





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__			(__lowerCamelCase	):  # picklable for multiprocessing
     return x.sum()






def     lowerCamelCase__			(__lowerCamelCase	):  # picklable for multiprocessing
     return i + 1





@dataclass
class 			lowerCAmelCase__:






       '''simple docstring'''


       __snake_case 			=    42
       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							{}
            _SCREAMING_SNAKE_CASE       :	str						    =							[]
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							1
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[1, 2]
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							{"a": 1, "b": 2}
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							{"a": [1, 2], "b": [3, 4]}
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							{"a": {"1": 1}, "b": 2}
            _SCREAMING_SNAKE_CASE       :	str						    =							{"a": 1, "b": 2, "c": 3, "d": 4}
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							{}
            _SCREAMING_SNAKE_CASE       :	str						    =							[]
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							2
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[2, 3]
            _SCREAMING_SNAKE_CASE       :	int						    =							{"a": 2, "b": 3}
            _SCREAMING_SNAKE_CASE       :	int						    =							{"a": [2, 3], "b": [4, 5]}
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							{"a": {"1": 2}, "b": 3}
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							{"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							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							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							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							{"a": np.eye(2							), "b": np.zeros(3							), "c": np.ones(2							)}
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							{"a": 2, "b": 0, "c": 2}
            _SCREAMING_SNAKE_CASE       :	Dict						    =							{
                "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     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Any						    =							{"a": 1, "b": 2}
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							{"a": 3, "b": 4}
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							{"a": 5, "b": 6}
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]							)
            self.assertEqual(sorted(zip_dict(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)							)    ,						__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     str:





            class 			lowerCAmelCase__:






                   '''simple docstring'''


                   __snake_case 			=    'bar'
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     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       :	Tuple						    =							{f"""{i}""": i for i in range(__lowerCamelCase	)}
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							map_nested(lambda __lowerCamelCase	: x + 10,       __lowerCamelCase,       num_proc=__lowerCamelCase,       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__( __lowercase    ):






       '''simple docstring'''


       @require_tf
       def     UpperCamelCase_			(      self							)			->     int:
            import tensorflow as tf
            from tensorflow.keras import layers

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							layers.Dense(2							)

            def gen_random_output():
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tf.random.uniform((1, 3)							)
                 return model(__lowerCamelCase							).numpy()

            with temp_seed(4_2    ,						set_tensorflow=__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							gen_random_output()
            with temp_seed(4_2    ,						set_tensorflow=__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							gen_random_output()
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							gen_random_output()

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




       @require_torch
       def     UpperCamelCase_			(      self							)			->     Any:
            import torch

            def gen_random_output():
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.nn.Linear(3    ,						2							)
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							torch.rand(1    ,						3							)
                 return model(__lowerCamelCase							).detach().numpy()

            with temp_seed(4_2    ,						set_pytorch=__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	str						    =							gen_random_output()
            with temp_seed(4_2    ,						set_pytorch=__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							gen_random_output()
            _SCREAMING_SNAKE_CASE       :	Any						    =							gen_random_output()

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







       def     UpperCamelCase_			(      self							)			->     List[str]:
            def gen_random_output():
                 return np.random.rand(1    ,						3							)

            with temp_seed(4_2							):
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							gen_random_output()
            with temp_seed(4_2							):
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							gen_random_output()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							gen_random_output()

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






@pytest.mark.parametrize("input_data",       [{}]	)
def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							NestedDataStructure(__lowerCamelCase	).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__			(__lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							NestedDataStructure(__lowerCamelCase	).flatten()
     assert output == expected_output






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							A(x=1,       y="foobar"	)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							{"x": 1, "y": "foobar"}
     assert asdict(__lowerCamelCase	) == expected_output

     _SCREAMING_SNAKE_CASE       :	Dict						    =							{"a": {"b": A(x=10,       y="foo"	)}, "c": [A(x=20,       y="bar"	)]}
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							{"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
     assert asdict(__lowerCamelCase	) == expected_output

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






def     lowerCamelCase__			(__lowerCamelCase	):
     return text.split()






def     lowerCamelCase__			(__lowerCamelCase	):
     yield (time.time(), content)
     time.sleep(2	)
     yield (time.time(), content)






def     lowerCamelCase__			():
     with Pool(2	) as pool:
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							list(iflatmap_unordered(__lowerCamelCase,       _split_text,       kwargs_iterable=[{"text": "hello there"}] * 10	)	)
          assert out.count("hello"	) == 10
          assert out.count("there"	) == 10
          assert len(__lowerCamelCase	) == 20

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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

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