
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_						(				__SCREAMING_SNAKE_CASE				):  # picklable for multiprocessing
	return x.sum()






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):  # picklable for multiprocessing
	return i + 1







@dataclass
class 			A_  :




	'''simple docstring'''


	_UpperCamelCase	:	int
	_UpperCamelCase	:	str







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       {}
		lowercase	    =       []
		lowercase	    =       1
		lowercase	    =       [1, 2]
		lowercase	    =       {'a': 1, 'b': 2}
		lowercase	    =       {'a': [1, 2], 'b': [3, 4]}
		lowercase	    =       {'a': {'1': 1}, 'b': 2}
		lowercase	    =       {'a': 1, 'b': 2, 'c': 3, 'd': 4}
		lowercase	    =       {}
		lowercase	    =       []
		lowercase	    =       2
		lowercase	    =       [2, 3]
		lowercase	    =       {'a': 2, 'b': 3}
		lowercase	    =       {'a': [2, 3], 'b': [4, 5]}
		lowercase	    =       {'a': {'1': 2}, 'b': 3}
		lowercase	    =       {'a': 2, 'b': 3, 'c': 4, 'd': 5}
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case			)						,       snake_case			)

		lowercase	    =       2
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(map_nested(snake_case						,       snake_case						,       num_proc=snake_case			)						,       snake_case			)

		lowercase	    =       {'a': np.eye(2			), 'b': np.zeros(3			), 'c': np.ones(2			)}
		lowercase	    =       {'a': 2, 'b': 0, 'c': 2}
		lowercase	    =       {
		    'a': np.eye(2			).astype(snake_case			),
		    'b': np.zeros(3			).astype(snake_case			),
		    'c': np.ones(2			).astype(snake_case			),
		}
		self.assertEqual(map_nested(snake_case						,       snake_case						,       map_numpy=snake_case			)						,       snake_case			)
		self.assertEqual(
		    {k: v.tolist() for k, v in map_nested(snake_case						,       snake_case						,       map_numpy=snake_case			).items()}						,       {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}						,       )
		self.assertEqual(map_nested(snake_case						,       snake_case						,       map_numpy=snake_case						,       num_proc=snake_case			)						,       snake_case			)
		self.assertEqual(
		    {k: v.tolist() for k, v in map_nested(snake_case						,       snake_case						,       map_numpy=snake_case						,       num_proc=snake_case			).items()}						,       {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}						,       )
		with self.assertRaises(snake_case			):  # can't pickle a local lambda
			map_nested(lambda snake_case			: x + 1						,       snake_case						,       num_proc=snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       {'a': 1, 'b': 2}
		lowercase	    =       {'a': 3, 'b': 4}
		lowercase	    =       {'a': 5, 'b': 6}
		lowercase	    =       sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]			)
		self.assertEqual(sorted(zip_dict(snake_case						,       snake_case						,       snake_case			)			)						,       snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):







		class 			A_  :




			'''simple docstring'''


			_UpperCamelCase	:	Tuple									=			"""bar"""




		lowercase	    =       Foo()
		self.assertEqual(foo.my_attr						,       'bar'			)
		with temporary_assignment(snake_case						,       '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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	with patch('datasets.utils.py_utils._single_map_nested'				) as mock_single_map_nested, patch(
	    'datasets.parallel.parallel.Pool'				) as mock_multiprocessing_pool:
		lowercase	    =       {F'''{i}''': i for i in range(__SCREAMING_SNAKE_CASE				)}
		lowercase	    =       map_nested(lambda __SCREAMING_SNAKE_CASE				: x + 10					,				__SCREAMING_SNAKE_CASE					,				num_proc=__SCREAMING_SNAKE_CASE					,				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 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	@require_tf
	def       SCREAMING_SNAKE_CASE__		(			self			):
		import tensorflow as tf
		from tensorflow.keras import layers

		lowercase	    =       layers.Dense(2			)

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

		with temp_seed(42						,       set_tensorflow=snake_case			):
			lowercase	    =       gen_random_output()
		with temp_seed(42						,       set_tensorflow=snake_case			):
			lowercase	    =       gen_random_output()
		lowercase	    =       gen_random_output()

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





	@require_torch
	def       SCREAMING_SNAKE_CASE__		(			self			):
		import torch

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

		with temp_seed(42						,       set_pytorch=snake_case			):
			lowercase	    =       gen_random_output()
		with temp_seed(42						,       set_pytorch=snake_case			):
			lowercase	    =       gen_random_output()
		lowercase	    =       gen_random_output()

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		def gen_random_output():
			return np.random.rand(1						,       3			)

		with temp_seed(42			):
			lowercase	    =       gen_random_output()
		with temp_seed(42			):
			lowercase	    =       gen_random_output()
		lowercase	    =       gen_random_output()

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






@pytest.mark.parametrize('input_data'					,				[{}]				)
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       NestedDataStructure(__SCREAMING_SNAKE_CASE				).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_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       NestedDataStructure(__SCREAMING_SNAKE_CASE				).flatten()
	assert output == expected_output






def 				UpperCAmelCase_						(				):
	lowercase	    =       A(x=1					,				y='foobar'				)
	lowercase	    =       {'x': 1, 'y': 'foobar'}
	assert asdict(__SCREAMING_SNAKE_CASE				) == expected_output

	lowercase	    =       {'a': {'b': A(x=10					,				y='foo'				)}, 'c': [A(x=20					,				y='bar'				)]}
	lowercase	    =       {'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
	assert asdict(__SCREAMING_SNAKE_CASE				) == expected_output

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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	return text.split()






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	yield (time.time(), content)
	time.sleep(2				)
	yield (time.time(), content)






def 				UpperCAmelCase_						(				):
	with Pool(2				) as pool:
		lowercase	    =       list(iflatmap_unordered(__SCREAMING_SNAKE_CASE					,				_split_text					,				kwargs_iterable=[{'text': 'hello there'}] * 10				)				)
		assert out.count('hello'				) == 10
		assert out.count('there'				) == 10
		assert len(__SCREAMING_SNAKE_CASE				) == 20

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

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





from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





