





"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCamelCase_          =	logging.get_logger(__name__)


if is_vision_available():
  import PIL






class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						['''pixel_values''']




       def __init__(		self	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = 1 / 2_55	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   None:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						size if size is not None else {'''height''': 25_60, '''width''': 19_20}
             if isinstance(SCREAMING_SNAKE_CASE__	,(tuple, list)							):
                   # The previous feature extractor size parameter was in (width, height) format
                   __SCREAMING_SNAKE_CASE     :Dict	       =						size[::-1]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :int	       =						do_resize
             __SCREAMING_SNAKE_CASE     :Dict	       =						size
             __SCREAMING_SNAKE_CASE     :Tuple	       =						resample
             __SCREAMING_SNAKE_CASE     :str	       =						do_thumbnail
             __SCREAMING_SNAKE_CASE     :str	       =						do_align_long_axis
             __SCREAMING_SNAKE_CASE     :List[str]	       =						do_pad
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						do_rescale
             __SCREAMING_SNAKE_CASE     :List[str]	       =						rescale_factor
             __SCREAMING_SNAKE_CASE     :List[str]	       =						do_normalize
             __SCREAMING_SNAKE_CASE     :List[str]	       =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						image_std if image_std is not None else IMAGENET_STANDARD_STD




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_image_size(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Tuple	       =						size['''height'''], size['''width''']

             if (output_width < output_height and input_width > input_height) or (
                 output_width > output_height and input_width < input_height
             ):
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						np.rotaa(SCREAMING_SNAKE_CASE__	,3							)

             if data_format is not None:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						to_channel_dimension_format(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

             return image




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = None	,)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						size['''height'''], size['''width''']
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						get_image_size(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :List[str]	       =						output_width - input_width
             __SCREAMING_SNAKE_CASE     :List[str]	       =						output_height - input_height

             if random_padding:
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						np.random.randint(low=0	,high=delta_height + 1							)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						np.random.randint(low=0	,high=delta_width + 1							)
             else:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						delta_height // 2
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						delta_width // 2

             __SCREAMING_SNAKE_CASE     :Tuple	       =						delta_height - pad_top
             __SCREAMING_SNAKE_CASE     :Dict	       =						delta_width - pad_left

             __SCREAMING_SNAKE_CASE     :int	       =						((pad_top, pad_bottom), (pad_left, pad_right))
             return pad(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''							)
             return self.pad_image(*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_image_size(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						size['''height'''], size['''width''']

             # We always resize to the smallest of either the input or output size.
             __SCREAMING_SNAKE_CASE     :Any	       =						min(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						min(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

             if height == input_height and width == input_width:
                   return image

             if input_height > input_width:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						int(input_width * height / input_height							)
             elif input_width > input_height:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						int(input_height * width / input_width							)

             return resize(
                 SCREAMING_SNAKE_CASE__	,size=(height, width)	,resample=SCREAMING_SNAKE_CASE__	,reducing_gap=2.0	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :int	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						min(size['''height''']	,size['''width''']							)
             __SCREAMING_SNAKE_CASE     :int	       =						get_resize_output_image_size(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,default_to_square=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						resize(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,resample=SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)
             return resized_image




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   Union[str, Any]:





             """simple docstring"""






             return rescale(SCREAMING_SNAKE_CASE__	,scale=SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   np.ndarray:





             """simple docstring"""






             return normalize(SCREAMING_SNAKE_CASE__	,mean=SCREAMING_SNAKE_CASE__	,std=SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST	,**SCREAMING_SNAKE_CASE__	,)    ->   PIL.Image.Image:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						do_resize if do_resize is not None else self.do_resize
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						size if size is not None else self.size
             if isinstance(SCREAMING_SNAKE_CASE__	,(tuple, list)							):
                   # Previous feature extractor had size in (width, height) format
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						size[::-1]
             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						resample if resample is not None else self.resample
             __SCREAMING_SNAKE_CASE     :Any	       =						do_thumbnail if do_thumbnail is not None else self.do_thumbnail
             __SCREAMING_SNAKE_CASE     :Dict	       =						do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
             __SCREAMING_SNAKE_CASE     :str	       =						do_pad if do_pad is not None else self.do_pad
             __SCREAMING_SNAKE_CASE     :Any	       =						do_rescale if do_rescale is not None else self.do_rescale
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						do_normalize if do_normalize is not None else self.do_normalize
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						image_mean if image_mean is not None else self.image_mean
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						image_std if image_std is not None else self.image_std

             __SCREAMING_SNAKE_CASE     :str	       =						make_list_of_images(SCREAMING_SNAKE_CASE__							)

             if not valid_images(SCREAMING_SNAKE_CASE__							):
                   raise ValueError(
                       '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                       '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

             if do_resize and size is None:
                   raise ValueError('''Size must be specified if do_resize is True.'''							)

             if do_rescale and rescale_factor is None:
                   raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

             if do_pad and size is None:
                   raise ValueError('''Size must be specified if do_pad is True.'''							)

             if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

             # All transformations expect numpy arrays.
             __SCREAMING_SNAKE_CASE     :int	       =						[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

             if do_align_long_axis:
                   __SCREAMING_SNAKE_CASE     :int	       =						[self.align_long_axis(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_resize:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.resize(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,resample=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_thumbnail:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						[self.thumbnail(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_pad:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						[self.pad_image(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,random_padding=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_rescale:
                   __SCREAMING_SNAKE_CASE     :int	       =						[self.rescale(image=SCREAMING_SNAKE_CASE__	,scale=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_normalize:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						[self.normalize(image=SCREAMING_SNAKE_CASE__	,mean=SCREAMING_SNAKE_CASE__	,std=SCREAMING_SNAKE_CASE__							) for image in images]

             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) for image in images]

             __SCREAMING_SNAKE_CASE     :Any	       =						{'''pixel_values''': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__	,tensor_type=SCREAMING_SNAKE_CASE__							)






"""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


lowerCamelCase_          =	(3_0, 4_8_7)
lowerCamelCase_          =	(3_6, 1_0_2_4)
lowerCamelCase_          =	(1_0_0, 1_0_0)
lowerCamelCase_          =	1_0_0

lowerCamelCase_          =	datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

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



@get_duration
def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	List[str]				,     a_  :	List[str]							)    ->   List[str]:
      with ArrowWriter(features=a_				,     path=os.path.join(a_				,     '''beta.arrow'''							)							) as writer:
            for key, record in dummy_data:
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						my_features.encode_example(a_							)
                  writer.write(a_							)
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						writer.finalize()



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[Any]							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	List[str]				,     a_  :	List[str]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for _ in dataset:
            pass



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



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



@get_duration
def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	Optional[Any]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :Tuple	       =						dataset[col]



@get_duration
def   __lowerCamelCase     (							a_  :	int				,     a_  :	List[str]							)    ->   str:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						dataset[col]



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						{}
      __SCREAMING_SNAKE_CASE     :int	       =						(
          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:
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': ArrayaD(a_				,     dtype='''float32'''							)}							)
            __SCREAMING_SNAKE_CASE     :Any	       =						generate_examples(features=a_				,     num_examples=a_							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      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])}
            # )
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''							)							)							)}							)
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': SPEED_TEST_SHAPE}							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :Tuple	       =						read_func(a_				,     a_							)

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

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


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