
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 						lowercase_      (       A__		)							->							List[Any]:  # picklable for multiprocessing
    """simple docstring"""

    return x.sum()



def 						lowercase_      (       A__		)							->							List[str]:  # picklable for multiprocessing
    """simple docstring"""

    return i + 1







@dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							int
     UpperCAmelCase__    :							str







class 					lowerCamelCase		(  a__  ):





     def 		UpperCAmelCase(self				:   List[Any]       )						->						int:
         snake_case								= {}
         snake_case								= []
         snake_case								= 1
         snake_case								= [1, 2]
         snake_case								= {'''a''': 1, '''b''': 2}
         snake_case								= {'''a''': [1, 2], '''b''': [3, 4]}
         snake_case								= {'''a''': {'''1''': 1}, '''b''': 2}
         snake_case								= {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
         snake_case								= {}
         snake_case								= []
         snake_case								= 2
         snake_case								= [2, 3]
         snake_case								= {'''a''': 2, '''b''': 3}
         snake_case								= {'''a''': [2, 3], '''b''': [4, 5]}
         snake_case								= {'''a''': {'''1''': 2}, '''b''': 3}
         snake_case								= {'''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       )

         snake_case								= 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       )

         snake_case								= {'''a''': np.eye(2       ), '''b''': np.zeros(3       ), '''c''': np.ones(2       )}
         snake_case								= {'''a''': 2, '''b''': 0, '''c''': 2}
         snake_case								= {
             '''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 _A       : x + 1    ,   _lowerCamelCase    ,   num_proc=_lowerCamelCase       )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						List[Any]:
         snake_case								= {'''a''': 1, '''b''': 2}
         snake_case								= {'''a''': 3, '''b''': 4}
         snake_case								= {'''a''': 5, '''b''': 6}
         snake_case								= sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]       )
         self.assertEqual(sorted(zip_dict(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase       )       )    ,   _lowerCamelCase       )





     def 		UpperCAmelCase(self				:   Any       )						->						Dict:







         class 					lowerCamelCase		:
              UpperCAmelCase__    :							Optional[int]									=		'bar'


         snake_case								= 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 						lowercase_      (       A__			,		A__			,		A__		)							->							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:
        snake_case								= {F'{i}': i for i in range(a__		)}
        snake_case								= 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		(  a__  ):





     @require_tf
     def 		UpperCAmelCase(self				:   Tuple       )						->						Union[str, Any]:
         import tensorflow as tf
         from tensorflow.keras import layers

         snake_case								= layers.Dense(2       )

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

         with temp_seed(4_2    ,   set_tensorflow=_lowerCamelCase       ):
             snake_case								= gen_random_output()
         with temp_seed(4_2    ,   set_tensorflow=_lowerCamelCase       ):
             snake_case								= gen_random_output()
         snake_case								= gen_random_output()

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





     @require_torch
     def 		UpperCAmelCase(self				:   Tuple       )						->						str:
         import torch

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

         with temp_seed(4_2    ,   set_pytorch=_lowerCamelCase       ):
             snake_case								= gen_random_output()
         with temp_seed(4_2    ,   set_pytorch=_lowerCamelCase       ):
             snake_case								= gen_random_output()
         snake_case								= gen_random_output()

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





     def 		UpperCAmelCase(self				:   Tuple       )						->						Union[str, Any]:
         def gen_random_output():
             return np.random.rand(1    ,   3       )

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

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



@pytest.mark.parametrize("input_data"			,		[{}]		)
def 						lowercase_      (       A__		)							->							Optional[Any]:
    """simple docstring"""

    snake_case								= 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 						lowercase_      (       A__			,		A__		)							->							Optional[Any]:
    """simple docstring"""

    snake_case								= NestedDataStructure(a__		).flatten()
    assert output == expected_output



def 						lowercase_      (       )							->							List[str]:
    """simple docstring"""

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

    snake_case								= {'''a''': {'''b''': A(x=10			,		y="foo"		)}, '''c''': [A(x=20			,		y="bar"		)]}
    snake_case								= {'''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 						lowercase_      (       A__		)							->							Dict:
    """simple docstring"""

    return text.split()



def 						lowercase_      (       A__		)							->							Tuple:
    """simple docstring"""

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





def 						lowercase_      (       )							->							Tuple:
    """simple docstring"""

    with Pool(2		) as pool:
        snake_case								= 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:
        snake_case								= 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:
        snake_case								= []
        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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

   _A   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

