






import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any					)		->			str:  # picklable for multiprocessing






	"""simple docstring"""



	return x.sum()

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]					)		->			Tuple:  # picklable for multiprocessing






	"""simple docstring"""



	return i + 1

@dataclass
class     _SCREAMING_SNAKE_CASE       :
	snake_case__							:     Dict     	=  4_2
	snake_case__							:     Dict     	=  4_2

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def 						_A (		self				:					str	):
		UpperCamelCase     :int          =	{}
		UpperCamelCase     :Tuple          =	[]
		UpperCamelCase     :Any          =	1
		UpperCamelCase     :str          =	[1, 2]
		UpperCamelCase     :List[str]          =	{"""a""": 1, """b""": 2}
		UpperCamelCase     :List[str]          =	{"""a""": [1, 2], """b""": [3, 4]}
		UpperCamelCase     :Dict          =	{"""a""": {"""1""": 1}, """b""": 2}
		UpperCamelCase     :List[Any]          =	{"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
		UpperCamelCase     :List[str]          =	{}
		UpperCamelCase     :List[str]          =	[]
		UpperCamelCase     :str          =	2
		UpperCamelCase     :Dict          =	[2, 3]
		UpperCamelCase     :List[Any]          =	{"""a""": 2, """b""": 3}
		UpperCamelCase     :Dict          =	{"""a""": [2, 3], """b""": [4, 5]}
		UpperCamelCase     :List[str]          =	{"""a""": {"""1""": 2}, """b""": 3}
		UpperCamelCase     :List[Any]          =	{"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_	) ,  snake_case_	)

		UpperCamelCase     :List[str]          =	2
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)

		UpperCamelCase     :Tuple          =	{"""a""": np.eye(2	), """b""": np.zeros(3	), """c""": np.ones(2	)}
		UpperCamelCase     :Union[str, Any]          =	{"""a""": 2, """b""": 0, """c""": 2}
		UpperCamelCase     :Optional[Any]          =	{
		    """a""": np.eye(2	).astype(snake_case_	),
		    """b""": np.zeros(3	).astype(snake_case_	),
		    """c""": np.ones(2	).astype(snake_case_	),
		}
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  map_numpy=snake_case_	) ,  snake_case_	)
		self.assertEqual(
		    {k: v.tolist() for k, v in map_nested(snake_case_ ,  snake_case_ ,  map_numpy=snake_case_	).items()} ,  {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,  )
		self.assertEqual(map_nested(snake_case_ ,  snake_case_ ,  map_numpy=snake_case_ ,  num_proc=snake_case_	) ,  snake_case_	)
		self.assertEqual(
		    {k: v.tolist() for k, v in map_nested(snake_case_ ,  snake_case_ ,  map_numpy=snake_case_ ,  num_proc=snake_case_	).items()} ,  {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,  )
		with self.assertRaises(snake_case_	):  # can't pickle a local lambda
			map_nested(lambda __lowerCamelCase	: x + 1 ,  snake_case_ ,  num_proc=snake_case_	)





	def 						_A (		self				:					int	):
		UpperCamelCase     :Tuple          =	{"""a""": 1, """b""": 2}
		UpperCamelCase     :Dict          =	{"""a""": 3, """b""": 4}
		UpperCamelCase     :List[str]          =	{"""a""": 5, """b""": 6}
		UpperCamelCase     :Tuple          =	sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]	)
		self.assertEqual(sorted(zip_dict(snake_case_ ,  snake_case_ ,  snake_case_	)	) ,  snake_case_	)





	def 						_A (		self				:					int	):

		class     _SCREAMING_SNAKE_CASE       :
			snake_case__							:     str     	=  """bar"""
		UpperCamelCase     :Tuple          =	Foo()
		self.assertEqual(foo.my_attr ,  """bar"""	)
		with temporary_assignment(snake_case_ ,  """my_attr""" ,  """BAR"""	):
			self.assertEqual(foo.my_attr ,  """BAR"""	)
		self.assertEqual(foo.my_attr ,  """bar"""	)

@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""	,			[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]	,			)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  Dict	,			__magic_name__					:  List[Any]					)		->			Optional[int]:






	"""simple docstring"""



	with patch("""datasets.utils.py_utils._single_map_nested"""					) as mock_single_map_nested, patch(
	    """datasets.parallel.parallel.Pool"""					) as mock_multiprocessing_pool:
		UpperCamelCase     :Union[str, Any]          =	{f"""{i}""": i for i in range(_lowerCAmelCase					)}
		UpperCamelCase     :int          =	map_nested(lambda __magic_name__					: x + 10	,			_lowerCAmelCase	,			num_proc=_lowerCAmelCase	,			parallel_min_length=16					)
		if expected_num_proc == 1:
			assert mock_single_map_nested.called
			assert not mock_multiprocessing_pool.called
		else:
			assert not mock_single_map_nested.called
			assert mock_multiprocessing_pool.called
			assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	@require_tf
	def 						_A (		self				:					Optional[int]	):
		import tensorflow as tf
		from tensorflow.keras import layers

		UpperCamelCase     :Tuple          =	layers.Dense(2	)

		def gen_random_output():
			UpperCamelCase     :Union[str, Any]          =	tf.random.uniform((1, 3)	)
			return model(snake_case_	).numpy()

		with temp_seed(42 ,  set_tensorflow=snake_case_	):
			UpperCamelCase     :List[Any]          =	gen_random_output()
		with temp_seed(42 ,  set_tensorflow=snake_case_	):
			UpperCamelCase     :List[str]          =	gen_random_output()
		UpperCamelCase     :Any          =	gen_random_output()

		np.testing.assert_equal(snake_case_ ,  snake_case_	)
		self.assertGreater(np.abs(outa - outa	).sum() ,  0	)





	@require_torch
	def 						_A (		self				:					Union[str, Any]	):
		import torch

		def gen_random_output():
			UpperCamelCase     :List[str]          =	torch.nn.Linear(3 ,  2	)
			UpperCamelCase     :Tuple          =	torch.rand(1 ,  3	)
			return model(snake_case_	).detach().numpy()

		with temp_seed(42 ,  set_pytorch=snake_case_	):
			UpperCamelCase     :List[str]          =	gen_random_output()
		with temp_seed(42 ,  set_pytorch=snake_case_	):
			UpperCamelCase     :List[str]          =	gen_random_output()
		UpperCamelCase     :List[str]          =	gen_random_output()

		np.testing.assert_equal(snake_case_ ,  snake_case_	)
		self.assertGreater(np.abs(outa - outa	).sum() ,  0	)





	def 						_A (		self				:					Optional[Any]	):
		def gen_random_output():
			return np.random.rand(1 ,  3	)

		with temp_seed(42	):
			UpperCamelCase     :Union[str, Any]          =	gen_random_output()
		with temp_seed(42	):
			UpperCamelCase     :List[str]          =	gen_random_output()
		UpperCamelCase     :Optional[int]          =	gen_random_output()

		np.testing.assert_equal(snake_case_ ,  snake_case_	)
		self.assertGreater(np.abs(outa - outa	).sum() ,  0	)

@pytest.mark.parametrize("""input_data"""	,			[{}]					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :Any          =	NestedDataStructure(_lowerCAmelCase					).data
	assert output_data == input_data

@pytest.mark.parametrize(
    """data, expected_output"""	,			[
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]	,			)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  Optional[int]					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	NestedDataStructure(_lowerCAmelCase					).flatten()
	assert output == expected_output

def    SCREAMING_SNAKE_CASE_							(							)		->			Any:






	"""simple docstring"""



	UpperCamelCase     :Optional[int]          =	A(x=1	,			y="""foobar"""					)
	UpperCamelCase     :Any          =	{"""x""": 1, """y""": """foobar"""}
	assert asdict(_lowerCAmelCase					) == expected_output

	UpperCamelCase     :str          =	{"""a""": {"""b""": A(x=10	,			y="""foo"""					)}, """c""": [A(x=20	,			y="""bar"""					)]}
	UpperCamelCase     :Optional[int]          =	{"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
	assert asdict(_lowerCAmelCase					) == expected_output

	with pytest.raises(_lowerCAmelCase					):
		asdict([1, A(x=10	,			y="""foo"""					)]					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]					)		->			Optional[int]:






	"""simple docstring"""



	return text.split()

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Tuple					)		->			Optional[int]:






	"""simple docstring"""



	yield (time.time(), content)
	time.sleep(2					)
	yield (time.time(), content)

def    SCREAMING_SNAKE_CASE_							(							)		->			Optional[Any]:






	"""simple docstring"""



	with Pool(2					) as pool:
		UpperCamelCase     :Tuple          =	list(iflatmap_unordered(_lowerCAmelCase	,			_split_text	,			kwargs_iterable=[{"""text""": """hello there"""}] * 10					)					)
		assert out.count("""hello"""					) == 10
		assert out.count("""there"""					) == 10
		assert len(_lowerCAmelCase					) == 20

	# check multiprocess from pathos (uses dill for pickling)
	with multiprocess.Pool(2					) as pool:
		UpperCamelCase     :Optional[Any]          =	list(iflatmap_unordered(_lowerCAmelCase	,			_split_text	,			kwargs_iterable=[{"""text""": """hello there"""}] * 10					)					)
		assert out.count("""hello"""					) == 10
		assert out.count("""there"""					) == 10
		assert len(_lowerCAmelCase					) == 20

	# check that we get items as fast as possible
	with Pool(2					) as pool:
		UpperCamelCase     :str          =	[]
		for yield_time, content in iflatmap_unordered(
		    _lowerCAmelCase	,			_aseconds_generator_of_aitems_with_timing	,			kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]					):
			assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
			out.append(_lowerCAmelCase					)
		assert out.count("""a"""					) == 2
		assert out.count("""b"""					) == 2
		assert len(_lowerCAmelCase					) == 4





'''simple docstring'''





from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		CustomTokenizer
    pass
