
'''simple docstring'''
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_(     A						:		str):  # picklable for multiprocessing
							return x.sum()







def 		A_(     A						:		Union[str, Any]):  # picklable for multiprocessing
							return i + 1


@dataclass
class 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     {}
											UpperCamelCase   			=     []
											UpperCamelCase   			=     1
											UpperCamelCase   			=     [1, 2]
											UpperCamelCase   			=     {'a': 1, 'b': 2}
											UpperCamelCase   			=     {'a': [1, 2], 'b': [3, 4]}
											UpperCamelCase   			=     {'a': {'1': 1}, 'b': 2}
											UpperCamelCase   			=     {'a': 1, 'b': 2, 'c': 3, 'd': 4}
											UpperCamelCase   			=     {}
											UpperCamelCase   			=     []
											UpperCamelCase   			=     2
											UpperCamelCase   			=     [2, 3]
											UpperCamelCase   			=     {'a': 2, 'b': 3}
											UpperCamelCase   			=     {'a': [2, 3], 'b': [4, 5]}
											UpperCamelCase   			=     {'a': {'1': 2}, 'b': 3}
											UpperCamelCase   			=     {'a': 2, 'b': 3, 'c': 4, 'd': 5}
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_     )						,				A_     )

											UpperCamelCase   			=     2
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )
											self.assertEqual(map_nested(A_						,				A_						,				num_proc=A_     )						,				A_     )

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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     {'a': 1, 'b': 2}
											UpperCamelCase   			=     {'a': 3, 'b': 4}
											UpperCamelCase   			=     {'a': 5, 'b': 6}
											UpperCamelCase   			=     sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]     )
											self.assertEqual(sorted(zip_dict(A_						,				A_						,				A_     )     )						,				A_     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											class 			SCREAMING_SNAKE_CASE__      :
															lowerCAmelCase_     			=							"""bar"""


											UpperCamelCase   			=     Foo()
											self.assertEqual(foo.my_attr						,				'bar'     )
											with temporary_assignment(A_						,				'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_(     A						:		Optional[Any]		,     A						:		str		,     A						:		Optional[Any]):
							with patch('datasets.utils.py_utils._single_map_nested') as mock_single_map_nested, patch(
							    'datasets.parallel.parallel.Pool') as mock_multiprocessing_pool:
														UpperCamelCase   			=     {f'''{i}''': i for i in range(A)}
														UpperCamelCase   			=     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 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@require_tf
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											import tensorflow as tf
											from tensorflow.keras import layers

											UpperCamelCase   			=     layers.Dense(2     )

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

											with temp_seed(42						,				set_tensorflow=A_     ):
																		UpperCamelCase   			=     gen_random_output()
											with temp_seed(42						,				set_tensorflow=A_     ):
																		UpperCamelCase   			=     gen_random_output()
											UpperCamelCase   			=     gen_random_output()

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







				@require_torch
				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											import torch

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

											with temp_seed(42						,				set_pytorch=A_     ):
																		UpperCamelCase   			=     gen_random_output()
											with temp_seed(42						,				set_pytorch=A_     ):
																		UpperCamelCase   			=     gen_random_output()
											UpperCamelCase   			=     gen_random_output()

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







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






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

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

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







@pytest.mark.parametrize('input_data'		,     [{}])
def 		A_(     A						:		Any):
							UpperCamelCase   			=     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 		A_(     A						:		Optional[Any]		,     A						:		Optional[int]):
							UpperCamelCase   			=     NestedDataStructure(A).flatten()
							assert output == expected_output







def 		A_(     ):
							UpperCamelCase   			=     A(x=1		,     y='foobar')
							UpperCamelCase   			=     {'x': 1, 'y': 'foobar'}
							assert asdict(A) == expected_output

							UpperCamelCase   			=     {'a': {'b': A(x=10		,     y='foo')}, 'c': [A(x=20		,     y='bar')]}
							UpperCamelCase   			=     {'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 		A_(     A						:		str):
							return text.split()







def 		A_(     A						:		Optional[int]):
							yield (time.time(), content)
							time.sleep(2)
							yield (time.time(), content)







def 		A_(     ):
							with Pool(2) as pool:
														UpperCamelCase   			=     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:
														UpperCamelCase   			=     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:
														UpperCamelCase   			=     []
														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






'''simple docstring'''
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowercase__											={
    'cola': 2,
    'mnli': 3,
    'mrpc': 2,
    'sst-2': 2,
    'sts-b': 1,
    'qqp': 2,
    'qnli': 2,
    'rte': 2,
    'wnli': 2,
}


logging.set_verbosity_info()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__=None						):
	# Initialise PyTorch model
	a_      				=						XLNetConfig.from_json_file(A__						)

	a_      				=						finetuning_task.lower() if finetuning_task is not None else """"""
	if finetuning_task in GLUE_TASKS_NUM_LABELS:
		print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
		a_      				=						finetuning_task
		a_      				=						GLUE_TASKS_NUM_LABELS[finetuning_task]
		a_      				=						XLNetForSequenceClassification(A__						)
	elif "squad" in finetuning_task:
		a_      				=						finetuning_task
		a_      				=						XLNetForQuestionAnswering(A__						)
	else:
		a_      				=						XLNetLMHeadModel(A__						)

	# Load weights from tf checkpoint
	load_tf_weights_in_xlnet(A__  ,      A__  ,      A__						)

	# Save pytorch-model
	a_      				=						os.path.join(A__  ,      A__						)
	a_      				=						os.path.join(A__  ,      A__						)
	print(F'''Save PyTorch model to {os.path.abspath(A__						)}'''						)
	torch.save(model.state_dict()  ,      A__						)
	print(F'''Save configuration file to {os.path.abspath(A__						)}'''						)
	with open(A__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
		f.write(config.to_json_string()						)


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--xlnet_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained XLNet model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path to the folder to store the PyTorch model or dataset/vocab.',
		)
		parser.add_argument(
		    '--finetuning_task',
		    default=None,
		    type=str,
		    help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
		)
		lowercase__											=parser.parse_args()
		print(args)

		convert_xlnet_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
		)
