


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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCAmelCase : Optional[int] 			=				logging.get_logger(__name__)


if is_vision_available():
		import PIL



class    UpperCAmelCase_	(							_a):
							'''simple docstring'''
							__UpperCamelCase      :     Dict							=					["pixel_values"]






							def __init__(     self ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = 1 / 255 ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = True ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




														super().__init__(**__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	Optional[Any]   	=		size if size is not None else {'''shortest_edge''': 224}
														UpperCamelCase    :	Dict   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	Any   	=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
														UpperCamelCase    :	Dict   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE ,					param_name='''crop_size'''						)

														UpperCamelCase    :	Any   	=		do_resize
														UpperCamelCase    :	Tuple   	=		size
														UpperCamelCase    :	Optional[Any]   	=		resample
														UpperCamelCase    :	Optional[Any]   	=		do_center_crop
														UpperCamelCase    :	Any   	=		crop_size
														UpperCamelCase    :	Dict   	=		do_rescale
														UpperCamelCase    :	Optional[int]   	=		rescale_factor
														UpperCamelCase    :	str   	=		do_normalize
														UpperCamelCase    :	Union[str, Any]   	=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
														UpperCamelCase    :	str   	=		image_std if image_std is not None else OPENAI_CLIP_STD
														UpperCamelCase    :	Optional[Any]   	=		do_convert_rgb






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




														UpperCamelCase    :	List[str]   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE						)
														if "shortest_edge" not in size:
																					raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
														UpperCamelCase    :	Optional[int]   	=		get_resize_output_image_size(__SCREAMING_SNAKE_CASE ,					size=size['''shortest_edge'''] ,					default_to_square=__SCREAMING_SNAKE_CASE						)
														return resize(__SCREAMING_SNAKE_CASE ,					size=__SCREAMING_SNAKE_CASE ,					resample=__SCREAMING_SNAKE_CASE ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




														UpperCamelCase    :	Tuple   	=		get_size_dict(__SCREAMING_SNAKE_CASE						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""						)
														return center_crop(__SCREAMING_SNAKE_CASE ,					size=(size['''height'''], size['''width''']) ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




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






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




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






							def  _lowercase   (     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 = 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 ,					):




														"""simple docstring"""




														UpperCamelCase    :	int   	=		do_resize if do_resize is not None else self.do_resize
														UpperCamelCase    :	Union[str, Any]   	=		size if size is not None else self.size
														UpperCamelCase    :	int   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					param_name='''size''' ,					default_to_square=__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	Any   	=		resample if resample is not None else self.resample
														UpperCamelCase    :	str   	=		do_center_crop if do_center_crop is not None else self.do_center_crop
														UpperCamelCase    :	Any   	=		crop_size if crop_size is not None else self.crop_size
														UpperCamelCase    :	Union[str, Any]   	=		get_size_dict(__SCREAMING_SNAKE_CASE ,					param_name='''crop_size''' ,					default_to_square=__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	int   	=		do_rescale if do_rescale is not None else self.do_rescale
														UpperCamelCase    :	Tuple   	=		rescale_factor if rescale_factor is not None else self.rescale_factor
														UpperCamelCase    :	Optional[int]   	=		do_normalize if do_normalize is not None else self.do_normalize
														UpperCamelCase    :	int   	=		image_mean if image_mean is not None else self.image_mean
														UpperCamelCase    :	Tuple   	=		image_std if image_std is not None else self.image_std
														UpperCamelCase    :	Union[str, Any]   	=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														UpperCamelCase    :	Any   	=		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_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

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

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					UpperCamelCase    :	Any   	=		[convert_to_rgb(__SCREAMING_SNAKE_CASE						) for image in images]

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

														if do_resize:
																					UpperCamelCase    :	Union[str, Any]   	=		[self.resize(image=__SCREAMING_SNAKE_CASE ,					size=__SCREAMING_SNAKE_CASE ,					resample=__SCREAMING_SNAKE_CASE						) for image in images]

														if do_center_crop:
																					UpperCamelCase    :	str   	=		[self.center_crop(image=__SCREAMING_SNAKE_CASE ,					size=__SCREAMING_SNAKE_CASE						) for image in images]

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

														if do_normalize:
																					UpperCamelCase    :	Optional[int]   	=		[self.normalize(image=__SCREAMING_SNAKE_CASE ,					mean=__SCREAMING_SNAKE_CASE ,					std=__SCREAMING_SNAKE_CASE						) for image in images]

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

														UpperCamelCase    :	List[str]   	=		{'''pixel_values''': images}
														return BatchFeature(data=__SCREAMING_SNAKE_CASE ,					tensor_type=__SCREAMING_SNAKE_CASE						)





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		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()

