

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__	:   Dict     )    ->      Optional[Any]:  # picklable for multiprocessing





      """simple docstring"""



      return x.sum()
def      a      (			A__	:   List[Any]     )    ->      Optional[int]:  # picklable for multiprocessing





      """simple docstring"""



      return i + 1





@dataclass
class  __lowerCAmelCase     :
  _a									=  42
  _a									=  42





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase	       ={}
        _lowercase	       =[]
        _lowercase	       =1
        _lowercase	       =[1, 2]
        _lowercase	       ={'a': 1, 'b': 2}
        _lowercase	       ={'a': [1, 2], 'b': [3, 4]}
        _lowercase	       ={'a': {'1': 1}, 'b': 2}
        _lowercase	       ={'a': 1, 'b': 2, 'c': 3, 'd': 4}
        _lowercase	       ={}
        _lowercase	       =[]
        _lowercase	       =2
        _lowercase	       =[2, 3]
        _lowercase	       ={'a': 2, 'b': 3}
        _lowercase	       ={'a': [2, 3], 'b': [4, 5]}
        _lowercase	       ={'a': {'1': 2}, 'b': 3}
        _lowercase	       ={'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					)

        _lowercase	       =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					)

        _lowercase	       ={'a': np.eye(2					), 'b': np.zeros(3					), 'c': np.ones(2					)}
        _lowercase	       ={'a': 2, 'b': 0, 'c': 2}
        _lowercase	       ={
            '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					)  -> Optional[int]:
        '''simple docstring'''






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




  def   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        class  __lowerCAmelCase     :
          _a									=  """bar"""
        _lowercase	       =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      a      (			A__	:   Optional[Any]      ,   A__	:   List[Any]      ,   A__	:   Optional[int]     )    ->      Union[str, 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:
            _lowercase	       ={F'''{i}''': i for i in range(A__     )}
            _lowercase	       =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     (					SCREAMING_SNAKE_CASE							):




  @require_tf
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        import tensorflow as tf
        from tensorflow.keras import layers

        _lowercase	       =layers.Dense(2					)

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

        with temp_seed(42							,   set_tensorflow=lowerCAmelCase					):
              _lowercase	       =gen_random_output()
        with temp_seed(42							,   set_tensorflow=lowerCAmelCase					):
              _lowercase	       =gen_random_output()
        _lowercase	       =gen_random_output()

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




  @require_torch
  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        import torch

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

        with temp_seed(42							,   set_pytorch=lowerCAmelCase					):
              _lowercase	       =gen_random_output()
        with temp_seed(42							,   set_pytorch=lowerCAmelCase					):
              _lowercase	       =gen_random_output()
        _lowercase	       =gen_random_output()

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




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






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

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

        np.testing.assert_equal(lowerCAmelCase							,   lowerCAmelCase					)
        self.assertGreater(np.abs(outa - outa					).sum()							,   0					)
@pytest.mark.parametrize('input_data'      ,   [{}]     )
def      a      (			A__	:   Union[str, Any]     )    ->      List[str]:





      """simple docstring"""



      _lowercase	       =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__	:   Dict      ,   A__	:   str     )    ->      List[str]:





      """simple docstring"""



      _lowercase	       =NestedDataStructure(A__     ).flatten()
      assert output == expected_output
def      a      (			)    ->      Optional[Any]:





      """simple docstring"""



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

      _lowercase	       ={'a': {'b': A(x=10      ,   y='foo'     )}, 'c': [A(x=20      ,   y='bar'     )]}
      _lowercase	       ={'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     )    ->      Union[str, Any]:





      """simple docstring"""



      return text.split()
def      a      (			A__	:   Optional[int]     )    ->      Any:





      """simple docstring"""



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







def      a      (			)    ->      int:





      """simple docstring"""



      with Pool(2     ) as pool:
            _lowercase	       =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:
            _lowercase	       =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:
            _lowercase	       =[]
            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
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


if __name__ == "__main__":
  print(f"{solution() = }")


