




"""simple docstring"""







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_      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =					'Hello world! cécé herlolip'





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase					,  UpperCamelCase										=  os.path.split(_lowercase						)
    UpperCamelCase										=  DataaVecTextModel.from_pretrained(
        _lowercase ,checkpoint_file=_lowercase						)
    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=514 ,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:''' ,_lowercase						)

    UpperCamelCase										=  DataaVecTextForSequenceClassification(_lowercase						) if classification_head else DataaVecTextForMaskedLM(_lowercase						)
    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(_lowercase						).unsqueeze(0						)  # batch of size 1

    UpperCamelCase										=  model(_lowercase						)[0]
    if classification_head:
        UpperCamelCase										=  dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(_lowercase						)						)
    else:
        UpperCamelCase										=  dataavec_model(_lowercase						)[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(_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__":
  SCREAMING_SNAKE_CASE_      =					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_      =					parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )





"""simple docstring"""







import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  Dataset.from_dict({'''i''': range(17						)}						)
    UpperCamelCase										=  len(_lowercase						)
    UpperCamelCase										=  3
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(_lowercase						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





@pytest.mark.parametrize('''shards_per_node''' ,[1, 2, 3]						)
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    def gen(_lowercase						):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  shards_per_node * world_size
    UpperCamelCase										=  {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(_lowercase						)]}
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase ,gen_kwargs=_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    assert full_ds.n_shards == world_size * shards_per_node
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  2
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)

    UpperCamelCase										=  split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle(seed=42						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle()

    UpperCamelCase										=  split_dataset_by_node(full_ds.shuffle(seed=42						) ,rank=0 ,world_size=_lowercase						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(full_ds.shuffle() ,rank=0 ,world_size=_lowercase						)





@pytest.mark.parametrize('''streaming''' ,[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  2
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)





@pytest.mark.parametrize(
    '''nproc_per_node, num_workers''' ,[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ] ,)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  True
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)