




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_    :       str       =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    :       int       =							'''Hello world! cécé herlolip'''
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__			,		a__  			=							os.path.split(UpperCAmelCase_		)
    a__  			=							DataaVecTextModel.from_pretrained(
        UpperCAmelCase_ ,     checkpoint_file=UpperCAmelCase_		)
    dataavec.eval()  # disable dropout
    a__  			=							dataavec.models[0]
    a__  			=							dataavec_model.encoder.sentence_encoder
    a__  			=							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:
        a__  			=							dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
    print("""Our BERT config:""" ,     UpperCAmelCase_		)

    a__  			=							DataaVecTextForSequenceClassification(UpperCAmelCase_		) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase_		)
    model.eval()

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

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

        # self attention
        a__  			=							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)		)}"""
        )

        a__  			=							dataavec_layer.self_attn.q_proj.weight
        a__  			=							dataavec_layer.self_attn.q_proj.bias
        a__  			=							dataavec_layer.self_attn.k_proj.weight
        a__  			=							dataavec_layer.self_attn.k_proj.bias
        a__  			=							dataavec_layer.self_attn.v_proj.weight
        a__  			=							dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        a__  			=							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}"""
        a__  			=							dataavec_layer.self_attn.out_proj.weight
        a__  			=							dataavec_layer.self_attn.out_proj.bias
        a__  			=							dataavec_layer.self_attn_layer_norm.weight
        a__  			=							dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        a__  			=							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}"""
        a__  			=							dataavec_layer.fca.weight
        a__  			=							dataavec_layer.fca.bias

        # output
        a__  			=							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}"""
        a__  			=							dataavec_layer.fca.weight
        a__  			=							dataavec_layer.fca.bias
        a__  			=							dataavec_layer.final_layer_norm.weight
        a__  			=							dataavec_layer.final_layer_norm.bias
        # end of layer

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

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :       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.'''
 )
 SCREAMING_SNAKE_CASE_    :       str       =							parser.parse_args()
 convert_dataavec_checkpoint_to_pytorch(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )












from __future__ import annotations

import random







class 		__UpperCAmelCase:





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     6

     def __init__(   self			:							int     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							list(range(1    ,					Dice.NUM_SIDES + 1     )     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         return random.choice(self.sides     )






def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ = 2		):
    a__  			=							[Dice() for i in range(UpperCAmelCase_		)]
    a__  			=							[0] * (len(UpperCAmelCase_		) * Dice.NUM_SIDES + 1)
    for _ in range(UpperCAmelCase_		):
        count_of_sum[sum(dice.roll() for dice in dices		)] += 1
    a__  			=							[round((count * 100) / num_throws ,     2		) for count in count_of_sum]
    return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
 import doctest

 doctest.testmod()






