
"""simple docstring"""


import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Optional[Any]          =    PhobertTokenizer
				a__:      Optional[Any]          =    False




				def        UpperCAmelCase__						(				self   ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCamelCase_					=  ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
							lowerCamelCase_					=  dict(zip(UpperCAmelCase							,						range(len(UpperCAmelCase   )   )   )   )
							lowerCamelCase_					=  ['''#version: 0.2''', '''l à</w>''']
							lowerCamelCase_					=  {'''unk_token''': '''<unk>'''}

							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file''']   )
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''merges_file''']   )

							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										for token in vocab_tokens:
													fp.write(f"{token} {vocab_tokens[token]}\n"   )
							with open(self.merges_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										fp.write('''\n'''.join(UpperCAmelCase   )   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return PhobertTokenizer.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  '''Tôi là VinAI Research'''
							lowerCamelCase_					=  '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
							return input_text, output_text





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  PhobertTokenizer(self.vocab_file							,						self.merges_file							,						**self.special_tokens_map   )
							lowerCamelCase_					=  '''Tôi là VinAI Research'''
							lowerCamelCase_					=  '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
							lowerCamelCase_					=  tokenizer.tokenize(UpperCAmelCase   )
							print(UpperCAmelCase   )
							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  tokens + [tokenizer.unk_token]

							lowerCamelCase_					=  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase   )							,						UpperCAmelCase   )





"""simple docstring"""


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def  lowercase						(	lowerCAmelCase__						):
			def wrapper(*lowerCAmelCase__	,**lowerCAmelCase__						):
						lowerCamelCase_					=  timeit.default_timer()
						lowerCamelCase_					=  func(*lowerCAmelCase__	,**lowerCAmelCase__						)
						lowerCamelCase_					=  timeit.default_timer() - starttime
						return delta

			lowerCamelCase_					=  func.__name__

			return wrapper






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  []
			lowerCamelCase_					=  seq_shapes or {}
			for i in range(lowerCAmelCase__						):
						lowerCamelCase_					=  {}
						for col_id, (k, v) in enumerate(features.items()						):
									if isinstance(lowerCAmelCase__	,_ArrayXD						):
												lowerCamelCase_					=  np.random.rand(*v.shape						).astype(v.dtype						)
									elif isinstance(lowerCAmelCase__	,datasets.Value						):
												if v.dtype == "string":
															lowerCamelCase_					=  '''The small grey turtle was surprisingly fast when challenged.'''
												else:
															lowerCamelCase_					=  np.random.randint(10	,size=1						).astype(v.dtype						).item()
									elif isinstance(lowerCAmelCase__	,datasets.Sequence						):
												while isinstance(lowerCAmelCase__	,datasets.Sequence						):
															lowerCamelCase_					=  v.feature
												lowerCamelCase_					=  seq_shapes[k]
												lowerCamelCase_					=  np.random.rand(*lowerCAmelCase__						).astype(v.dtype						)
									lowerCamelCase_					=  data

						dummy_data.append((i, example)						)

			return dummy_data






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  generate_examples(lowerCAmelCase__	,num_examples=lowerCAmelCase__	,seq_shapes=lowerCAmelCase__						)

			with ArrowWriter(features=lowerCAmelCase__	,path=lowerCAmelCase__						) as writer:
						for key, record in dummy_data:
									lowerCamelCase_					=  features.encode_example(lowerCAmelCase__						)
									writer.write(lowerCAmelCase__						)

						lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."						)

			lowerCamelCase_					=  datasets.Dataset.from_file(filename=lowerCAmelCase__	,info=datasets.DatasetInfo(features=lowerCAmelCase__						)						)

			return dataset



