





'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_SCREAMING_SNAKE_CASE	 =							50_00_00

_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE	 =							os.path.split(__file__)
_SCREAMING_SNAKE_CASE	 =							os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def 	__a(SCREAMING_SNAKE_CASE_  :					datasets.Dataset		,       **SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





    _lowerCAmelCase				=      dataset.map(**SCREAMING_SNAKE_CASE_    )


@get_duration
def 	__a(SCREAMING_SNAKE_CASE_  :					datasets.Dataset		,       **SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





    _lowerCAmelCase				=      dataset.filter(**SCREAMING_SNAKE_CASE_    )


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      {"num examples": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _lowerCAmelCase				=      datasets.Features({"text": datasets.Value("string"    ), "numbers": datasets.Value("float32"    )}    )
        _lowerCAmelCase				=      generate_example_dataset(
            os.path.join(SCREAMING_SNAKE_CASE_		,       "dataset.arrow"    )		,       SCREAMING_SNAKE_CASE_		,       num_examples=SCREAMING_SNAKE_CASE_    )

        _lowerCAmelCase				=      transformers.AutoTokenizer.from_pretrained("bert-base-cased"		,       use_fast=SCREAMING_SNAKE_CASE_    )

        def tokenize(SCREAMING_SNAKE_CASE_  :					int    ):
            return tokenizer(examples["text"]    )

        _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_    )

        _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_		,       batched=SCREAMING_SNAKE_CASE_    )

        _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_		,       function=lambda SCREAMING_SNAKE_CASE_    : None		,       batched=SCREAMING_SNAKE_CASE_    )

        with dataset.formatted_as(type="numpy"    ):
            _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_		,       function=lambda SCREAMING_SNAKE_CASE_    : None		,       batched=SCREAMING_SNAKE_CASE_    )

        with dataset.formatted_as(type="pandas"    ):
            _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_		,       function=lambda SCREAMING_SNAKE_CASE_    : None		,       batched=SCREAMING_SNAKE_CASE_    )

        with dataset.formatted_as(type="torch"		,       columns="numbers"    ):
            _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_		,       function=lambda SCREAMING_SNAKE_CASE_    : None		,       batched=SCREAMING_SNAKE_CASE_    )

        with dataset.formatted_as(type="tensorflow"		,       columns="numbers"    ):
            _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_		,       function=lambda SCREAMING_SNAKE_CASE_    : None		,       batched=SCREAMING_SNAKE_CASE_    )

        _lowerCAmelCase				=      map(SCREAMING_SNAKE_CASE_		,       function=SCREAMING_SNAKE_CASE_		,       batched=SCREAMING_SNAKE_CASE_    )

        _lowerCAmelCase				=      filter(SCREAMING_SNAKE_CASE_    )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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









'''simple docstring'''


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
       from transformers import TvltImageProcessor

if is_speech_available():
       from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      "ZinengTang/tvlt-base"
     _lowerCAmelCase				=      tempfile.mkdtemp()




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Any:
     return TvltImageProcessor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							str:
     return TvltFeatureExtractor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     shutil.rmtree(self.tmpdirname		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)
     processor.save_pretrained(self.tmpdirname		)
     _lowerCAmelCase				=      TvltProcessor.from_pretrained(self.tmpdirname		)

     self.assertIsInstance(processor.feature_extractor			,     _lowerCAmelCase		)
     self.assertIsInstance(processor.image_processor			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)

     _lowerCAmelCase				=      feature_extractor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     return_tensors="np"		)

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




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      image_processor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(images=_lowerCAmelCase			,     return_tensors="np"		)

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




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)
     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     images=_lowerCAmelCase		)

     self.assertListEqual(list(inputs.keys()		)			,     ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

     # test if it raises when no input is passed
     with pytest.raises(_lowerCAmelCase		):
         processor()





 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

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


