
import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    assert isinstance(a__ ,						a__   )
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory''' ,						[False, True]   )
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE				:				Dict				      = CsvDatasetReader(a__ ,						cache_dir=a__ ,						keep_in_memory=a__   ).read()
    _check_csv_dataset(a__ ,						a__   )







@pytest.mark.parametrize(
    '''features''' ,						[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ] ,						)
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = tmp_path / '''cache'''
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = features.copy() if features else default_expected_features
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = (
        Features({feature: Value(a__   ) for feature, dtype in features.items()}   ) if features is not None else None
    )
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CsvDatasetReader(a__ ,						features=a__ ,						cache_dir=a__   ).read()
    _check_csv_dataset(a__ ,						a__   )







@pytest.mark.parametrize('''split''' ,						[None, NamedSplit('''train'''   ), '''train''', '''test''']   )
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Dict				      = {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE				:				str				      = CsvDatasetReader(a__ ,						cache_dir=a__ ,						split=a__   ).read()
    _check_csv_dataset(a__ ,						a__   )
    assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type''' ,						[str, list]   )
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    if issubclass(a__ ,						a__   ):
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = csv_path
    elif issubclass(a__ ,						a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = [csv_path]
    SCREAMING_SNAKE_CASE				:				Any				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Tuple				      = {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = CsvDatasetReader(a__ ,						cache_dir=a__   ).read()
    _check_csv_dataset(a__ ,						a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__=("train",)   ):



    """simple docstring"""



    assert isinstance(a__ ,						a__   )
    for split in splits:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory''' ,						[False, True]   )
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Dict				      = {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE				:				List[str]				      = CsvDatasetReader({'''train''': csv_path} ,						cache_dir=a__ ,						keep_in_memory=a__   ).read()
    _check_csv_datasetdict(a__ ,						a__   )







@pytest.mark.parametrize(
    '''features''' ,						[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ] ,						)
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = tmp_path / '''cache'''
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = features.copy() if features else default_expected_features
    SCREAMING_SNAKE_CASE				:				List[str]				      = (
        Features({feature: Value(a__   ) for feature, dtype in features.items()}   ) if features is not None else None
    )
    SCREAMING_SNAKE_CASE				:				List[str]				      = CsvDatasetReader({'''train''': csv_path} ,						features=a__ ,						cache_dir=a__   ).read()
    _check_csv_datasetdict(a__ ,						a__   )







@pytest.mark.parametrize('''split''' ,						[None, NamedSplit('''train'''   ), '''train''', '''test''']   )
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    if split:
        SCREAMING_SNAKE_CASE				:				Any				      = {split: csv_path}
    else:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {'''train''': csv_path, '''test''': csv_path}
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Dict				      = {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CsvDatasetReader(a__ ,						cache_dir=a__   ).read()
    _check_csv_datasetdict(a__ ,						a__ ,						splits=list(path.keys()   )   )
    assert all(dataset[split].split == split for split in path.keys()   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    with open(a__ ,						encoding='''utf-8'''   ) as csvfile:
        yield from csv.reader(a__   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = os.path.join(a__ ,						'''tmp.csv'''   )
    SCREAMING_SNAKE_CASE				:				str				      = CsvDatasetReader({'''train''': csv_path} ,						cache_dir=a__   ).read()
    CsvDatasetWriter(dataset['''train'''] ,						a__ ,						num_proc=1   ).write()

    SCREAMING_SNAKE_CASE				:				List[str]				      = iter_csv_file(a__   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = iter_csv_file(a__   )

    for rowa, rowa in zip(a__ ,						a__   ):
        assert rowa == rowa







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Any				      = os.path.join(a__ ,						'''tmp.csv'''   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = CsvDatasetReader({'''train''': csv_path} ,						cache_dir=a__   ).read()
    CsvDatasetWriter(dataset['''train'''] ,						a__ ,						num_proc=2   ).write()

    SCREAMING_SNAKE_CASE				:				Dict				      = iter_csv_file(a__   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = iter_csv_file(a__   )

    for rowa, rowa in zip(a__ ,						a__   ):
        assert rowa == rowa







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = os.path.join(a__ ,						'''tmp.csv'''   )
    SCREAMING_SNAKE_CASE				:				str				      = CsvDatasetReader({'''train''': csv_path} ,						cache_dir=a__   ).read()
    with pytest.raises(a__   ):
        CsvDatasetWriter(dataset['''train'''] ,						a__ ,						num_proc=0   )



"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 512 * 9    ,	lowercase_  :       int = 512 * 3    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 128    ,	lowercase_  :       int = 5    ,	lowercase_  :       int = 1024    ,	lowercase_  :       str = "multi_hop"    ,	lowercase_  :       int = 512    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 12    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 32    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       str = "gelu"    ,	lowercase_  :       float = None    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 0    ,	lowercase_  :       bool = False    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       int = 8    ,	lowercase_  :       int = None    ,	lowercase_  :       int = None    ,	lowercase_  :       bool = True    ,	lowercase_  :       bool = True    ,	lowercase_  :       List[str]=0    ,	lowercase_  :       Union[str, Any]=1    ,	lowercase_  :       str=2    ,	**lowercase_  :       str    ,	):
								UpperCamelCase__		:     Dict								=num_classes
								UpperCamelCase__		:     str								=num_atoms
								UpperCamelCase__		:     Union[str, Any]								=num_in_degree
								UpperCamelCase__		:     Optional[int]								=num_out_degree
								UpperCamelCase__		:     List[str]								=num_edges
								UpperCamelCase__		:     List[str]								=num_spatial
								UpperCamelCase__		:     Dict								=num_edge_dis
								UpperCamelCase__		:     Tuple								=edge_type
								UpperCamelCase__		:     str								=multi_hop_max_dist
								UpperCamelCase__		:     Optional[Any]								=spatial_pos_max
								UpperCamelCase__		:     Tuple								=max_nodes
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Optional[Any]								=embedding_dim
								UpperCamelCase__		:     Dict								=embedding_dim
								UpperCamelCase__		:     Dict								=ffn_embedding_dim
								UpperCamelCase__		:     str								=num_attention_heads
								UpperCamelCase__		:     Tuple								=dropout
								UpperCamelCase__		:     List[str]								=attention_dropout
								UpperCamelCase__		:     List[str]								=layerdrop
								UpperCamelCase__		:     Tuple								=encoder_normalize_before
								UpperCamelCase__		:     List[Any]								=pre_layernorm
								UpperCamelCase__		:     Optional[Any]								=apply_graphormer_init
								UpperCamelCase__		:     Tuple								=activation_fn
								UpperCamelCase__		:     Any								=embed_scale
								UpperCamelCase__		:     List[str]								=freeze_embeddings
								UpperCamelCase__		:     Optional[Any]								=num_trans_layers_to_freeze
								UpperCamelCase__		:     List[Any]								=share_input_output_embed
								UpperCamelCase__		:     Union[str, Any]								=traceable
								UpperCamelCase__		:     List[Any]								=q_noise
								UpperCamelCase__		:     Union[str, Any]								=qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								UpperCamelCase__		:     Any								=kdim
								UpperCamelCase__		:     Tuple								=vdim
								UpperCamelCase__		:     Any								=self_attention
								UpperCamelCase__		:     List[str]								=bias

								super().__init__(
								    pad_token_id=lowercase_    ,	bos_token_id=lowercase_    ,	eos_token_id=lowercase_    ,	**lowercase_    ,	)


