






'''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__			(				__lowerCAmelCase		:			List[Any]						):  # picklable for multiprocessing
						return x.sum()
def 						A__			(				__lowerCAmelCase		:			int						):  # picklable for multiprocessing
						return i + 1




@dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





		_UpperCamelCase				  =					42
		_UpperCamelCase				  =					42




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								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 _lowerCAmelCase       : x + 1   ,_lowerCAmelCase   ,num_proc=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self       ):




								class 		UpperCamelCase__    :




										'''simple docstring'''





										_UpperCamelCase				  =					'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 						A__			(				__lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			int						):
						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(__lowerCAmelCase						)}
												lowerCamelCase__         =  map_nested(lambda __lowerCAmelCase						: x + 10   ,    __lowerCAmelCase   ,    num_proc=__lowerCAmelCase   ,    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 		UpperCamelCase__    (a			):




		'''simple docstring'''





		@require_tf
		def 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self       ):
								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 						A__			(				__lowerCAmelCase		:			Optional[int]						):
						lowerCamelCase__         =  NestedDataStructure(__lowerCAmelCase						).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__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  NestedDataStructure(__lowerCAmelCase						).flatten()
						assert output == expected_output
def 						A__			(				):
						lowerCamelCase__         =  A(x=1   ,    y="""foobar"""						)
						lowerCamelCase__         =  {"""x""": 1, """y""": """foobar"""}
						assert asdict(__lowerCAmelCase						) == 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(__lowerCAmelCase						) == expected_output

						with pytest.raises(__lowerCAmelCase						):
												asdict([1, A(x=10   ,    y="""foo"""						)]						)
def 						A__			(				__lowerCAmelCase		:			str						):
						return text.split()
def 						A__			(				__lowerCAmelCase		:			Optional[Any]						):
						yield (time.time(), content)
						time.sleep(2						)
						yield (time.time(), content)





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

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

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









'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase    :      int  			=		logging.get_logger(__name__)

UpperCamelCase    :      Optional[int]  			=		{'vocab_file': 'sentencepiece.bpe.model'}

UpperCamelCase    :      Dict  			=		{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCamelCase    :      List[Any]  			=		{
    'camembert-base': 5_12,
}

UpperCamelCase    :      List[str]  			=		'▁'




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					VOCAB_FILES_NAMES
		_UpperCamelCase				  =					PRETRAINED_VOCAB_FILES_MAP
		_UpperCamelCase				  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_UpperCamelCase				  =					['input_ids', 'attention_mask']






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase="<s>"   ,_lowerCAmelCase="</s>"   ,_lowerCAmelCase="</s>"   ,_lowerCAmelCase="<s>"   ,_lowerCAmelCase="<unk>"   ,_lowerCAmelCase="<pad>"   ,_lowerCAmelCase="<mask>"   ,_lowerCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase__         =  AddedToken(_lowerCAmelCase   ,lstrip=_lowerCAmelCase   ,rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ) else mask_token

								lowerCamelCase__         =  {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_lowerCAmelCase   ,eos_token=_lowerCAmelCase   ,unk_token=_lowerCAmelCase   ,sep_token=_lowerCAmelCase   ,cls_token=_lowerCAmelCase   ,pad_token=_lowerCAmelCase   ,mask_token=_lowerCAmelCase   ,additional_special_tokens=_lowerCAmelCase   ,sp_model_kwargs=self.sp_model_kwargs   ,**_lowerCAmelCase   ,)
								lowerCamelCase__         =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(str(_lowerCAmelCase       )       )
								lowerCamelCase__         =  vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								lowerCamelCase__         =  {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
								lowerCamelCase__         =  len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):

								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase__         =  [self.cls_token_id]
								lowerCamelCase__         =  [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,_lowerCAmelCase = False       ):
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_lowerCAmelCase   ,token_ids_a=_lowerCAmelCase   ,already_has_special_tokens=_lowerCAmelCase       )

								if token_ids_a is None:
														return [1] + ([0] * len(_lowerCAmelCase       )) + [1]
								return [1] + ([0] * len(_lowerCAmelCase       )) + [1, 1] + ([0] * len(_lowerCAmelCase       )) + [1]






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								lowerCamelCase__         =  [self.sep_token_id]
								lowerCamelCase__         =  [self.cls_token_id]

								if token_ids_a is None:
														return len(cls + token_ids_a + sep       ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






		@property
		def 			UpperCamelCase_					(      self       ):
								return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  {self.convert_ids_to_tokens(_lowerCAmelCase       ): i for i in range(self.vocab_size       )}
								vocab.update(self.added_tokens_encoder       )
								return vocab






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self.sp_model.encode(_lowerCAmelCase   ,out_type=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(_lowerCAmelCase       ) == 0:
														# Convert sentence piece unk token to fairseq unk token index
														return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if index in self.fairseq_ids_to_tokens:
														return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  []
								lowerCamelCase__         =  """"""
								lowerCamelCase__         =  False
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				if not prev_is_special:
																										out_string += " "
																				out_string += self.sp_model.decode(_lowerCAmelCase       ) + token
																				lowerCamelCase__         =  True
																				lowerCamelCase__         =  []
														else:
																				current_sub_tokens.append(_lowerCAmelCase       )
																				lowerCamelCase__         =  False
								out_string += self.sp_model.decode(_lowerCAmelCase       )
								return out_string.strip()






		def __getstate__(      self       ):
								lowerCamelCase__         =  self.__dict__.copy()
								lowerCamelCase__         =  None
								return state






		def __setstate__(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  d

								# for backward compatibility
								if not hasattr(self   ,"""sp_model_kwargs"""       ):
														lowerCamelCase__         =  {}

								lowerCamelCase__         =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(self.vocab_file       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								if not os.path.isdir(_lowerCAmelCase       ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
														return
								lowerCamelCase__         =  os.path.join(
								    _lowerCAmelCase   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

								if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowerCAmelCase       ) and os.path.isfile(self.vocab_file       ):
														copyfile(self.vocab_file   ,_lowerCAmelCase       )
								elif not os.path.isfile(self.vocab_file       ):
														with open(_lowerCAmelCase   ,"""wb"""       ) as fi:
																				lowerCamelCase__         =  self.sp_model.serialized_model_proto()
																				fi.write(_lowerCAmelCase       )

								return (out_vocab_file,)

