

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()
SCREAMING_SNAKE_CASE		:Dict				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:str				  =    """Hello world! cécé herlolip"""




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	List[str]:



    """simple docstring"""






    UpperCamelCase_     ,					UpperCamelCase_      =	os.path.split(SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	DataaVecTextModel.from_pretrained(
        SCREAMING_SNAKE_CASE_ ,	checkpoint_file=SCREAMING_SNAKE_CASE_ )
    dataavec.eval()  # disable dropout
    UpperCamelCase_      =	dataavec.models[0]
    UpperCamelCase_      =	dataavec_model.encoder.sentence_encoder
    UpperCamelCase_      =	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_      =	dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
    print("Our BERT config:" ,	SCREAMING_SNAKE_CASE_ )

    UpperCamelCase_      =	DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE_ ) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE_ )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    UpperCamelCase_      =	dataavec_sent_encoder.embed_tokens.weight
    UpperCamelCase_      =	dataavec_sent_encoder.embed_positions.weight
    UpperCamelCase_      =	torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight )  # just zero them out b/c data2vec doesn't use them.
    UpperCamelCase_      =	dataavec_sent_encoder.layernorm_embedding.weight
    UpperCamelCase_      =	dataavec_sent_encoder.layernorm_embedding.bias

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

        # self attention
        UpperCamelCase_      =	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_      =	dataavec_layer.self_attn.q_proj.weight
        UpperCamelCase_      =	dataavec_layer.self_attn.q_proj.bias
        UpperCamelCase_      =	dataavec_layer.self_attn.k_proj.weight
        UpperCamelCase_      =	dataavec_layer.self_attn.k_proj.bias
        UpperCamelCase_      =	dataavec_layer.self_attn.v_proj.weight
        UpperCamelCase_      =	dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        UpperCamelCase_      =	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_      =	dataavec_layer.self_attn.out_proj.weight
        UpperCamelCase_      =	dataavec_layer.self_attn.out_proj.bias
        UpperCamelCase_      =	dataavec_layer.self_attn_layer_norm.weight
        UpperCamelCase_      =	dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        UpperCamelCase_      =	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_      =	dataavec_layer.fca.weight
        UpperCamelCase_      =	dataavec_layer.fca.bias

        # output
        UpperCamelCase_      =	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_      =	dataavec_layer.fca.weight
        UpperCamelCase_      =	dataavec_layer.fca.bias
        UpperCamelCase_      =	dataavec_layer.final_layer_norm.weight
        UpperCamelCase_      =	dataavec_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        UpperCamelCase_      =	dataavec.model.classification_heads["mnli"].dense.weight
        UpperCamelCase_      =	dataavec.model.classification_heads["mnli"].dense.bias
        UpperCamelCase_      =	dataavec.model.classification_heads["mnli"].out_proj.weight
        UpperCamelCase_      =	dataavec.model.classification_heads["mnli"].out_proj.bias
    else:
        # LM Head
        UpperCamelCase_      =	dataavec_model.encoder.lm_head.dense.weight
        UpperCamelCase_      =	dataavec_model.encoder.lm_head.dense.bias
        UpperCamelCase_      =	dataavec_model.encoder.lm_head.layer_norm.weight
        UpperCamelCase_      =	dataavec_model.encoder.lm_head.layer_norm.bias
        UpperCamelCase_      =	dataavec_model.encoder.lm_head.weight
        UpperCamelCase_      =	dataavec_model.encoder.lm_head.bias

    # Let's check that we get the same results.
    UpperCamelCase_      =	dataavec.encode(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 )  # batch of size 1

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



def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	str:



    """simple docstring"""






    if power < 0:
        return -1
    base %= mod
    UpperCamelCase_      =	1

    while power > 0:
        if power & 1:
            UpperCamelCase_      =	(result * base) % mod
        UpperCamelCase_      =	power >> 1
        UpperCamelCase_      =	(base * base) % mod

    return result




def  lowerCAmelCase(			)->	Tuple:



    """simple docstring"""






    print(modular_exponential(3 ,	2_0_0 ,	1_3 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
