

'''simple docstring'''




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


_a				: Optional[Any] 							=						(30, 487)
_a				: Tuple 							=						(36, 1_024)
_a				: Optional[Any] 							=						(100, 100)
_a				: Optional[Any] 							=						100

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

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


@get_duration
def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   int       ,   SCREAMING_SNAKE_CASE      :   int       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]						):
  with ArrowWriter(features=SCREAMING_SNAKE_CASE       ,   path=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)						) as writer:
    for key, record in dummy_data:
      UpperCAmelCase													=	my_features.encode_example(SCREAMING_SNAKE_CASE						)
      writer.write(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	writer.finalize()


@get_duration
def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   int						):
  UpperCAmelCase													=	datasets.Dataset.from_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
  for _ in dataset:
    pass


@get_duration
def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Tuple       ,   SCREAMING_SNAKE_CASE      :   Union[str, Any]						):
  UpperCAmelCase													=	datasets.Dataset.from_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
  dataset.set_format('numpy'						)
  for _ in dataset:
    pass


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


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


@get_duration
def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   Tuple						):
  UpperCAmelCase													=	datasets.Dataset.from_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
  for col in feats:
    UpperCAmelCase													=	dataset[col]


@get_duration
def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   List[str]						):
  UpperCAmelCase													=	datasets.Dataset.from_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
  dataset.set_format('numpy'						)
  for col in feats:
    UpperCAmelCase													=	dataset[col]


def       lowerCamelCase__		(						):
  UpperCAmelCase													=	{}
  UpperCAmelCase													=	(
      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:
    UpperCAmelCase													=	datasets.Features({'image': ArrayaD(SCREAMING_SNAKE_CASE       ,   dtype='float32'						)}						)
    UpperCAmelCase													=	generate_examples(features=SCREAMING_SNAKE_CASE       ,   num_examples=SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	write(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    for read_func in read_functions:
      UpperCAmelCase													=	read_func(SCREAMING_SNAKE_CASE       ,   SCREAMING_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])}
    # )
    UpperCAmelCase													=	datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'						)						)						)}						)
    UpperCAmelCase													=	generate_examples(
        features=SCREAMING_SNAKE_CASE       ,   num_examples=SCREAMING_SNAKE_CASE       ,   seq_shapes={'image': SPEED_TEST_SHAPE}						)
    UpperCAmelCase													=	write(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    for read_func in read_functions:
      UpperCAmelCase													=	read_func(SCREAMING_SNAKE_CASE       ,   SCREAMING_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])}
    # )
    UpperCAmelCase													=	datasets.Features({'image': datasets.Sequence(datasets.Value('float32'						)						)}						)
    UpperCAmelCase													=	generate_examples(
        features=SCREAMING_SNAKE_CASE       ,   num_examples=SCREAMING_SNAKE_CASE       ,   seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}       ,   )
    UpperCAmelCase													=	write(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    for read_func in read_functions:
      UpperCAmelCase													=	read_func(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  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_array_xd()








'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor



class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def __init__(     self   , a_   , a_=7   , a_=3   , a_=1_8   , a_=3_0   , a_=4_0_0   , a_=True   , a_=None   , a_=True   , a_=None   , )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	size if size is not None else {'shortest_edge': 2_0}
    UpperCAmelCase													=	crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
    UpperCAmelCase													=	parent
    UpperCAmelCase													=	batch_size
    UpperCAmelCase													=	num_channels
    UpperCAmelCase													=	image_size
    UpperCAmelCase													=	min_resolution
    UpperCAmelCase													=	max_resolution
    UpperCAmelCase													=	do_resize
    UpperCAmelCase													=	size
    UpperCAmelCase													=	do_center_crop
    UpperCAmelCase													=	crop_size





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


    """simple docstring"""
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class        lowercase_	(		a ,       unittest.TestCase ):

  '''simple docstring'''





  __lowerCAmelCase					: Union[str, Any]      =   MobileNetVaImageProcessor if is_vision_available() else None
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	MobileNetVaImageProcessingTester(self     )
  @property
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    self.assertTrue(hasattr(a_   , 'do_resize'     )     )
    self.assertTrue(hasattr(a_   , 'size'     )     )
    self.assertTrue(hasattr(a_   , 'do_center_crop'     )     )
    self.assertTrue(hasattr(a_   , 'center_crop'     )     )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 2_0}     )
    self.assertEqual(image_processor.crop_size   , {'height': 1_8, 'width': 1_8}     )

    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict   , size=4_2   , crop_size=8_4     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 4_2}     )
    self.assertEqual(image_processor.crop_size   , {'height': 8_4, 'width': 8_4}     )
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    pass
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PIL images
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , Image.Image     )

    # Test not batched input
    UpperCAmelCase													=	image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processing(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random numpy tensors
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_   , numpify=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , np.ndarray     )

    # Test not batched input
    UpperCAmelCase													=	image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processing(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )





  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PyTorch tensors
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_   , torchify=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , torch.Tensor     )

    # Test not batched input
    UpperCAmelCase													=	image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processing(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_images.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )


