





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


SCREAMING_SNAKE_CASE_      :			Dict		  =      (30, 487)
SCREAMING_SNAKE_CASE_      :			str		  =      (36, 1024)
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      (100, 100)
SCREAMING_SNAKE_CASE_      :			List[str]		  =      100

SCREAMING_SNAKE_CASE_      :			Optional[Any]		  =      datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_      :			Optional[int]		  =      os.path.split(__file__)
SCREAMING_SNAKE_CASE_      :			Dict		  =      os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						Dict:
		with ArrowWriter(features=snake_case	,      path=os.path.join(snake_case	,      'beta.arrow'       )       ) as writer:
				for key, record in dummy_data:
						__lowercase					=    my_features.encode_example(snake_case       )
						writer.write(snake_case       )
				__lowercase	,			__lowercase					=    writer.finalize()



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Dict:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		for _ in dataset:
				pass



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Tuple:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		dataset.set_format('numpy'       )
		for _ in dataset:
				pass



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Optional[int]:
		__lowercase					=    10
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		for i in range(0	,      len(snake_case       )	,      snake_case       ):
				__lowercase					=    dataset[i : i + batch_size]



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Any:
		__lowercase					=    10
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		dataset.set_format('numpy'       )
		for i in range(0	,      len(snake_case       )	,      snake_case       ):
				__lowercase					=    dataset[i : i + batch_size]



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Optional[int]:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		for col in feats:
				__lowercase					=    dataset[col]



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Tuple:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		dataset.set_format('numpy'       )
		for col in feats:
				__lowercase					=    dataset[col]



def    SCREAMING_SNAKE_CASE							(			)		->						List[Any]:
		__lowercase					=    {}
		__lowercase					=    (
		    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:
				__lowercase					=    datasets.Features({'image': ArrayaD(snake_case	,      dtype='float32'       )}       )
				__lowercase					=    generate_examples(features=snake_case	,      num_examples=snake_case       )
				__lowercase					=    write(snake_case	,      snake_case	,      snake_case       )
				for read_func in read_functions:
						__lowercase					=    read_func(snake_case	,      snake_case       )

		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])}
				# )
				__lowercase					=    datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'       )       )       )}       )
				__lowercase					=    generate_examples(
				    features=snake_case	,      num_examples=snake_case	,      seq_shapes={'image': SPEED_TEST_SHAPE}       )
				__lowercase					=    write(snake_case	,      snake_case	,      snake_case       )
				for read_func in read_functions:
						__lowercase					=    read_func(snake_case	,      snake_case       )

		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])}
				# )
				__lowercase					=    datasets.Features({'image': datasets.Sequence(datasets.Value('float32'       )       )}       )
				__lowercase					=    generate_examples(
				    features=snake_case	,      num_examples=snake_case	,      seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}	,      )
				__lowercase					=    write(snake_case	,      snake_case	,      snake_case       )
				for read_func in read_functions:
						__lowercase					=    read_func(snake_case	,      snake_case       )

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


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











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    List[Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    tempfile.mkdtemp()

			__lowercase					=    ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    Path(self.tmpdirname      )
			save_json(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['vocab_file']      )
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['spm_file']      )

			__lowercase					=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
			tokenizer.save_pretrained(self.tmpdirname      )

			__lowercase					=    {
			    'feature_size': 24,
			    'num_mel_bins': 24,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}
			save_json(__lowerCamelCase				,			save_dir / FEATURE_EXTRACTOR_NAME      )




	def       UpperCAmelCase (	self       :    Dict				,			**__lowerCamelCase       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname      )




	def       UpperCAmelCase (	self       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.get_tokenizer()
			__lowercase					=    self.get_feature_extractor()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			processor.save_pretrained(self.tmpdirname      )
			__lowercase					=    SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__lowercase					=    self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'      )
			__lowercase					=    self.get_feature_extractor(do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			__lowercase					=    SpeechaTextProcessor.from_pretrained(
			    self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor_add_kwargs.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    floats_list((3, 1_000)      )

			__lowercase					=    feature_extractor(__lowerCamelCase				,			return_tensors='np'      )
			__lowercase					=    processor(__lowerCamelCase				,			return_tensors='np'      )

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1E-2      )




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

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    'This is a test string'

			__lowercase					=    processor(text=__lowerCamelCase      )

			__lowercase					=    tokenizer(__lowerCamelCase      )

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]      )




	def       UpperCAmelCase (	self       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowercase					=    processor.batch_decode(__lowerCamelCase      )
			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase      )

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			self.assertListEqual(
			    processor.model_input_names				,			feature_extractor.model_input_names				,			msg='`processor` and `feature_extractor` model input names do not match'				,			)




