


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       lowercase_		(		_A			:    Union[str, Any]						):

      """simple docstring"""




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

@pytest.fixture
def       lowercase_		(		_A			:    Optional[int]						):

      """simple docstring"""




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

@pytest.fixture
def       lowercase_		(		_A			:    Dict    ,					_A			:    Tuple						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[int]    =  tmp_path / "csv_with_image.csv"
      lowerCamelCase__		:				Dict    =  textwrap.dedent(
          F"\\n        image\n        {image_file}\n        "						)
      with open(_snake_case    ,					"w"						) as f:
            f.write(_snake_case						)
      return str(_snake_case						)

@pytest.fixture
def       lowercase_		(		_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  tmp_path / "csv_with_label.csv"
      lowerCamelCase__		:				Tuple    =  textwrap.dedent(
          "\\n        label\n        good\n        bad\n        good\n        "						)
      with open(_snake_case    ,					"w"						) as f:
            f.write(_snake_case						)
      return str(_snake_case						)

@pytest.fixture
def       lowercase_		(		_A			:    Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:				int    =  tmp_path / "csv_with_int_list.csv"
      lowerCamelCase__		:				Dict    =  textwrap.dedent(
          "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "						)
      with open(_snake_case    ,					"w"						) as f:
            f.write(_snake_case						)
      return str(_snake_case						)

def       lowercase_		(		_A			:    Any    ,					_A			:    Any    ,					_A			:    Any						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  Csv()
      lowerCamelCase__		:				int    =  csv._generate_tables([[csv_file, malformed_csv_file]]						)
      with pytest.raises(_snake_case    ,					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(_snake_case						) in record.message
          for record in caplog.records						)

@require_pil
def       lowercase_		(		_A			:    Optional[int]						):

      """simple docstring"""




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

def       lowercase_		(		_A			:    Union[str, Any]						):

      """simple docstring"""




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


def       lowercase_		(		_A			:    str						):

      """simple docstring"""




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





from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






