





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_UpperCAmelCase				:       Union[str, Any]	   =				[
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=True )			->     Optional[Any]:
    if with_config:
        return [
            {
                "testcase_name": d["dataset"] + "/" + d["config_name"],
                "dataset": d["dataset"],
                "config_name": d["config_name"],
            }
            for d in DATASETS_ON_HF_GCP
        ]
    else:
        return [
            {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
        ]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCamelCase			)			)
class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Union[str, Any]	)			->	Dict:
     with TemporaryDirectory() as tmp_dir:
         lowerCamelCase__    :      List[Any]					 =		dataset_module_factory(UpperCAmelCase			,     cache_dir=UpperCAmelCase	)

         lowerCamelCase__    :      int					 =		import_main_class(dataset_module.module_path			,     dataset=UpperCAmelCase	)

         lowerCamelCase__    :      DatasetBuilder					 =		builder_cls(
             cache_dir=UpperCAmelCase			,     config_name=UpperCAmelCase			,     hash=dataset_module.hash			,     )

         lowerCamelCase__    :      int					 =		'/'.join(
             [
                 HF_GCP_BASE_URL,
                 builder_instance._relative_data_dir(with_hash=UpperCAmelCase	).replace(os.sep			,     '/'	),
                 config.DATASET_INFO_FILENAME,
             ]	)
         lowerCamelCase__    :      Optional[int]					 =		cached_path(UpperCAmelCase			,     cache_dir=UpperCAmelCase	)
         self.assertTrue(os.path.exists(UpperCAmelCase	)	)


@pytest.mark.integration
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Tuple					 =		tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
    lowerCamelCase__    :      Tuple					 =		dataset_module_factory('wikipedia'						,	cache_dir=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		import_main_class(dataset_module.module_path )
    lowerCamelCase__    :      DatasetBuilder					 =		builder_cls(
        cache_dir=_UpperCAmelCase						,	config_name='20220301.frr'						,	hash=dataset_module.hash						,	)
    # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
    lowerCamelCase__    :      Tuple					 =		None
    builder_instance.download_and_prepare()
    lowerCamelCase__    :      str					 =		builder_instance.as_dataset()
    assert ds


@pytest.mark.integration
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      str					 =		dataset_module_factory('wikipedia'						,	cache_dir=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		import_main_class(dataset_module.module_path						,	dataset=_UpperCAmelCase )
    lowerCamelCase__    :      DatasetBuilder					 =		builder_cls(
        cache_dir=_UpperCAmelCase						,	config_name='20220301.frr'						,	hash=dataset_module.hash						,	)
    lowerCamelCase__    :      Tuple					 =		builder_instance.as_streaming_dataset()
    assert ds
    assert isinstance(_UpperCAmelCase						,	_UpperCAmelCase )
    assert "train" in ds
    assert isinstance(ds['train']						,	_UpperCAmelCase )
    assert next(iter(ds['train'] ) )












import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	max_position_embeddings=1024						,	encoder_layers=args.encoder_layers						,	decoder_layers=args.decoder_layers						,	encoder_attention_heads=args.encoder_attention_heads						,	decoder_attention_heads=args.decoder_attention_heads						,	encoder_ffn_dim=args.encoder_ffn_embed_dim						,	decoder_ffn_dim=args.decoder_ffn_embed_dim						,	d_model=args.encoder_embed_dim						,	encoder_layerdrop=args.encoder_layerdrop						,	decoder_layerdrop=args.decoder_layerdrop						,	dropout=args.dropout						,	attention_dropout=args.attention_dropout						,	activation_dropout=args.activation_dropout						,	activation_function='relu'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
    parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





