





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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

class 		UpperCamelCase__ (      __SCREAMING_SNAKE_CASE   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			["""pixel_values"""]






     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    Tuple = True						,lowerCamelCase__						:    Optional[Any] = None						,lowerCamelCase__						:    List[Any] = None						,lowerCamelCase__						:    Optional[Any] = PILImageResampling.BILINEAR						,lowerCamelCase__						:    Optional[int] = True						,lowerCamelCase__						:    List[Any] = 1 / 255						,lowerCamelCase__						:    Tuple = True						,lowerCamelCase__						:    List[str] = None						,lowerCamelCase__						:    Any = None						,**lowerCamelCase__						:    Optional[int]						,)     ->		int:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						size if size is not None else {"""shortest_edge""": 384}
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						do_resize
           SCREAMING_SNAKE_CASE		=						size
           # Default value set here for backwards compatibility where the value in config is None
           SCREAMING_SNAKE_CASE		=						crop_pct if crop_pct is not None else 224 / 256
           SCREAMING_SNAKE_CASE		=						resample
           SCREAMING_SNAKE_CASE		=						do_rescale
           SCREAMING_SNAKE_CASE		=						rescale_factor
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           SCREAMING_SNAKE_CASE		=						image_std if image_std is not None else IMAGENET_STANDARD_STD






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    List[str]						,lowerCamelCase__						:    Any						,lowerCamelCase__						:    int = PILImageResampling.BICUBIC						,lowerCamelCase__						:    Tuple = None						,**lowerCamelCase__						:    str						,)     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__       )
           if "shortest_edge" not in size:
                 raise ValueError(F"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""       )
           SCREAMING_SNAKE_CASE		=						size["""shortest_edge"""]

           if shortest_edge < 384:
                 # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                 SCREAMING_SNAKE_CASE		=						int(shortest_edge / crop_pct       )
                 SCREAMING_SNAKE_CASE		=						get_resize_output_image_size(lowerCamelCase__						,size=lowerCamelCase__						,default_to_square=lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						resize(image=lowerCamelCase__						,size=lowerCamelCase__						,resample=lowerCamelCase__						,data_format=lowerCamelCase__						,**lowerCamelCase__       )
                 # then crop to (shortest_edge, shortest_edge)
                 return center_crop(image=lowerCamelCase__						,size=(shortest_edge, shortest_edge)						,data_format=lowerCamelCase__						,**lowerCamelCase__       )
           else:
                 # warping (no cropping) when evaluated at 384 or larger
                 return resize(
                     lowerCamelCase__						,size=(shortest_edge, shortest_edge)						,resample=lowerCamelCase__						,data_format=lowerCamelCase__						,**lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    Optional[int]						,lowerCamelCase__						:    Optional[Any]						,lowerCamelCase__						:    str = None						,**lowerCamelCase__						:    int						,)     ->		List[Any]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    Union[str, Any]						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    Tuple						,lowerCamelCase__						:    Any = None						,**lowerCamelCase__						:    Dict						,)     ->		Any:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    Dict						,lowerCamelCase__						:    List[Any] = None						,lowerCamelCase__						:    Optional[int] = None						,lowerCamelCase__						:    Optional[Any] = None						,lowerCamelCase__						:    int = None						,lowerCamelCase__						:    Union[str, Any] = None						,lowerCamelCase__						:    Any = None						,lowerCamelCase__						:    Optional[int] = None						,lowerCamelCase__						:    Any = None						,lowerCamelCase__						:    str = None						,lowerCamelCase__						:    List[Any] = None						,lowerCamelCase__						:    Dict = ChannelDimension.FIRST						,**lowerCamelCase__						:    int						,)     ->		str:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						size if size is not None else self.size
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						make_list_of_images(lowerCamelCase__       )

           if not valid_images(lowerCamelCase__       ):
                 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_resize and size["shortest_edge"] < 384 and crop_pct is None:
                 raise ValueError("""crop_pct must be specified if size < 384."""       )

           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.
           SCREAMING_SNAKE_CASE		=						[to_numpy_array(lowerCamelCase__       ) for image in images]

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

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

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

           SCREAMING_SNAKE_CASE		=						[to_channel_dimension_format(lowerCamelCase__						,lowerCamelCase__       ) for image in images]

           SCREAMING_SNAKE_CASE		=						{"""pixel_values""": images}
           return BatchFeature(data=lowerCamelCase__						,tensor_type=lowerCamelCase__       )




import json
import sys





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 with open(lowerCamelCase,	encoding="""utf-8"""							) as f:
  lowerCamelCase		:	List[Any]							=      json.load(lowerCamelCase							)

 lowerCamelCase		:	Optional[Any]							=      ["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

 for benchmark_name in sorted(lowerCamelCase							):
  lowerCamelCase		:	List[Any]							=      results[benchmark_name]

  lowerCamelCase		:	Union[str, Any]							=      benchmark_name.split("""/"""							)[-1]
  output_md.append(F'''### Benchmark: {benchmark_file_name}'''							)

  lowerCamelCase		:	Any							=      """| metric |"""
  lowerCamelCase		:	str							=      """|--------|"""
  lowerCamelCase		:	List[Any]							=      """| new / old (diff) |"""
  for metric_name in sorted(lowerCamelCase							):
   lowerCamelCase		:	List[Any]							=      benchmark_res[metric_name]
   lowerCamelCase		:	Tuple							=      metric_vals["""new"""]
   lowerCamelCase		:	int							=      metric_vals.get("""old""",	lowerCamelCase							)
   lowerCamelCase		:	Dict							=      metric_vals.get("""diff""",	lowerCamelCase							)

   lowerCamelCase		:	Dict							=      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					   =sys.argv[1]
      _lowerCamelCase					   =sys.argv[2]

      format_json_to_md(input_json_file, output_md_file)




