

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						:Any      =			"""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						: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						:List[str]      =			parser.parse_args()
 convert_dataavec_checkpoint_to_pytorch(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )



from __future__ import annotations

from queue import Queue




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple[int, int]:



    """simple docstring"""






    a ^= b
    b ^= a
    a ^= b
    return a, b




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list[list[int]]:



    """simple docstring"""






    UpperCamelCase_          =		1
    while (1 << j) < max_node:
        for i in range(1       ,		max_node + 1       ):
            UpperCamelCase_          =		parent[j - 1][parent[j - 1][i]]
        j += 1
    return parent




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



    """simple docstring"""






    if level[u] < level[v]:
        UpperCamelCase_   ,    UpperCamelCase_          =		swap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    # making depth of u same as depth of v
    for i in range(1_8       ,		-1       ,		-1       ):
        if level[u] - (1 << i) >= level[v]:
            UpperCamelCase_          =		parent[i][u]
    # at the same depth if u==v that mean lca is found
    if u == v:
        return u
    # moving both nodes upwards till lca in found
    for i in range(1_8       ,		-1       ,		-1       ):
        if parent[i][u] not in [0, parent[i][v]]:
            UpperCamelCase_   ,    UpperCamelCase_          =		parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
    return parent[0][u]




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       ,		)->		tuple[list[int], list[list[int]]]:



    """simple docstring"""






    UpperCamelCase_          =		0
    UpperCamelCase_          =		Queue(maxsize=SCREAMING_SNAKE_CASE_       )
    q.put(SCREAMING_SNAKE_CASE_       )
    while q.qsize() != 0:
        UpperCamelCase_          =		q.get()
        for v in graph[u]:
            if level[v] == -1:
                UpperCamelCase_          =		level[u] + 1
                q.put(SCREAMING_SNAKE_CASE_       )
                UpperCamelCase_          =		u
    return level, parent




def        lowerCAmelCase(	)->		None:



    """simple docstring"""






    UpperCamelCase_          =		1_3
    # initializing with 0
    UpperCamelCase_          =		[[0 for _ in range(max_node + 1_0       )] for _ in range(2_0       )]
    # initializing with -1 which means every node is unvisited
    UpperCamelCase_          =		[-1 for _ in range(max_node + 1_0       )]
    UpperCamelCase_          =		{
        1: [2, 3, 4],
        2: [5],
        3: [6, 7],
        4: [8],
        5: [9, 1_0],
        6: [1_1],
        7: [],
        8: [1_2, 1_3],
        9: [],
        1_0: [],
        1_1: [],
        1_2: [],
        1_3: [],
    }
    UpperCamelCase_   ,    UpperCamelCase_          =		breadth_first_search(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		1       )
    UpperCamelCase_          =		create_sparse(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    print("LCA of node 1 and 3 is: "       ,		lowest_common_ancestor(1       ,		3       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 5 and 6 is: "       ,		lowest_common_ancestor(5       ,		6       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 7 and 11 is: "       ,		lowest_common_ancestor(7       ,		1_1       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 6 and 7 is: "       ,		lowest_common_ancestor(6       ,		7       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 4 and 12 is: "       ,		lowest_common_ancestor(4       ,		1_2       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 8 and 8 is: "       ,		lowest_common_ancestor(8       ,		8       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )


if __name__ == "__main__":
 main()
