

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





def      A__					(			__A  :						Any			)					->List[Any]:  # picklable for multiprocessing
      return i + 1





@dataclass
class        lowerCAmelCase__  :



       '''simple docstring'''







       lowercase_						=    42
       lowercase_						=    42





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

             __A    =2
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )
             self.assertEqual(map_nested(lowercase__ ,   lowercase__ ,   num_proc=lowercase__     ) ,   lowercase__     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             class        lowerCAmelCase__  :



                    '''simple docstring'''







                    lowercase_						=    """bar"""

             __A    =Foo()
             self.assertEqual(foo.my_attr ,   '''bar'''     )
             with temporary_assignment(lowercase__ ,   '''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      A__					(			__A  :						int     ,				__A  :						List[Any]     ,				__A  :						Union[str, Any]			)					->str:
      with patch('''datasets.utils.py_utils._single_map_nested'''			) as mock_single_map_nested, patch(
          '''datasets.parallel.parallel.Pool'''			) as mock_multiprocessing_pool:
            __A    ={F'''{i}''': i for i in range(__A			)}
            __A    =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        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       @require_tf
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import tensorflow as tf
             from tensorflow.keras import layers

             __A    =layers.Dense(2     )

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

             with temp_seed(4_2 ,   set_tensorflow=lowercase__     ):
                   __A    =gen_random_output()
             with temp_seed(4_2 ,   set_tensorflow=lowercase__     ):
                   __A    =gen_random_output()
             __A    =gen_random_output()

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

       @require_torch
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import torch

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

             with temp_seed(4_2 ,   set_pytorch=lowercase__     ):
                   __A    =gen_random_output()
             with temp_seed(4_2 ,   set_pytorch=lowercase__     ):
                   __A    =gen_random_output()
             __A    =gen_random_output()

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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

             with temp_seed(4_2     ):
                   __A    =gen_random_output()
             with temp_seed(4_2     ):
                   __A    =gen_random_output()
             __A    =gen_random_output()

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





@pytest.mark.parametrize('''input_data'''     ,				[{}]			)
def      A__					(			__A  :						List[Any]			)					->Any:
      __A    =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      A__					(			__A  :						List[Any]     ,				__A  :						Tuple			)					->str:
      __A    =NestedDataStructure(__A			).flatten()
      assert output == expected_output





def      A__					(			)					->Optional[int]:
      __A    =A(x=1     ,				y='''foobar'''			)
      __A    ={'''x''': 1, '''y''': '''foobar'''}
      assert asdict(__A			) == expected_output

      __A    ={'''a''': {'''b''': A(x=10     ,				y='''foo'''			)}, '''c''': [A(x=20     ,				y='''bar'''			)]}
      __A    ={'''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      A__					(			__A  :						str			)					->Optional[Any]:
      return text.split()





def      A__					(			__A  :						int			)					->Optional[Any]:
      yield (time.time(), content)
      time.sleep(2			)
      yield (time.time(), content)





def      A__					(			)					->List[str]:
      with Pool(2			) as pool:
            __A    =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:
            __A    =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:
            __A    =[]
            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





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCamelCase      :					Union[str, Any]					    =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCamelCase      :					str					    =      '''    \"""
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"""

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''





class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =tempfile.mkdtemp()
             os.makedirs(os.path.join(self.diffusers_dir ,   '''schedulers/'''     )     )
             __A    =self.diffusers_dir
             shutil.copy(
                 os.path.join(lowercase__ ,   '''src/diffusers/schedulers/scheduling_ddpm.py'''     ) ,   os.path.join(self.diffusers_dir ,   '''schedulers/scheduling_ddpm.py'''     ) ,   )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''src/diffusers'''
             shutil.rmtree(self.diffusers_dir     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=None     ):







             '''simple docstring'''



             __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
             if overwrite_result is not None:
                   __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
             __A    =black.Mode(target_versions={black.TargetVersion.PYaa} ,   line_length=1_1_9     )
             __A    =black.format_str(lowercase__ ,   mode=lowercase__     )
             __A    =os.path.join(self.diffusers_dir ,   '''new_code.py'''     )
             with open(lowercase__ ,   '''w''' ,   newline='''\n'''     ) as f:
                   f.write(lowercase__     )
             if overwrite_result is None:
                   self.assertTrue(len(check_copies.is_copy_consistent(lowercase__     )     ) == 0     )
             else:
                   check_copies.is_copy_consistent(f.name ,   overwrite=lowercase__     )
                   with open(lowercase__ ,   '''r'''     ) as f:
                         self.assertTrue(f.read() ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     )
             self.assertEqual(lowercase__ ,   lowercase__     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   REFERENCE_CODE + '''\n''' ,   )

             # With no empty line at the end
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   lowercase__ ,   )

             # Copy consistency with rename
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )

             # Copy consistency with a really long name
             __A    ='''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
             self.check_copy_consistency(
                 f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' ,   f'''{long_class_name}SchedulerOutput''' ,   re.sub('''Bert''' ,   lowercase__ ,   lowercase__     ) ,   )

             # Copy consistency with overwrite
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   lowercase__ ,   overwrite_result=re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )


