



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments









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 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						int:  # picklable for multiprocessing
					return x.sum()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any	)  ->						Any:  # picklable for multiprocessing
					return i + 1






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42






class 						__a (     SCREAMING_SNAKE_CASE				):



	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =1
						__lowerCAmelCase	   =[1, 2]
						__lowerCAmelCase	   ={"""a""": 1, """b""": 2}
						__lowerCAmelCase	   ={"""a""": [1, 2], """b""": [3, 4]}
						__lowerCAmelCase	   ={"""a""": {"""1""": 1}, """b""": 2}
						__lowerCAmelCase	   ={"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =2
						__lowerCAmelCase	   =[2, 3]
						__lowerCAmelCase	   ={"""a""": 2, """b""": 3}
						__lowerCAmelCase	   ={"""a""": [2, 3], """b""": [4, 5]}
						__lowerCAmelCase	   ={"""a""": {"""1""": 2}, """b""": 3}
						__lowerCAmelCase	   ={"""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_)

						__lowerCAmelCase	   =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_)

						__lowerCAmelCase	   ={"""a""": np.eye(2), """b""": np.zeros(3), """c""": np.ones(2)}
						__lowerCAmelCase	   ={"""a""": 2, """b""": 0, """c""": 2}
						__lowerCAmelCase	   ={
						    """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 snake_case_: x + 1				,	snake_case_				,	num_proc=snake_case_)



	def 		UpperCamelCase							(						self							:    Any)->							int:
						__lowerCAmelCase	   ={"""a""": 1, """b""": 2}
						__lowerCAmelCase	   ={"""a""": 3, """b""": 4}
						__lowerCAmelCase	   ={"""a""": 5, """b""": 6}
						__lowerCAmelCase	   =sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))])
						self.assertEqual(sorted(zip_dict(snake_case_				,	snake_case_				,	snake_case_))				,	snake_case_)



	def 		UpperCamelCase							(						self							:    Optional[Any])->							Optional[int]:






						class 						__a :
							SCREAMING_SNAKE_CASE       =       "bar"


						__lowerCAmelCase	   =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 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    Dict	)  ->						Union[str, Any]:
					with patch("""datasets.utils.py_utils._single_map_nested"""	) as mock_single_map_nested, patch(
					    """datasets.parallel.parallel.Pool"""	) as mock_multiprocessing_pool:
										__lowerCAmelCase	   ={f"""{i}""": i for i in range(__lowerCamelCase	)}
										__lowerCAmelCase	   =map_nested(lambda __lowerCamelCase	: 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 						__a (     SCREAMING_SNAKE_CASE				):



	@require_tf
	def 		UpperCamelCase							(						self							:    Tuple)->							Optional[int]:
						import tensorflow as tf
						from tensorflow.keras import layers

						__lowerCAmelCase	   =layers.Dense(2)

						def gen_random_output():
											__lowerCAmelCase	   =tf.random.uniform((1, 3))
											return model(snake_case_).numpy()

						with temp_seed(42				,	set_tensorflow=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						with temp_seed(42				,	set_tensorflow=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						__lowerCAmelCase	   =gen_random_output()

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



	@require_torch
	def 		UpperCamelCase							(						self							:    Dict)->							Dict:
						import torch

						def gen_random_output():
											__lowerCAmelCase	   =torch.nn.Linear(3				,	2)
											__lowerCAmelCase	   =torch.rand(1				,	3)
											return model(snake_case_).detach().numpy()

						with temp_seed(42				,	set_pytorch=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						with temp_seed(42				,	set_pytorch=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						__lowerCAmelCase	   =gen_random_output()

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



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							List[Any]:
						def gen_random_output():
											return np.random.rand(1				,	3)

						with temp_seed(42):
											__lowerCAmelCase	   =gen_random_output()
						with temp_seed(42):
											__lowerCAmelCase	   =gen_random_output()
						__lowerCAmelCase	   =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 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						List[str]:
					__lowerCAmelCase	   =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 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    int	)  ->						List[str]:
					__lowerCAmelCase	   =NestedDataStructure(__lowerCamelCase	).flatten()
					assert output == expected_output






def 					__lowerCAmelCase		(  )  ->						int:
					__lowerCAmelCase	   =A(x=1				,   y="""foobar"""	)
					__lowerCAmelCase	   ={"""x""": 1, """y""": """foobar"""}
					assert asdict(__lowerCamelCase	) == expected_output

					__lowerCAmelCase	   ={"""a""": {"""b""": A(x=10				,   y="""foo"""	)}, """c""": [A(x=20				,   y="""bar"""	)]}
					__lowerCAmelCase	   ={"""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 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						Dict:
					return text.split()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					yield (time.time(), content)
					time.sleep(2	)
					yield (time.time(), content)






def 					__lowerCAmelCase		(  )  ->						Any:
					with Pool(2	) as pool:
										__lowerCAmelCase	   =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:
										__lowerCAmelCase	   =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:
										__lowerCAmelCase	   =[]
										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




