1

    # Mount the message
    SCREAMING_SNAKE_CASE				:				List[Any]				      = 0  # parity bit counter
    for x in range(0 ,						size_par + len(a__   )   ):
        if data_ord[x] is None:
            data_out.append(str(parity[cont_bp]   )   )
            cont_bp += 1
        else:
            data_out.append(data_ord[x]   )

    return data_out







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = []
    # Parity bit counter
    SCREAMING_SNAKE_CASE				:				Any				      = 0
    # Counter p data bit reading
    SCREAMING_SNAKE_CASE				:				List[Any]				      = 0
    # list of parity received
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = []
    SCREAMING_SNAKE_CASE				:				int				      = []

    for x in range(1 ,						len(a__   ) + 1   ):
        # Performs a template of bit positions - who should be given,
        #  and who should be parity
        if qtd_bp < size_par and (np.log(a__   ) / np.log(2   )).is_integer():
            data_out_gab.append('''P'''   )
            SCREAMING_SNAKE_CASE				:				List[Any]				      = qtd_bp + 1
        else:
            data_out_gab.append('''D'''   )

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_output.append(data[cont_data]   )
        else:
            parity_received.append(data[cont_data]   )
        cont_data += 1

    # -----------calculates the parity with the data
    SCREAMING_SNAKE_CASE				:				List[Any]				      = []
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = []
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = [bin(a__   )[2:] for x in range(1 ,						size_par + len(a__   ) + 1   )]

    #  sorted information data for the size of the output data
    SCREAMING_SNAKE_CASE				:				Tuple				      = []
    # Data position feedback + parity
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = []
    # Parity bit counter
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 0
    # Counter p data bit reading
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 0

    for x in range(1 ,						size_par + len(a__   ) + 1   ):
        # Performs a template position of bits - who should be given,
        # and who should be parity
        if qtd_bp < size_par and (np.log(a__   ) / np.log(2   )).is_integer():
            data_out_gab.append('''P'''   )
            SCREAMING_SNAKE_CASE				:				Tuple				      = qtd_bp + 1
        else:
            data_out_gab.append('''D'''   )

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_ord.append(data_output[cont_data]   )
            cont_data += 1
        else:
            data_ord.append(a__   )

    # Calculates parity
    SCREAMING_SNAKE_CASE				:				int				      = 0  # parity bit counter
    for bp in range(1 ,						size_par + 1   ):
        # Bit counter one for a certain parity
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 0
        # Counter to control loop reading
        SCREAMING_SNAKE_CASE				:				List[Any]				      = 0
        for x in data_ord:
            if x is not None:
                try:
                    SCREAMING_SNAKE_CASE				:				Any				      = (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    SCREAMING_SNAKE_CASE				:				Any				      = '''0'''
                if aux == "1" and x == "1":
                    cont_bo += 1
            cont_loop += 1
        parity.append(str(cont_bo % 2   )   )

        qtd_bp += 1

    # Mount the message
    SCREAMING_SNAKE_CASE				:				Tuple				      = 0  # Parity bit counter
    for x in range(0 ,						size_par + len(a__   )   ):
        if data_ord[x] is None:
            data_out.append(str(parity[cont_bp]   )   )
            cont_bp += 1
        else:
            data_out.append(data_ord[x]   )

    SCREAMING_SNAKE_CASE				:				List[str]				      = parity_received == parity
    return data_output, ack


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__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



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

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

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

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

        # self attention
        SCREAMING_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)   )}"""
        )

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

        # self-attention output
        SCREAMING_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}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_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}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_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}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

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

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

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 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__":
      a__				:      str    		=     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__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
