

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]	)				->    Union[str, Any]:
			_UpperCAmelCase    :						Optional[int]							=      tmp_path / '''file.csv'''
			_UpperCAmelCase    :						List[str]							=      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:					Union[str, Any]	)				->    int:
			_UpperCAmelCase    :						str							=      tmp_path / '''malformed_file.csv'''
			_UpperCAmelCase    :						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:					Union[str, Any]					, lowerCAmelCase:					Tuple	)				->    Tuple:
			_UpperCAmelCase    :						str							=      tmp_path / '''csv_with_image.csv'''
			_UpperCAmelCase    :						str							=      textwrap.dedent(
			    F'\\n        image\n        {image_file}\n        '	)
			with open(lowerCamelCase_					, "w"	) as f:
						f.write(lowerCamelCase_	)
			return str(lowerCamelCase_	)

@pytest.fixture
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]	)				->    str:
			_UpperCAmelCase    :						List[str]							=      tmp_path / '''csv_with_label.csv'''
			_UpperCAmelCase    :						Dict							=      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:					int	)				->    List[Any]:
			_UpperCAmelCase    :						Any							=      tmp_path / '''csv_with_int_list.csv'''
			_UpperCAmelCase    :						Tuple							=      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:					Any					, lowerCAmelCase:					List[Any]					, lowerCAmelCase:					Optional[Any]	)				->    int:
			_UpperCAmelCase    :						int							=      Csv()
			_UpperCAmelCase    :						Optional[int]							=      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:					Union[str, Any]	)				->    Tuple:

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    List[str]:

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





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





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


