

import json
import sys



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	with open(SCREAMING_SNAKE_CASE	,encoding="utf-8"      ) as f:
		UpperCAmelCase__:	Union[str, Any]									= json.load(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	Union[str, Any]									= ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

	for benchmark_name in sorted(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	Tuple									= results[benchmark_name]

		UpperCAmelCase__:	Optional[Any]									= benchmark_name.split("/"      )[-1]
		output_md.append(f"### Benchmark: {benchmark_file_name}"      )

		UpperCAmelCase__:	Union[str, Any]									= "| metric |"
		UpperCAmelCase__:	Union[str, Any]									= "|--------|"
		UpperCAmelCase__:	Optional[Any]									= "| new / old (diff) |"
		for metric_name in sorted(SCREAMING_SNAKE_CASE      ):
			UpperCAmelCase__:	List[Any]									= benchmark_res[metric_name]
			UpperCAmelCase__:	int									= metric_vals["new"]
			UpperCAmelCase__:	List[Any]									= metric_vals.get("old"	,SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Optional[int]									= metric_vals.get("diff"	,SCREAMING_SNAKE_CASE      )

			UpperCAmelCase__:	Optional[Any]									= f" {new_val:f}" if isinstance(SCREAMING_SNAKE_CASE	,(int, float)      ) else "None"

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

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

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

	output_md.append("</details>"      )

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


if __name__ == "__main__":
					_lowerCAmelCase  :      Union[str, Any]			 =sys.argv[1]
					_lowerCAmelCase  :      List[str]			 =sys.argv[2]

					format_json_to_md(input_json_file, output_md_file)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)