
"""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			):



						'''simple docstring'''


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      List[str]:
							A        =      'ZinengTang/tvlt-base'
							A        =      tempfile.mkdtemp()


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,**A_   :					Optional[Any]					)   ->      List[Any]:
							return TvltImageProcessor.from_pretrained(self.checkpoint     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,**A_   :					int					)   ->      Optional[int]:
							return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      List[Any]:
							shutil.rmtree(self.tmpdirname					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Optional[Any]:
							A        =      self.get_image_processor()
							A        =      self.get_feature_extractor()

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

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Any:
							A        =      self.get_image_processor()
							A        =      self.get_feature_extractor()

							A        =      TvltProcessor(image_processor=A_     ,feature_extractor=A_					)

							A        =      np.ones([1_2000]					)

							A        =      feature_extractor(A_     ,return_tensors='np'					)
							A        =      processor(audio=A_     ,return_tensors='np'					)

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      Tuple:
							A        =      self.get_image_processor()
							A        =      self.get_feature_extractor()

							A        =      TvltProcessor(image_processor=A_     ,feature_extractor=A_					)

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

							A        =      image_processor(A_     ,return_tensors='np'					)
							A        =      processor(images=A_     ,return_tensors='np'					)

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      int:
							A        =      self.get_image_processor()
							A        =      self.get_feature_extractor()

							A        =      TvltProcessor(image_processor=A_     ,feature_extractor=A_					)

							A        =      np.ones([1_2000]					)
							A        =      np.ones([3, 224, 224]					)

							A        =      processor(audio=A_     ,images=A_					)

							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(A_					):
								processor()




						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Dict:
							A        =      self.get_image_processor()
							A        =      self.get_feature_extractor()

							A        =      TvltProcessor(image_processor=A_     ,feature_extractor=A_					)

							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'     ,)






"""simple docstring"""



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


a_ =			50000
a_ =			5000

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







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Tuple						)   ->Tuple:



    '''simple docstring'''







    for i in range(snake_case_						):
        __A    :						int						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Optional[Any]				,snake_case_  :  int						)   ->Tuple:



    '''simple docstring'''







    for i in range(0				,len(snake_case_						)				,snake_case_						):
        __A    :						List[str]						 =     dataset[i : i + batch_size]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  List[Any]				,snake_case_  :  Any						)   ->int:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(snake_case_						):
            __A    :						Union[str, Any]						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Any				,snake_case_  :  Union[str, Any]				,snake_case_  :  Optional[int]						)   ->Union[str, Any]:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(0				,snake_case_				,snake_case_						):
            __A    :						Dict						 =     dataset[i : i + batch_size]







def 		__lowercase     (   )   ->Optional[int]:



    '''simple docstring'''







    __A    :						int						 =     {'''num examples''': SPEED_TEST_N_EXAMPLES}
    __A    :						Optional[int]						 =     [
        (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}),
    ]

    __A    :						int						 =     [
        (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'''						)
        __A    :						Any						 =     datasets.Features(
            {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
        __A    :						List[Any]						 =     generate_example_dataset(
            os.path.join(snake_case_				,'''dataset.arrow'''						)				,snake_case_				,num_examples=snake_case_				,seq_shapes={'''list''': (100,)}				,)
        print('''first set of iterations'''						)
        for func, kwargs in functions:
            print(func.__name__				,str(snake_case_						)						)
            __A    :						Dict						 =     func(snake_case_				,**snake_case_						)

        print('''shuffling dataset'''						)
        __A    :						int						 =     dataset.shuffle()
        print('''Second set of iterations (after shuffling'''						)
        for func, kwargs in functions_shuffled:
            print('''shuffled '''				,func.__name__				,str(snake_case_						)						)
            __A    :						Optional[Any]						 =     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_iterating()




