


from ...configuration_utils import PretrainedConfig


A			:						Optional[int]        =  {
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''tapas'''

					def __init__(self       :		Optional[int] ,   _UpperCAmelCase       :		int=3_0522 ,   _UpperCAmelCase       :		str=768 ,   _UpperCAmelCase       :		Optional[int]=12 ,   _UpperCAmelCase       :		int=12 ,   _UpperCAmelCase       :		Tuple=3072 ,   _UpperCAmelCase       :		Dict="gelu" ,   _UpperCAmelCase       :		List[Any]=0.1 ,   _UpperCAmelCase       :		Optional[Any]=0.1 ,   _UpperCAmelCase       :		List[str]=1024 ,   _UpperCAmelCase       :		Any=[3, 256, 256, 2, 256, 256, 10] ,   _UpperCAmelCase       :		List[Any]=0.02 ,   _UpperCAmelCase       :		Union[str, Any]=1E-1_2 ,   _UpperCAmelCase       :		Optional[int]=0 ,   _UpperCAmelCase       :		List[str]=10.0 ,   _UpperCAmelCase       :		Optional[int]=0 ,   _UpperCAmelCase       :		List[Any]=1.0 ,   _UpperCAmelCase       :		Dict=None ,   _UpperCAmelCase       :		Dict=1.0 ,   _UpperCAmelCase       :		str=False ,   _UpperCAmelCase       :		int=None ,   _UpperCAmelCase       :		List[str]=1.0 ,   _UpperCAmelCase       :		Union[str, Any]=1.0 ,   _UpperCAmelCase       :		List[str]=False ,   _UpperCAmelCase       :		Dict=False ,   _UpperCAmelCase       :		Any="ratio" ,   _UpperCAmelCase       :		Optional[int]=None ,   _UpperCAmelCase       :		Dict=None ,   _UpperCAmelCase       :		List[Any]=64 ,   _UpperCAmelCase       :		Any=32 ,   _UpperCAmelCase       :		Optional[Any]=False ,   _UpperCAmelCase       :		List[str]=True ,   _UpperCAmelCase       :		List[str]=False ,   _UpperCAmelCase       :		Tuple=False ,   _UpperCAmelCase       :		Any=True ,   _UpperCAmelCase       :		List[Any]=False ,   _UpperCAmelCase       :		Tuple=None ,   _UpperCAmelCase       :		Any=None ,   **_UpperCAmelCase       :		Dict ,   )		->  Dict:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase ,   **_UpperCAmelCase						)

							# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_sizes
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps

							# Fine-tuning task hyperparameters
							lowercase__	     =						positive_label_weight
							lowercase__	     =						num_aggregation_labels
							lowercase__	     =						aggregation_loss_weight
							lowercase__	     =						use_answer_as_supervision
							lowercase__	     =						answer_loss_importance
							lowercase__	     =						use_normalized_answer_loss
							lowercase__	     =						huber_loss_delta
							lowercase__	     =						temperature
							lowercase__	     =						aggregation_temperature
							lowercase__	     =						use_gumbel_for_cells
							lowercase__	     =						use_gumbel_for_aggregation
							lowercase__	     =						average_approximation_function
							lowercase__	     =						cell_selection_preference
							lowercase__	     =						answer_loss_cutoff
							lowercase__	     =						max_num_rows
							lowercase__	     =						max_num_columns
							lowercase__	     =						average_logits_per_cell
							lowercase__	     =						select_one_column
							lowercase__	     =						allow_empty_column_selection
							lowercase__	     =						init_cell_selection_weights_to_zero
							lowercase__	     =						reset_position_index_per_cell
							lowercase__	     =						disable_per_token_loss

							# Aggregation hyperparameters
							lowercase__	     =						aggregation_labels
							lowercase__	     =						no_aggregation_label_index

							if isinstance(self.aggregation_labels ,   _UpperCAmelCase						):
									lowercase__	     =						{int(_UpperCAmelCase						): v for k, v in aggregation_labels.items()}






"""simple docstring"""







import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			str						    = tmp_path / "file.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = tmp_path / "malformed_file.csv"
						snake_case   :			Union[str, Any]						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20,\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			Dict						    = tmp_path / "csv_with_image.csv"
						snake_case   :			List[Any]						    = textwrap.dedent(
						    f'''\
        image
        {image_file}
        ''' )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = tmp_path / "csv_with_label.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        label\n        good\n        bad\n        good\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = tmp_path / "csv_with_int_list.csv"
						snake_case   :			Optional[int]						    = textwrap.dedent(
						    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Tuple						    = Csv()
						snake_case   :			str						    = csv._generate_tables([[csv_file, malformed_csv_file]] )
						with pytest.raises(lowerCamelCase_     ,		match="Error tokenizing data" ):
												for _ in generator:
																		pass
						assert any(
						    record.levelname == "ERROR"
						    and "Failed to read file" in record.message
						    and os.path.basename(lowerCamelCase_ ) in record.message
						    for record in caplog.records )

@require_pil
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						with open(lowerCamelCase_     ,		encoding="utf-8" ) as f:
												snake_case   :			List[Any]						    = f.read().splitlines()[1]
						snake_case   :			List[str]						    = Csv(encoding="utf-8"     ,		features=Features({"image": Image()} ) )
						snake_case   :			Optional[int]						    = csv._generate_tables([[csv_file_with_image]] )
						snake_case   :			List[Any]						    = pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field("image" ).type == Image()()
						snake_case   :			str						    = pa_table.to_pydict()["image"]
						assert generated_content == [{"path": image_file, "bytes": None}]

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						with open(lowerCamelCase_     ,		encoding="utf-8" ) as f:
												snake_case   :			Optional[Any]						    = f.read().splitlines()[1:]
						snake_case   :			List[str]						    = Csv(encoding="utf-8"     ,		features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
						snake_case   :			int						    = csv._generate_tables([[csv_file_with_label]] )
						snake_case   :			Dict						    = pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
						snake_case   :			Dict						    = pa_table.to_pydict()["label"]
						assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(lowerCamelCase_ ) for label in labels]

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = Csv(encoding="utf-8"     ,		sep=","     ,		converters={"int_list": lambda lowerCamelCase_ : [int(lowerCamelCase_ ) for i in x.split()]} )
						snake_case   :			Tuple						    = csv._generate_tables([[csv_file_with_int_list]] )
						snake_case   :			str						    = pa.concat_tables([table for _, table in generator] )
						assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
						snake_case   :			Any						    = pa_table.to_pydict()["int_list"]
						assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



