






"""simple docstring"""







import json
import sys






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       with open(lowerCAmelCase	,      encoding="""utf-8"""		) as f:
              UpperCAmelCase	    =			json.load(lowerCAmelCase		)

       UpperCAmelCase	    =			["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

       for benchmark_name in sorted(lowerCAmelCase		):
              UpperCAmelCase	    =			results[benchmark_name]

              UpperCAmelCase	    =			benchmark_name.split("""/"""		)[-1]
              output_md.append(F'''### Benchmark: {benchmark_file_name}'''		)

              UpperCAmelCase	    =			"""| metric |"""
              UpperCAmelCase	    =			"""|--------|"""
              UpperCAmelCase	    =			"""| new / old (diff) |"""
              for metric_name in sorted(lowerCAmelCase		):
                     UpperCAmelCase	    =			benchmark_res[metric_name]
                     UpperCAmelCase	    =			metric_vals["""new"""]
                     UpperCAmelCase	    =			metric_vals.get("""old"""	,      lowerCAmelCase		)
                     UpperCAmelCase	    =			metric_vals.get("""diff"""	,      lowerCAmelCase		)

                     UpperCAmelCase	    =			F''' {new_val:f}''' if isinstance(lowerCAmelCase	,      (int, float)		) else """None"""

                     if old_val is not None:
                            val_str += F''' / {old_val:f}''' if isinstance(lowerCAmelCase	,      (int, float)		) else "None"
                     if dif_val is not None:
                            val_str += F''' ({dif_val:f})''' if isinstance(lowerCAmelCase	,      (int, float)		) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append("""</details>"""		)

       with open(lowerCAmelCase	,      """w"""	,      encoding="""utf-8"""		) as f:
              f.writelines("""\n""".join(lowerCAmelCase		)		)


if __name__ == "__main__":
 lowerCAmelCase_	:    Optional[Any]            =      sys.argv[1]
 lowerCAmelCase_	:    List[Any]            =      sys.argv[2]

 format_json_to_md(input_json_file, output_md_file)








"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase_	:    Union[str, Any]            =      logging.get_logger(__name__)





class   UpperCamelCase_ (       a_  ):
  _A       :					List[str]				=	['pixel_values']



  def __init__(      self ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 0.9 ,		snake_case__ = PILImageResampling.BICUBIC ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = None ,		**snake_case__ ,		)			->			None:



         """simple docstring"""





         super().__init__(**snake_case__				)
         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 2_24}
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         UpperCAmelCase	    =			crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		param_name="""crop_size"""				)

         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size
         UpperCAmelCase	    =			crop_pct
         UpperCAmelCase	    =			resample
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			crop_size
         UpperCAmelCase	    =			do_rescale
         UpperCAmelCase	    =			rescale_factor
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase	    =			image_std if image_std is not None else IMAGENET_DEFAULT_STD



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



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
                raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
         if crop_pct is not None:
                if "shortest_edge" in size:
                       UpperCAmelCase	    =			int(size["""shortest_edge"""] / crop_pct				)
                elif "height" in size and "width" in size:
                       if size["height"] == size["width"]:
                              UpperCAmelCase	    =			int(size["""height"""] / crop_pct				)
                       else:
                              UpperCAmelCase	    =			(int(size["""height"""] / crop_pct				), int(size["""width"""] / crop_pct				))
                else:
                       raise ValueError("""Invalid size for resize: {}""".format(snake_case__				)				)

                UpperCAmelCase	    =			get_resize_output_image_size(snake_case__ ,		size=snake_case__ ,		default_to_square=snake_case__				)
         else:
                if "shortest_edge" in size:
                       UpperCAmelCase	    =			get_resize_output_image_size(snake_case__ ,		size=size["""shortest_edge"""] ,		default_to_square=snake_case__				)
                elif "height" in size and "width" in size:
                       UpperCAmelCase	    =			(size["""height"""], size["""width"""])
                else:
                       raise ValueError("""Invalid size for resize: {}""".format(snake_case__				)				)

         return resize(snake_case__ ,		size=snake_case__ ,		resample=snake_case__ ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__				)
         if "height" not in size or "width" not in size:
                raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''				)
         return center_crop(snake_case__ ,		size=(size["""height"""], size["""width"""]) ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = ChannelDimension.FIRST ,		**snake_case__ ,		)			->			PIL.Image.Image:



         """simple docstring"""





         UpperCAmelCase	    =			do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase	    =			crop_pct if crop_pct is not None else self.crop_pct
         UpperCAmelCase	    =			resample if resample is not None else self.resample
         UpperCAmelCase	    =			do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase	    =			do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase	    =			rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase	    =			do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase	    =			image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase	    =			image_std if image_std is not None else self.image_std

         UpperCAmelCase	    =			size if size is not None else self.size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         UpperCAmelCase	    =			crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		param_name="""crop_size"""				)

         UpperCAmelCase	    =			make_list_of_images(snake_case__				)

         if not valid_images(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 or resample is None:
                raise ValueError("""Size and resample must be specified if do_resize is True."""				)

         if do_center_crop and crop_pct is None:
                raise ValueError("""Crop_pct 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."""				)

         # All transformations expect numpy arrays.
         UpperCAmelCase	    =			[to_numpy_array(snake_case__				) for image in images]

         if do_resize:
                UpperCAmelCase	    =			[self.resize(image=snake_case__ ,		size=snake_case__ ,		crop_pct=snake_case__ ,		resample=snake_case__				) for image in images]

         if do_center_crop:
                UpperCAmelCase	    =			[self.center_crop(image=snake_case__ ,		size=snake_case__				) for image in images]

         if do_rescale:
                UpperCAmelCase	    =			[self.rescale(image=snake_case__ ,		scale=snake_case__				) for image in images]

         if do_normalize:
                UpperCAmelCase	    =			[self.normalize(image=snake_case__ ,		mean=snake_case__ ,		std=snake_case__				) for image in images]

         UpperCAmelCase	    =			[to_channel_dimension_format(snake_case__ ,		snake_case__				) for image in images]

         UpperCAmelCase	    =			{"""pixel_values""": images}
         return BatchFeature(data=snake_case__ ,		tensor_type=snake_case__				)
