

import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([1, 2, 3]						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises(lowerCamelCase__						):
							UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([1, 2, 3]						)				,      type=pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises(lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= pa.array(TypedSequence([1, 2, 3]				,      try_type=Value("bool"						)				,      type=Value("int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= pa.array(TypedSequence([1, 2, 3]				,      type=Value("int32"						)						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
							UpperCAmelCase__:	List[str]									= pa.array(TypedSequence(["foo", "bar"]				,      type=Value("int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= pa.array(TypedSequence([1, 2, 3]				,      try_type=Value("int32"						)						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= pa.array(TypedSequence(["foo", "bar"]				,      try_type=Value("int64"						)						)						)
						self.assertEqual(arr.type				,      pa.string()						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([[[1, 2, 3]]]				,      type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      ArrayaDExtensionType((1, 3)				,      "int64"						)						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
							UpperCAmelCase__:	Union[str, Any]									= pa.array(TypedSequence(["foo", "bar"]				,      type=ArrayaD((1, 3)				,      "int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= pa.array(TypedSequence([[[1, 2, 3]]]				,      try_type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      ArrayaDExtensionType((1, 3)				,      "int64"						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= pa.array(TypedSequence(["foo", "bar"]				,      try_type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      pa.string()						)





					@require_pil
					def    _UpperCAmelCase    (				self						):
						import PIL.Image

						UpperCAmelCase__:	int									= PIL.Image.fromarray(np.arange(1_0				,      dtype=np.uinta						).reshape(2				,      5						)						)
						with patch(
						    "datasets.arrow_writer.cast_to_python_objects"				,      side_effect=lowerCamelCase__						) as mock_cast_to_python_objects:
							UpperCAmelCase__:	Tuple									= pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]				,      type=Image()						)						)
							UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= mock_cast_to_python_objects.call_args_list[-1]
							self.assertIn("optimize_list_casting"				,      lowerCamelCase__						)
							self.assertFalse(kwargs["optimize_list_casting"]						)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= pa.BufferReader(SCREAMING_SNAKE_CASE      ) if isinstance(SCREAMING_SNAKE_CASE	,pa.Buffer      ) else pa.memory_map(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	str									= pa.ipc.open_stream(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	pa.Table									= f.read_all()
	assert len(pa_table.to_batches()      ) == expected_num_chunks
	assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
	del pa_table



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	UpperCAmelCase__:	Tuple									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Optional[int]									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



def 			_A	(      ):
	UpperCAmelCase__:	Tuple									= pa.BufferOutputStream()
	UpperCAmelCase__:	Any									= Features({"labels": ClassLabel(names=["neg", "pos"]      )}      )
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,features=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"labels": 0}      )
		writer.write({"labels": 1}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	assert writer._schema == features.arrow_schema
	assert writer._schema.metadata == features.arrow_schema.metadata
	UpperCAmelCase__:	Dict									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	Any									= pa.ipc.open_stream(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	pa.Table									= f.read_all()
	UpperCAmelCase__:	Union[str, Any]									= pa_table.schema
	assert pa_table.num_rows == 2
	assert schema == features.arrow_schema
	assert schema.metadata == features.arrow_schema.metadata
	assert features == Features.from_arrow_schema(SCREAMING_SNAKE_CASE      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		with pytest.raises(SCREAMING_SNAKE_CASE      ):
			writer.write({"col_1": "foo", "col_2": 1}	,key=[1, 2]      )
			UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()



@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		with pytest.raises(SCREAMING_SNAKE_CASE      ):
			writer.write({"col_1": "foo", "col_2": 1}	,key=1_0      )
			writer.write({"col_1": "bar", "col_2": 2}	,key=1_0      )
			UpperCAmelCase__							,						UpperCAmelCase__:	str									= writer.finalize()



@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Dict									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		writer.write({"col_1": "foo", "col_2": 1}	,key=1      )
		writer.write({"col_1": "bar", "col_2": 2}	,key=2      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= pa.BufferOutputStream()
	UpperCAmelCase__:	Optional[Any]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )
		writer.write_batch({"col_1": [], "col_2": []}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Optional[Any]									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= pa.BufferOutputStream()
	UpperCAmelCase__:	List[str]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	str									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	UpperCAmelCase__:	List[Any]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}      )      )
		writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}      )      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Dict									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



def 			_A	(      ):
	with tempfile.TemporaryDirectory() as tmp_dir:
		UpperCAmelCase__:	List[str]									= {"col_1": pa.string(), "col_2": pa.intaa()}
		UpperCAmelCase__:	str									= os.path.join(SCREAMING_SNAKE_CASE	,"test.arrow"      )
		with ArrowWriter(path=SCREAMING_SNAKE_CASE	,schema=pa.schema(SCREAMING_SNAKE_CASE      )      ) as writer:
			writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )
			UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
		_check_output(SCREAMING_SNAKE_CASE	,1      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	if pa.types.is_list(SCREAMING_SNAKE_CASE      ):
		return get_base_dtype(arr_type.value_type      )
	else:
		return arr_type



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if isinstance(lst[0]	,SCREAMING_SNAKE_CASE      ):
		change_first_primitive_element_in_list(lst[0]	,SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	Dict									= value



@pytest.mark.parametrize("optimized_int_type, expected_dtype"	,[(None, pa.intaa()), (Value("int32"      ), pa.intaa())]      )
@pytest.mark.parametrize("sequence"	,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= pa.array(TypedSequence(SCREAMING_SNAKE_CASE	,optimized_int_type=SCREAMING_SNAKE_CASE      )      )
	assert get_base_dtype(arr.type      ) == expected_dtype



@pytest.mark.parametrize(
    "col, expected_dtype"	,[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]	,)
@pytest.mark.parametrize("sequence"	,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	# in range
	UpperCAmelCase__:	List[Any]									= pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE	,col=SCREAMING_SNAKE_CASE      )      )
	assert get_base_dtype(arr.type      ) == expected_dtype

	# not in range
	if col != "other":
		# avoids errors due to in-place modifications
		UpperCAmelCase__:	Dict									= copy.deepcopy(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	List[Any]									= np.iinfo(expected_dtype.to_pandas_dtype()      ).max + 1
		change_first_primitive_element_in_list(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Tuple									= pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE	,col=SCREAMING_SNAKE_CASE      )      )
		assert get_base_dtype(arr.type      ) == pa.intaa()



@pytest.mark.parametrize("raise_exception"	,[False, True]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= str(tmp_path / "dataset-train.arrow"      )
	try:
		with ArrowWriter(path=SCREAMING_SNAKE_CASE      ) as writer:
			if raise_exception:
				raise pa.lib.ArrowInvalid()
			else:
				writer.stream.close()
	except pa.lib.ArrowInvalid:
		pass
	finally:
		assert writer.stream.closed



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= "mock://dataset-train.arrow"
	with ArrowWriter(path=SCREAMING_SNAKE_CASE	,storage_options=mockfs.storage_options      ) as writer:
		assert isinstance(writer._fs	,type(SCREAMING_SNAKE_CASE      )      )
		assert writer._fs.storage_options == mockfs.storage_options
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	assert mockfs.exists(SCREAMING_SNAKE_CASE      )



def 			_A	(      ):
	UpperCAmelCase__:	Dict									= pa.BufferOutputStream()
	with ParquetWriter(stream=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	str									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	UpperCAmelCase__:	str									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	pa.Table									= pq.read_table(SCREAMING_SNAKE_CASE      )
	assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}



@require_pil
@pytest.mark.parametrize("embed_local_files"	,[False, True]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	import PIL.Image

	UpperCAmelCase__:	List[str]									= str(tmp_path / "test_image_rgb.jpg"      )
	PIL.Image.fromarray(np.zeros((5, 5)	,dtype=np.uinta      )      ).save(SCREAMING_SNAKE_CASE	,format="png"      )
	UpperCAmelCase__:	List[Any]									= pa.BufferOutputStream()
	with ParquetWriter(
	    stream=SCREAMING_SNAKE_CASE	,features=Features({"image": Image()}      )	,embed_local_files=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"image": image_path}      )
		writer.finalize()
	UpperCAmelCase__:	Optional[int]									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	pa.Table									= pq.read_table(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= pa_table.to_pydict()
	if embed_local_files:
		assert isinstance(out["image"][0]["path"]	,SCREAMING_SNAKE_CASE      )
		with open(SCREAMING_SNAKE_CASE	,"rb"      ) as f:
			assert out["image"][0]["bytes"] == f.read()
	else:
		assert out["image"][0]["path"] == image_path
		assert out["image"][0]["bytes"] is None



def 			_A	(      ):
	UpperCAmelCase__:	List[str]									= pa.schema([pa.field("col_1"	,pa.string()	,nullable=SCREAMING_SNAKE_CASE      )]      )
	UpperCAmelCase__:	Optional[Any]									= pa.BufferOutputStream()
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE      ) as writer:
		writer._build_writer(inferred_schema=SCREAMING_SNAKE_CASE      )
		assert writer._schema == pa.schema([pa.field("col_1"	,pa.string()      )]      )


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()