
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 		_UpperCAmelCase   (	UpperCamelCase:     List[str]  ):  # picklable for multiprocessing






 """simple docstring"""


 return x.sum()






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]  ):  # picklable for multiprocessing






 """simple docstring"""


 return i + 1

@dataclass
class       a		:
       lowercase_		:						int
       lowercase_		:						str

class       a		(   a__					):







       def       UpperCAmelCase__					(						self  :  Optional[Any] ):
        """simple docstring"""







        __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(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_ )

        __lowerCAmelCase         =							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_ )

        __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(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 snake_case__ : x + 1      ,				lowerCamelCase_      ,				num_proc=lowerCamelCase_ )







       def       UpperCAmelCase__					(						self  :  Optional[int] ):
        """simple docstring"""







        __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(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_ ) )      ,				lowerCamelCase_ )







       def       UpperCAmelCase__					(						self  :  Dict ):
        """simple docstring"""







        class       a		:
               lowercase_		:						Optional[int]   							=  "bar"
        __lowerCAmelCase         =							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),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Tuple  ):






 """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:
  __lowerCAmelCase         =							{F"{i}": i for i in range(UpperCamelCase  )}
  __lowerCAmelCase         =							map_nested(lambda UpperCamelCase  : x + 1_0			,							UpperCamelCase			,							num_proc=UpperCamelCase			,							parallel_min_length=1_6  )
  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		(   a__					):







       @require_tf
       def       UpperCAmelCase__					(						self  :  List[str] ):
        """simple docstring"""







        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(lowerCamelCase_ ).numpy()

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

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







       @require_torch
       def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
        """simple docstring"""







        import torch

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

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

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







       def       UpperCAmelCase__					(						self  :  Tuple ):
        """simple docstring"""







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






@pytest.mark.parametrize("input_data"			,							[{}]  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Any  ):






 """simple docstring"""


 __lowerCAmelCase         =							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 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     Union[str, Any]  ):






 """simple docstring"""


 __lowerCAmelCase         =							NestedDataStructure(UpperCamelCase  ).flatten()
 assert output == expected_output






def 		_UpperCAmelCase   (	):






 """simple docstring"""


 __lowerCAmelCase         =							A(x=1			,							y="foobar"  )
 __lowerCAmelCase         =							{"x": 1, "y": "foobar"}
 assert asdict(UpperCamelCase  ) == expected_output

 __lowerCAmelCase         =							{"a": {"b": A(x=1_0			,							y="foo"  )}, "c": [A(x=2_0			,							y="bar"  )]}
 __lowerCAmelCase         =							{"a": {"b": {"x": 1_0, "y": "foo"}}, "c": [{"x": 2_0, "y": "bar"}]}
 assert asdict(UpperCamelCase  ) == expected_output

 with pytest.raises(UpperCamelCase  ):
  asdict([1, A(x=1_0			,							y="foo"  )]  )






def 		_UpperCAmelCase   (	UpperCamelCase:     Dict  ):






 """simple docstring"""


 return text.split()






def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]  ):






 """simple docstring"""


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







def 		_UpperCAmelCase   (	):






 """simple docstring"""


 with Pool(2  ) as pool:
  __lowerCAmelCase         =							list(iflatmap_unordered(UpperCamelCase			,							_split_text			,							kwargs_iterable=[{"text": "hello there"}] * 1_0  )  )
  assert out.count("hello"  ) == 1_0
  assert out.count("there"  ) == 1_0
  assert len(UpperCamelCase  ) == 2_0

 # check multiprocess from pathos (uses dill for pickling)
 with multiprocess.Pool(2  ) as pool:
  __lowerCAmelCase         =							list(iflatmap_unordered(UpperCamelCase			,							_split_text			,							kwargs_iterable=[{"text": "hello there"}] * 1_0  )  )
  assert out.count("hello"  ) == 1_0
  assert out.count("there"  ) == 1_0
  assert len(UpperCamelCase  ) == 2_0

 # check that we get items as fast as possible
 with Pool(2  ) as pool:
  __lowerCAmelCase         =							[]
  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








# 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,
)






