






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		(					lowercase__  :  str  )		-> Any:  # picklable for multiprocessing
	return x.sum()




def 				A		(					lowercase__  :  Dict  )		-> List[Any]:  # picklable for multiprocessing
	return i + 1

@dataclass
class        lowerCAmelCase_				:







				"""simple docstring"""




				_snake_case				:   int
				_snake_case				:   str

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :List[str]						=      {}
					UpperCamelCase__ :Dict						=      []
					UpperCamelCase__ :List[Any]						=      1
					UpperCamelCase__ :Optional[Any]						=      [1, 2]
					UpperCamelCase__ :Dict						=      {"""a""": 1, """b""": 2}
					UpperCamelCase__ :Dict						=      {"""a""": [1, 2], """b""": [3, 4]}
					UpperCamelCase__ :List[Any]						=      {"""a""": {"""1""": 1}, """b""": 2}
					UpperCamelCase__ :Optional[Any]						=      {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
					UpperCamelCase__ :Union[str, Any]						=      {}
					UpperCamelCase__ :Tuple						=      []
					UpperCamelCase__ :Dict						=      2
					UpperCamelCase__ :Optional[Any]						=      [2, 3]
					UpperCamelCase__ :Dict						=      {"""a""": 2, """b""": 3}
					UpperCamelCase__ :int						=      {"""a""": [2, 3], """b""": [4, 5]}
					UpperCamelCase__ :Optional[int]						=      {"""a""": {"""1""": 2}, """b""": 3}
					UpperCamelCase__ :Union[str, Any]						=      {"""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__     )

					UpperCamelCase__ :Dict						=      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__     )

					UpperCamelCase__ :int						=      {"""a""": np.eye(2     ), """b""": np.zeros(3     ), """c""": np.ones(2     )}
					UpperCamelCase__ :int						=      {"""a""": 2, """b""": 0, """c""": 2}
					UpperCamelCase__ :Optional[int]						=      {
					    """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     :Tuple     ):
					UpperCamelCase__ :List[str]						=      {"""a""": 1, """b""": 2}
					UpperCamelCase__ :Tuple						=      {"""a""": 3, """b""": 4}
					UpperCamelCase__ :Optional[Any]						=      {"""a""": 5, """b""": 6}
					UpperCamelCase__ :Union[str, Any]						=      sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]     )
					self.assertEqual(sorted(zip_dict(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )     )  ,     lowerCamelCase__     )







				def 				__a   ( self     :Optional[Any]     ):

					class        lowerCAmelCase_				:







									"""simple docstring"""




									_snake_case				:   List[str]        =			"""bar"""

					UpperCamelCase__ :List[str]						=      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		(					lowercase__  :  str ,		lowercase__  :  Any ,		lowercase__  :  Tuple  )		-> Optional[int]:
	with patch("""datasets.utils.py_utils._single_map_nested"""  ) as mock_single_map_nested, patch(
	    """datasets.parallel.parallel.Pool"""  ) as mock_multiprocessing_pool:
		UpperCamelCase__ :Optional[int]						=      {f"""{i}""": i for i in range(lowercase__  )}
		UpperCamelCase__ :Any						=      map_nested(lambda lowercase__  : x + 10 ,		lowercase__ ,		num_proc=lowercase__ ,		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 				__a   ( self     :str     ):
					import tensorflow as tf
					from tensorflow.keras import layers

					UpperCamelCase__ :List[Any]						=      layers.Dense(2     )

					def gen_random_output():
						UpperCamelCase__ :str						=      tf.random.uniform((1, 3)     )
						return model(lowerCamelCase__     ).numpy()

					with temp_seed(42  ,     set_tensorflow=lowerCamelCase__     ):
						UpperCamelCase__ :List[Any]						=      gen_random_output()
					with temp_seed(42  ,     set_tensorflow=lowerCamelCase__     ):
						UpperCamelCase__ :Tuple						=      gen_random_output()
					UpperCamelCase__ :Any						=      gen_random_output()

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







				@require_torch
				def 				__a   ( self     :Union[str, Any]     ):
					import torch

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

					with temp_seed(42  ,     set_pytorch=lowerCamelCase__     ):
						UpperCamelCase__ :Optional[int]						=      gen_random_output()
					with temp_seed(42  ,     set_pytorch=lowerCamelCase__     ):
						UpperCamelCase__ :Union[str, Any]						=      gen_random_output()
					UpperCamelCase__ :Optional[Any]						=      gen_random_output()

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







				def 				__a   ( self     :List[Any]     ):
					def gen_random_output():
						return np.random.rand(1  ,     3     )

					with temp_seed(42     ):
						UpperCamelCase__ :Optional[int]						=      gen_random_output()
					with temp_seed(42     ):
						UpperCamelCase__ :str						=      gen_random_output()
					UpperCamelCase__ :Any						=      gen_random_output()

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




@pytest.mark.parametrize("""input_data""" ,		[{}]  )
def 				A		(					lowercase__  :  Dict  )		-> Union[str, Any]:
	UpperCamelCase__ :Tuple						=      NestedDataStructure(lowercase__  ).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		(					lowercase__  :  str ,		lowercase__  :  Union[str, Any]  )		-> List[Any]:
	UpperCamelCase__ :str						=      NestedDataStructure(lowercase__  ).flatten()
	assert output == expected_output




def 				A		(					)		-> Optional[Any]:
	UpperCamelCase__ :Optional[int]						=      A(x=1 ,		y="""foobar"""  )
	UpperCamelCase__ :Dict						=      {"""x""": 1, """y""": """foobar"""}
	assert asdict(lowercase__  ) == expected_output

	UpperCamelCase__ :Union[str, Any]						=      {"""a""": {"""b""": A(x=10 ,		y="""foo"""  )}, """c""": [A(x=20 ,		y="""bar"""  )]}
	UpperCamelCase__ :Dict						=      {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
	assert asdict(lowercase__  ) == expected_output

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




def 				A		(					lowercase__  :  str  )		-> List[str]:
	return text.split()




def 				A		(					lowercase__  :  Any  )		-> str:
	yield (time.time(), content)
	time.sleep(2  )
	yield (time.time(), content)




def 				A		(					)		-> str:
	with Pool(2  ) as pool:
		UpperCamelCase__ :List[Any]						=      list(iflatmap_unordered(lowercase__ ,		_split_text ,		kwargs_iterable=[{"""text""": """hello there"""}] * 10  )  )
		assert out.count("""hello"""  ) == 10
		assert out.count("""there"""  ) == 10
		assert len(lowercase__  ) == 20

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

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



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   :    List[Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roberta-prelayernorm'

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Tuple=5_02_65       ,		lowerCamelCase       :    Optional[int]=7_68       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Optional[int]="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Optional[int]=5_12       ,		lowerCamelCase       :    Union[str, Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    Any=1E-12       ,		lowerCamelCase       :    int=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    Optional[Any]="absolute"       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    Tuple=None       ,		**lowerCamelCase       :    int       ,		)  ->    Optional[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : List[Any]     =  num_attention_heads
      lowerCAmelCase_     : List[str]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : Tuple     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Optional[int]     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : Union[str, Any]     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Dict     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)



