




import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


_lowerCamelCase					:     List[str]					  =  (3_0, 4_8_7)
_lowerCamelCase					:     List[str]					  =  (3_6, 1_0_2_4)
_lowerCamelCase					:     Tuple					  =  (1_0_0, 1_0_0)
_lowerCamelCase					:     List[Any]					  =  1_0_0

_lowerCamelCase					:     Optional[int]					  =  datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

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




@get_duration
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				with ArrowWriter(features=UpperCamelCase_					,			path=os.path.join(UpperCamelCase_					,			"""beta.arrow"""				)				) as writer:
								for key, record in dummy_data:
												_lowerCAmelCase			:			Dict        =					my_features.encode_example(UpperCamelCase_				)
												writer.write(UpperCamelCase_				)
								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					writer.finalize()




@get_duration
def 							_UpperCAmelCase     (UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       List[Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Any        =					datasets.Dataset.from_file(
				    filename=os.path.join(UpperCamelCase_					,			"""beta.arrow"""				)					,			info=datasets.DatasetInfo(features=UpperCamelCase_				)				)
				for _ in dataset:
								pass




@get_duration
def 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       List[Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			str        =					datasets.Dataset.from_file(
				    filename=os.path.join(UpperCamelCase_					,			"""beta.arrow"""				)					,			info=datasets.DatasetInfo(features=UpperCamelCase_				)				)
				dataset.set_format("""numpy"""				)
				for _ in dataset:
								pass




@get_duration
def 							_UpperCAmelCase     (UpperCamelCase_       :       Union[str, Any]					,			UpperCamelCase_       :       List[str]				):
				'''simple docstring'''
				_lowerCAmelCase			:			int        =					10
				_lowerCAmelCase			:			Dict        =					datasets.Dataset.from_file(
				    filename=os.path.join(UpperCamelCase_					,			"""beta.arrow"""				)					,			info=datasets.DatasetInfo(features=UpperCamelCase_				)				)
				for i in range(0					,			len(UpperCamelCase_				)					,			UpperCamelCase_				):
								_lowerCAmelCase			:			str        =					dataset[i : i + batch_size]




@get_duration
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       int				):
				'''simple docstring'''
				_lowerCAmelCase			:			int        =					10
				_lowerCAmelCase			:			Union[str, Any]        =					datasets.Dataset.from_file(
				    filename=os.path.join(UpperCamelCase_					,			"""beta.arrow"""				)					,			info=datasets.DatasetInfo(features=UpperCamelCase_				)				)
				dataset.set_format("""numpy"""				)
				for i in range(0					,			len(UpperCamelCase_				)					,			UpperCamelCase_				):
								_lowerCAmelCase			:			Tuple        =					dataset[i : i + batch_size]




@get_duration
def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       List[Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Dict        =					datasets.Dataset.from_file(
				    filename=os.path.join(UpperCamelCase_					,			"""beta.arrow"""				)					,			info=datasets.DatasetInfo(features=UpperCamelCase_				)				)
				for col in feats:
								_lowerCAmelCase			:			Tuple        =					dataset[col]




@get_duration
def 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					datasets.Dataset.from_file(
				    filename=os.path.join(UpperCamelCase_					,			"""beta.arrow"""				)					,			info=datasets.DatasetInfo(features=UpperCamelCase_				)				)
				dataset.set_format("""numpy"""				)
				for col in feats:
								_lowerCAmelCase			:			Optional[Any]        =					dataset[col]




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[Any]        =					{}
				_lowerCAmelCase			:			List[str]        =					(
				    read_unformated,
				    read_formatted_as_numpy,
				    read_batch_unformated,
				    read_batch_formatted_as_numpy,
				    read_col_unformated,
				    read_col_formatted_as_numpy,
				)
				with tempfile.TemporaryDirectory() as tmp_dir:
								_lowerCAmelCase			:			Tuple        =					datasets.Features({"""image""": ArrayaD(UpperCamelCase_					,			dtype="""float32"""				)}				)
								_lowerCAmelCase			:			Optional[Any]        =					generate_examples(features=UpperCamelCase_					,			num_examples=UpperCamelCase_				)
								_lowerCAmelCase			:			Optional[Any]        =					write(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)
								for read_func in read_functions:
												_lowerCAmelCase			:			Union[str, Any]        =					read_func(UpperCamelCase_					,			UpperCamelCase_				)

				with tempfile.TemporaryDirectory() as tmp_dir:
								# don't use fixed length for fair comparison
								# feats = datasets.Features(
								#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
								# )
								_lowerCAmelCase			:			Any        =					datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""				)				)				)}				)
								_lowerCAmelCase			:			List[str]        =					generate_examples(
								    features=UpperCamelCase_					,			num_examples=UpperCamelCase_					,			seq_shapes={"""image""": SPEED_TEST_SHAPE}				)
								_lowerCAmelCase			:			int        =					write(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)
								for read_func in read_functions:
												_lowerCAmelCase			:			Union[str, Any]        =					read_func(UpperCamelCase_					,			UpperCamelCase_				)

				with tempfile.TemporaryDirectory() as tmp_dir:
								# don't use fixed length for fair comparison
								# feats = datasets.Features(
								#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
								# )
								_lowerCAmelCase			:			Union[str, Any]        =					datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""				)				)}				)
								_lowerCAmelCase			:			Optional[int]        =					generate_examples(
								    features=UpperCamelCase_					,			num_examples=UpperCamelCase_					,			seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}					,			)
								_lowerCAmelCase			:			List[Any]        =					write(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)
								for read_func in read_functions:
												_lowerCAmelCase			:			Tuple        =					read_func(UpperCamelCase_					,			UpperCamelCase_				)

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


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







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   SpeechaTextaTokenizer
				lowerCAmelCase__			=   False



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





								'''simple docstring'''




								super().setUp()

								_lowerCAmelCase			:			Optional[int]        =					"""<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab""".split(""" """					)
								_lowerCAmelCase			:			Optional[int]        =					["""he re</w> 123""", """here a 1456"""]
								_lowerCAmelCase			:			Union[str, Any]        =					dict(zip(_UpperCAmelCase						,		range(len(_UpperCAmelCase					)					)					)					)

								_lowerCAmelCase			:			Any        =					{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

								_lowerCAmelCase			:			Union[str, Any]        =					tempfile.mkdtemp()
								_lowerCAmelCase			:			Tuple        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase			:			Any        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""merges_file"""]					)

								with open(self.vocab_file						,		"""w"""						,		encoding="""utf-8"""					) as fp:
												fp.write(json.dumps(_UpperCAmelCase					) + """\n"""					)

								with open(self.merges_file						,		"""w"""					) as fp:
												fp.write("""\n""".join(_UpperCAmelCase					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0]						,		"""<s>"""					)
								self.assertEqual(vocab_keys[1]						,		"""<pad>"""					)
								self.assertEqual(vocab_keys[-1]						,		"""vocab"""					)
								self.assertEqual(len(_UpperCAmelCase					)						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								self.assertEqual(self.get_tokenizer().vocab_size						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								# make sure @@ is correctly concatenated
								_lowerCAmelCase			:			int        =					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
								_lowerCAmelCase			:			List[Any]        =					tokenizer.decode(_UpperCAmelCase					)

								self.assertTrue(output_string == """herecouple words ofthe"""					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(_UpperCAmelCase					)
												os.remove(os.path.join(_UpperCAmelCase						,		"""merges.txt"""					)					)

												# load tokenizer without merges file should not throw an error
												_lowerCAmelCase			:			Any        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			int        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								self.assertIsNotNone(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								if not self.test_slow_tokenizer:
												return

								_lowerCAmelCase			:			Dict        =					inspect.signature(self.tokenizer_class.__init__					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.get_tokenizer()

								for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																self.assertIn(_UpperCAmelCase						,		tokenizer.init_kwargs					)

