


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__				(	A       :      Any					):  # picklable for multiprocessing




						'''simple docstring'''






						return x.sum()


def 				lowerCamelCase__				(	A       :      Union[str, Any]					):  # picklable for multiprocessing




						'''simple docstring'''






						return i + 1






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				int  		=	42
				__magic_name__    :				Union[str, Any]  		=	42







class 	UpperCamelCase__(       lowerCAmelCase	):




				def 					a__(		self      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     {}
										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     [1, 2]
										UpperCAmelCase					    =     {'''a''': 1, '''b''': 2}
										UpperCAmelCase					    =     {'''a''': [1, 2], '''b''': [3, 4]}
										UpperCAmelCase					    =     {'''a''': {'''1''': 1}, '''b''': 2}
										UpperCAmelCase					    =     {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
										UpperCAmelCase					    =     {}
										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     [2, 3]
										UpperCAmelCase					    =     {'''a''': 2, '''b''': 3}
										UpperCAmelCase					    =     {'''a''': [2, 3], '''b''': [4, 5]}
										UpperCAmelCase					    =     {'''a''': {'''1''': 2}, '''b''': 3}
										UpperCAmelCase					    =     {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				)				,   lowerCamelCase_				)

										UpperCAmelCase					    =     2
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)

										UpperCAmelCase					    =     {'''a''': np.eye(2				), '''b''': np.zeros(3				), '''c''': np.ones(2				)}
										UpperCAmelCase					    =     {'''a''': 2, '''b''': 0, '''c''': 2}
										UpperCAmelCase					    =     {
										    '''a''': np.eye(2				).astype(lowerCamelCase_				),
										    '''b''': np.zeros(3				).astype(lowerCamelCase_				),
										    '''c''': np.ones(2				).astype(lowerCamelCase_				),
										}
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   map_numpy=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(
										    {k: v.tolist() for k, v in map_nested(lowerCamelCase_				,   lowerCamelCase_				,   map_numpy=lowerCamelCase_				).items()}				,   {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,   )
										self.assertEqual(map_nested(lowerCamelCase_				,   lowerCamelCase_				,   map_numpy=lowerCamelCase_				,   num_proc=lowerCamelCase_				)				,   lowerCamelCase_				)
										self.assertEqual(
										    {k: v.tolist() for k, v in map_nested(lowerCamelCase_				,   lowerCamelCase_				,   map_numpy=lowerCamelCase_				,   num_proc=lowerCamelCase_				).items()}				,   {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,   )
										with self.assertRaises(lowerCamelCase_				):  # can't pickle a local lambda
																map_nested(lambda lowerCAmelCase				: x + 1				,   lowerCamelCase_				,   num_proc=lowerCamelCase_				)




				def 					a__(		self      :			str				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     {'''a''': 1, '''b''': 2}
										UpperCAmelCase					    =     {'''a''': 3, '''b''': 4}
										UpperCAmelCase					    =     {'''a''': 5, '''b''': 6}
										UpperCAmelCase					    =     sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]				)
										self.assertEqual(sorted(zip_dict(lowerCamelCase_				,   lowerCamelCase_				,   lowerCamelCase_				)				)				,   lowerCamelCase_				)





				def 					a__(		self      :			Optional[Any]				)->					str:

										"""simple docstring"""




										class 	UpperCamelCase__:
														__magic_name__    :				int  		=	"bar"







										UpperCAmelCase					    =     Foo()
										self.assertEqual(foo.my_attr				,   '''bar'''				)
										with temporary_assignment(lowerCamelCase_				,   '''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__				(	A       :      str				,       A       :      Optional[Any]				,       A       :      List[Any]					):




						'''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					    =     {f"""{i}""": i for i in range(A					)}
												UpperCAmelCase					    =     map_nested(lambda A					: x + 10				,       A				,       num_proc=A				,       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 	UpperCamelCase__(       lowerCAmelCase	):




				@require_tf
				def 					a__(		self      :			Tuple				)->					Optional[Any]:

										"""simple docstring"""




										import tensorflow as tf
										from tensorflow.keras import layers

										UpperCAmelCase					    =     layers.Dense(2				)

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

										with temp_seed(42				,   set_tensorflow=lowerCamelCase_				):
																UpperCAmelCase					    =     gen_random_output()
										with temp_seed(42				,   set_tensorflow=lowerCamelCase_				):
																UpperCAmelCase					    =     gen_random_output()
										UpperCAmelCase					    =     gen_random_output()

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




				@require_torch
				def 					a__(		self      :			Dict				)->					Tuple:

										"""simple docstring"""




										import torch

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

										with temp_seed(42				,   set_pytorch=lowerCamelCase_				):
																UpperCAmelCase					    =     gen_random_output()
										with temp_seed(42				,   set_pytorch=lowerCamelCase_				):
																UpperCAmelCase					    =     gen_random_output()
										UpperCAmelCase					    =     gen_random_output()

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





				def 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




										def gen_random_output():
																return np.random.rand(1				,   3				)

										with temp_seed(42				):
																UpperCAmelCase					    =     gen_random_output()
										with temp_seed(42				):
																UpperCAmelCase					    =     gen_random_output()
										UpperCAmelCase					    =     gen_random_output()

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







@pytest.mark.parametrize('''input_data'''				,       [{}]					)
def 				lowerCamelCase__				(	A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     NestedDataStructure(A					).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__				(	A       :      Dict				,       A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     NestedDataStructure(A					).flatten()
						assert output == expected_output


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     A(x=1				,       y='''foobar'''					)
						UpperCAmelCase					    =     {'''x''': 1, '''y''': '''foobar'''}
						assert asdict(A					) == expected_output

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

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


def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






						return text.split()


def 				lowerCamelCase__				(	A       :      Tuple					):




						'''simple docstring'''






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




def 				lowerCamelCase__				(	):




						'''simple docstring'''






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

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

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









'''simple docstring'''



# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)





