





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

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



def _a				(	lowerCamelCase:   str )	->						List[List[ImageInput]]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				(list, tuple) ) and isinstance(videos[0]					,				(list, tuple) ) and is_valid_image(videos[0][0] ):
            return videos

      elif isinstance(lowerCamelCase					,				(list, tuple) ) and is_valid_image(videos[0] ):
            return [videos]

      elif is_valid_image(lowerCamelCase ):
            return [[videos]]

      raise ValueError(F"""Could not make batched video from {videos}""" )







class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = ["""pixel_values"""]




   def __init__(self	:str    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:PILImageResampling = PILImageResampling.BILINEAR    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Union[int, float] = 1 / 255    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   **_UpperCamelCase	:List[Any]    ,   )->					None:
         super().__init__(**_UpperCamelCase )
         __A          =				size if size is not None else {'''shortest_edge''': 256}
         __A          =				get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase )
         __A          =				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __A          =				get_size_dict(_UpperCamelCase    ,   param_name='''crop_size''' )

         __A          =				do_resize
         __A          =				size
         __A          =				do_center_crop
         __A          =				crop_size
         __A          =				resample
         __A          =				do_rescale
         __A          =				rescale_factor
         __A          =				offset
         __A          =				do_normalize
         __A          =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __A          =				image_std if image_std is not None else IMAGENET_STANDARD_STD




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Dict[str, int]    ,   _UpperCamelCase	:PILImageResampling = PILImageResampling.BILINEAR    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:Optional[int]    ,   )->					np.ndarray:
         __A          =				get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase )
         if "shortest_edge" in size:
               __A          =				get_resize_output_image_size(_UpperCamelCase    ,   size['''shortest_edge''']    ,   default_to_square=_UpperCamelCase )
         elif "height" in size and "width" in size:
               __A          =				(size['''height'''], size['''width'''])
         else:
               raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
         return resize(_UpperCamelCase    ,   size=_UpperCamelCase    ,   resample=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Dict[str, int]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:Any    ,   )->					np.ndarray:
         __A          =				get_size_dict(_UpperCamelCase )
         if "height" not in size or "width" not in size:
               raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
         return center_crop(_UpperCamelCase    ,   size=(size['''height'''], size['''width'''])    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Union[int, float]    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:Any    ,   )->					List[str]:
         __A          =				image.astype(np.floataa )
         if offset:
               __A          =				image - (scale / 2)
         return rescale(_UpperCamelCase    ,   scale=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Union[float, List[float]]    ,   _UpperCamelCase	:Union[float, List[float]]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:List[Any]    ,   )->					np.ndarray:
         return normalize(_UpperCamelCase    ,   mean=_UpperCamelCase    ,   std=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:ImageInput    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:PILImageResampling = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:float = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[ChannelDimension] = ChannelDimension.FIRST    ,   )->					np.ndarray:
         if do_resize and size is None or resample is None:
               raise ValueError('''Size and resample 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.''' )

         if offset and not do_rescale:
               raise ValueError('''For offset, do_rescale must also be set to True.''' )

         # All transformations expect numpy arrays.
         __A          =				to_numpy_array(_UpperCamelCase )

         if do_resize:
               __A          =				self.resize(image=_UpperCamelCase    ,   size=_UpperCamelCase    ,   resample=_UpperCamelCase )

         if do_center_crop:
               __A          =				self.center_crop(_UpperCamelCase    ,   size=_UpperCamelCase )

         if do_rescale:
               __A          =				self.rescale(image=_UpperCamelCase    ,   scale=_UpperCamelCase    ,   offset=_UpperCamelCase )

         if do_normalize:
               __A          =				self.normalize(image=_UpperCamelCase    ,   mean=_UpperCamelCase    ,   std=_UpperCamelCase )

         __A          =				to_channel_dimension_format(_UpperCamelCase    ,   _UpperCamelCase )
         return image






   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:ImageInput    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:PILImageResampling = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:float = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[str, TensorType]] = None    ,   _UpperCamelCase	:ChannelDimension = ChannelDimension.FIRST    ,   **_UpperCamelCase	:List[str]    ,   )->					PIL.Image.Image:
         __A          =				do_resize if do_resize is not None else self.do_resize
         __A          =				resample if resample is not None else self.resample
         __A          =				do_center_crop if do_center_crop is not None else self.do_center_crop
         __A          =				do_rescale if do_rescale is not None else self.do_rescale
         __A          =				rescale_factor if rescale_factor is not None else self.rescale_factor
         __A          =				offset if offset is not None else self.offset
         __A          =				do_normalize if do_normalize is not None else self.do_normalize
         __A          =				image_mean if image_mean is not None else self.image_mean
         __A          =				image_std if image_std is not None else self.image_std

         __A          =				size if size is not None else self.size
         __A          =				get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase )
         __A          =				crop_size if crop_size is not None else self.crop_size
         __A          =				get_size_dict(_UpperCamelCase    ,   param_name='''crop_size''' )

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

         __A          =				make_batched(_UpperCamelCase )

         __A          =				[
             [
                 self._preprocess_image(
                     image=_UpperCamelCase    ,   do_resize=_UpperCamelCase    ,   size=_UpperCamelCase    ,   resample=_UpperCamelCase    ,   do_center_crop=_UpperCamelCase    ,   crop_size=_UpperCamelCase    ,   do_rescale=_UpperCamelCase    ,   rescale_factor=_UpperCamelCase    ,   offset=_UpperCamelCase    ,   do_normalize=_UpperCamelCase    ,   image_mean=_UpperCamelCase    ,   image_std=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   )
                 for img in video
             ]
             for video in videos
         ]

         __A          =				{'''pixel_values''': videos}
         return BatchFeature(data=_UpperCamelCase    ,   tensor_type=_UpperCamelCase )












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

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



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






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

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # 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(lowerCamelCase					,				'''wb''' ) as f:
            f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )


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





