"""simple docstring"""






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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:  # picklable for multiprocessing
				return i + 1

@dataclass
class lowerCamelCase		:






					'''simple docstring'''


					a   	=		42
					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				{}
									SCREAMING_SNAKE_CASE__		  =				[]
									SCREAMING_SNAKE_CASE__		  =				1
									SCREAMING_SNAKE_CASE__		  =				[1, 2]
									SCREAMING_SNAKE_CASE__		  =				{"a": 1, "b": 2}
									SCREAMING_SNAKE_CASE__		  =				{"a": [1, 2], "b": [3, 4]}
									SCREAMING_SNAKE_CASE__		  =				{"a": {"1": 1}, "b": 2}
									SCREAMING_SNAKE_CASE__		  =				{"a": 1, "b": 2, "c": 3, "d": 4}
									SCREAMING_SNAKE_CASE__		  =				{}
									SCREAMING_SNAKE_CASE__		  =				[]
									SCREAMING_SNAKE_CASE__		  =				2
									SCREAMING_SNAKE_CASE__		  =				[2, 3]
									SCREAMING_SNAKE_CASE__		  =				{"a": 2, "b": 3}
									SCREAMING_SNAKE_CASE__		  =				{"a": [2, 3], "b": [4, 5]}
									SCREAMING_SNAKE_CASE__		  =				{"a": {"1": 2}, "b": 3}
									SCREAMING_SNAKE_CASE__		  =				{"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		)

									SCREAMING_SNAKE_CASE__		  =				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		)

									SCREAMING_SNAKE_CASE__		  =				{"a": np.eye(2		), "b": np.zeros(3		), "c": np.ones(2		)}
									SCREAMING_SNAKE_CASE__		  =				{"a": 2, "b": 0, "c": 2}
									SCREAMING_SNAKE_CASE__		  =				{
									    "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 			lowerCAmelCase_					(						self			:				Tuple		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				{"a": 1, "b": 2}
									SCREAMING_SNAKE_CASE__		  =				{"a": 3, "b": 4}
									SCREAMING_SNAKE_CASE__		  =				{"a": 5, "b": 6}
									SCREAMING_SNAKE_CASE__		  =				sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]		)
									self.assertEqual(sorted(zip_dict(_snake_case   , _snake_case   , _snake_case		)		)   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       str:

									class lowerCamelCase		:






														'''simple docstring'''


														a   	=		"bar"



									SCREAMING_SNAKE_CASE__		  =				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					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   str:
				with patch("datasets.utils.py_utils._single_map_nested"       ) as mock_single_map_nested, patch(
				    "datasets.parallel.parallel.Pool"       ) as mock_multiprocessing_pool:
								SCREAMING_SNAKE_CASE__		  =				{F"""{i}""": i for i in range(__UpperCAmelCase       )}
								SCREAMING_SNAKE_CASE__		  =				map_nested(lambda __UpperCAmelCase       : x + 10  ,					__UpperCAmelCase  ,					num_proc=__UpperCAmelCase  ,					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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


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

									SCREAMING_SNAKE_CASE__		  =				layers.Dense(2		)

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

									with temp_seed(42   , set_tensorflow=_snake_case		):
													SCREAMING_SNAKE_CASE__		  =				gen_random_output()
									with temp_seed(42   , set_tensorflow=_snake_case		):
													SCREAMING_SNAKE_CASE__		  =				gen_random_output()
									SCREAMING_SNAKE_CASE__		  =				gen_random_output()

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



					@require_torch
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       int:
									import torch

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

									with temp_seed(42   , set_pytorch=_snake_case		):
													SCREAMING_SNAKE_CASE__		  =				gen_random_output()
									with temp_seed(42   , set_pytorch=_snake_case		):
													SCREAMING_SNAKE_CASE__		  =				gen_random_output()
									SCREAMING_SNAKE_CASE__		  =				gen_random_output()

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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Dict:
									def gen_random_output():
													return np.random.rand(1   , 3		)

									with temp_seed(42		):
													SCREAMING_SNAKE_CASE__		  =				gen_random_output()
									with temp_seed(42		):
													SCREAMING_SNAKE_CASE__		  =				gen_random_output()
									SCREAMING_SNAKE_CASE__		  =				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					(  __UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				NestedDataStructure(__UpperCAmelCase       ).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					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				NestedDataStructure(__UpperCAmelCase       ).flatten()
				assert output == expected_output




def 						SCREAMING_SNAKE_CASE					(  )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				A(x=1  ,					y="foobar"       )
				SCREAMING_SNAKE_CASE__		  =				{"x": 1, "y": "foobar"}
				assert asdict(__UpperCAmelCase       ) == expected_output

				SCREAMING_SNAKE_CASE__		  =				{"a": {"b": A(x=10  ,					y="foo"       )}, "c": [A(x=20  ,					y="bar"       )]}
				SCREAMING_SNAKE_CASE__		  =				{"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
				assert asdict(__UpperCAmelCase       ) == expected_output

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[Any]:
				return text.split()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
				yield (time.time(), content)
				time.sleep(2       )
				yield (time.time(), content)




def 						SCREAMING_SNAKE_CASE					(  )							->   Tuple:
				with Pool(2       ) as pool:
								SCREAMING_SNAKE_CASE__		  =				list(iflatmap_unordered(__UpperCAmelCase  ,					_split_text  ,					kwargs_iterable=[{"text": "hello there"}] * 10       )       )
								assert out.count("hello"       ) == 10
								assert out.count("there"       ) == 10
								assert len(__UpperCAmelCase       ) == 20

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

				# check that we get items as fast as possible
				with Pool(2       ) as pool:
								SCREAMING_SNAKE_CASE__		  =				[]
								for yield_time, content in iflatmap_unordered(
								    __UpperCAmelCase  ,					_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(__UpperCAmelCase       )
								assert out.count("a"       ) == 2
								assert out.count("b"       ) == 2
								assert len(__UpperCAmelCase       ) == 4





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




