





import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class   _UpperCamelCase  (       A_						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       PhobertTokenizer
					lowerCamelCase     :     Optional[int]			    =       False





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCAmelCase_								=     ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
						UpperCAmelCase_								=     dict(zip(__lowercase ,					range(len(__lowercase		)		)		)		)
						UpperCAmelCase_								=     ["""#version: 0.2""", """l à</w>"""]
						UpperCAmelCase_								=     {"""unk_token""": """<unk>"""}

						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]		)
						UpperCAmelCase_								=     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(__lowercase		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					**__lowercase    :  str		):






						'''simple docstring'''







						kwargs.update(self.special_tokens_map		)
						return PhobertTokenizer.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     """Tôi là VinAI Research"""
						UpperCAmelCase_								=     """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
						return input_text, output_text





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     PhobertTokenizer(self.vocab_file ,					self.merges_file ,					**self.special_tokens_map		)
						UpperCAmelCase_								=     """Tôi là VinAI Research"""
						UpperCAmelCase_								=     """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
						UpperCAmelCase_								=     tokenizer.tokenize(__lowercase		)
						print(__lowercase		)
						self.assertListEqual(__lowercase ,					__lowercase		)

						UpperCAmelCase_								=     tokens + [tokenizer.unk_token]

						UpperCAmelCase_								=     [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(__lowercase		) ,					__lowercase		)









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__   : Tuple					  = 5_00_00
UpperCamelCase__   : Any					  = 50_00

UpperCamelCase__  ,			UpperCamelCase__   : Tuple					  = os.path.split(__file__)
UpperCamelCase__   : Union[str, Any]					  = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def    A_(			A				,      A   ):
	for i in range(A   ):
		UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A   ):
	for i in range(0				,      len(A   )				,      A   ):
		UpperCAmelCase_								=     dataset[i : i + batch_size]




@get_duration
def    A_(			A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(A   ):
			UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(0				,      A				,      A   ):
			UpperCAmelCase_								=     dataset[i : i + batch_size]




def    A_(			):
	UpperCAmelCase_								=     {"""num examples""": SPEED_TEST_N_EXAMPLES}
	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]

	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]
	with tempfile.TemporaryDirectory() as tmp_dir:
		print("""generating dataset"""   )
		UpperCAmelCase_								=     datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""   )   ), """numbers""": datasets.Value("""float32"""   )}   )
		UpperCAmelCase_								=     generate_example_dataset(
		    os.path.join(A				,      """dataset.arrow"""   )				,      A				,      num_examples=A				,      seq_shapes={"""list""": (100,)}				,      )
		print("""first set of iterations"""   )
		for func, kwargs in functions:
			print(func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(A				,      **A   )

		print("""shuffling dataset"""   )
		UpperCAmelCase_								=     dataset.shuffle()
		print("""Second set of iterations (after shuffling"""   )
		for func, kwargs in functions_shuffled:
			print("""shuffled """				,      func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(
			    A				,      **A   )

	with open(A				,      """wb"""   ) as f:
		f.write(json.dumps(A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_iterating()


