



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(						UpperCAmelCase__			):  # picklable for multiprocessing


      """simple docstring"""



      return x.sum()

def      __lowercase(						UpperCAmelCase__			):  # picklable for multiprocessing


      """simple docstring"""



      return i + 1





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _A      =      42
     _A      =      42





class  lowerCamelCase__	(    __a):
     """simple docstring"""



     def     _a   (self     ):






           '''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 __a     : x + 1					,      lowerCAmelCase__					,      num_proc=lowerCAmelCase__     )







     def     _a   (self     ):






           '''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     _a   (self     ):






           '''simple docstring'''



           class  lowerCamelCase__	:
                """simple docstring"""



                _A      =      '''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),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ] , )
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """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 + 10 , UpperCAmelCase__ , num_proc=UpperCAmelCase__ , 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):
     """simple docstring"""



     @require_tf
     def     _a   (self     ):






           '''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     _a   (self     ):






           '''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     _a   (self     ):






           '''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      __lowercase(						UpperCAmelCase__			):


      """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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		NestedDataStructure(UpperCAmelCase__			).flatten()
      assert output == expected_output

def      __lowercase(						):


      """simple docstring"""



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

      lowerCamelCase             =		{"a": {"b": A(x=10 , y="foo"			)}, "c": [A(x=20 , y="bar"			)]}
      lowerCamelCase             =		{"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
      assert asdict(UpperCAmelCase__			) == expected_output

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

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      return text.split()

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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






def      __lowercase(						):


      """simple docstring"""



      with Pool(2			) as pool:
            lowerCamelCase             =		list(iflatmap_unordered(UpperCAmelCase__ , _split_text , kwargs_iterable=[{"text": "hello there"}] * 10			)			)
            assert out.count("hello"			) == 10
            assert out.count("there"			) == 10
            assert len(UpperCAmelCase__			) == 20

      # 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"}] * 10			)			)
            assert out.count("hello"			) == 10
            assert out.count("there"			) == 10
            assert len(UpperCAmelCase__			) == 20

      # 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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case      :							List[str]     	=    logging.get_logger(__name__)

_snake_case      :							int     	=    {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					'''switch_transformers'''
						UpperCamelCase      :  Dict				   =					['''past_key_values''']
						UpperCamelCase      :  Union[str, Any]				   =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





						def __init__(			self							:int		,    lowerCAmelCase__							:str=32128		,    lowerCAmelCase__							:Union[str, Any]=768		,    lowerCAmelCase__							:Tuple=64		,    lowerCAmelCase__							:Any=2048		,    lowerCAmelCase__							:Optional[int]=64		,    lowerCAmelCase__							:Any=12		,    lowerCAmelCase__							:Any=3		,    lowerCAmelCase__							:Union[str, Any]=12		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:str=12		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[str]=0.01		,    lowerCAmelCase__							:Union[str, Any]="float32"		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:Optional[Any]=32		,    lowerCAmelCase__							:int=128		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=1E-6		,    lowerCAmelCase__							:Any=0.0_01		,    lowerCAmelCase__							:Union[str, Any]=0.0_01		,    lowerCAmelCase__							:List[Any]=1.0		,    lowerCAmelCase__							:Optional[Any]="relu"		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=True		,    lowerCAmelCase__							:int=0		,    lowerCAmelCase__							:Optional[Any]=1		,    **lowerCAmelCase__							:List[Any]		,    )  ->Dict:
										lowercase         =      vocab_size
										lowercase         =      d_model
										lowercase         =      d_kv
										lowercase         =      d_ff

										lowercase         =      num_sparse_encoder_layers

										lowercase         =      num_layers
										lowercase         =      (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowercase         =      num_sparse_decoder_layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_encoder_layers > 0:
														lowercase         =      self.num_layers // self.num_sparse_encoder_layers
										else:
														lowercase         =      self.num_layers  # HACK: this will create 0 sparse layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_decoder_layers > 0:
														lowercase         =      self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
														lowercase         =      self.num_decoder_layers  # HACK: this will create 0 sparse layers

										lowercase         =      num_heads
										lowercase         =      num_experts
										lowercase         =      expert_capacity
										lowercase         =      router_bias
										lowercase         =      router_jitter_noise
										if router_dtype not in ["float32", "float16", "bfloat16"]:
														raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''     )
										lowercase         =      router_dtype

										lowercase         =      router_ignore_padding_tokens
										lowercase         =      relative_attention_num_buckets
										lowercase         =      relative_attention_max_distance

										lowercase         =      dropout_rate
										lowercase         =      layer_norm_epsilon
										lowercase         =      initializer_factor
										lowercase         =      feed_forward_proj
										lowercase         =      use_cache
										lowercase         =      add_router_probs

										lowercase         =      router_z_loss_coef
										lowercase         =      router_aux_loss_coef

										lowercase         =      self.feed_forward_proj.split("-"     )
										lowercase         =      act_info[-1]
										lowercase         =      act_info[0] == "gated"

										if len(lowerCAmelCase__     ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__     ) > 2:
														raise ValueError(
														    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
														    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
														    "'gated-gelu' or 'relu'"     )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														lowercase         =      "gelu_new"

										super().__init__(
										    pad_token_id=lowerCAmelCase__		,    eos_token_id=lowerCAmelCase__		,    is_encoder_decoder=lowerCAmelCase__		,    **lowerCAmelCase__		,    )
