





import json
import sys






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Optional[int]     ):
		with open(UpperCamelCase__       , encoding="""utf-8"""     ) as f:
				SCREAMING_SNAKE_CASE__								=   json.load(UpperCamelCase__     )

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

		for benchmark_name in sorted(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__								=   results[benchmark_name]

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

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

						SCREAMING_SNAKE_CASE__								=   f''' {new_val:f}''' if isinstance(UpperCamelCase__       , (int, float)     ) else """None"""

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

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

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

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

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


if __name__ == "__main__":
	_lowerCamelCase 					=      sys.argv[1]
	_lowerCamelCase 					=      sys.argv[2]

	format_json_to_md(input_json_file, output_md_file)






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       42




class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__     ):


							@register_to_config
							def __init__(				self     :Union[str, Any]				,					__A     :int = 3				,					__A     :int = 3				,					__A     :Tuple[str] = ("DownEncoderBlock2D",)				,					__A     :Tuple[str] = ("UpDecoderBlock2D",)				,					__A     :Tuple[int] = (64,)				,					__A     :int = 1				,					__A     :str = "silu"				,					__A     :int = 3				,					__A     :int = 32				,					__A     :int = 256				,					__A     :int = 32				,					__A     :Optional[int] = None				,					__A     :float = 0.1_8_2_1_5				,					__A     :str = "group"				,					) -> Any:



									"""simple docstring"""



									super().__init__()

									# pass init params to Encoder
									SCREAMING_SNAKE_CASE__								=   Encoder(
									    in_channels=__A				,					out_channels=__A				,					down_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					double_z=__A				,					)

									SCREAMING_SNAKE_CASE__								=   vq_embed_dim if vq_embed_dim is not None else latent_channels

									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)
									SCREAMING_SNAKE_CASE__								=   VectorQuantizer(__A				,					__A				,					beta=0.2_5				,					remap=__A				,					sane_index_shape=__A		)
									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)

									# pass init params to Decoder
									SCREAMING_SNAKE_CASE__								=   Decoder(
									    in_channels=__A				,					out_channels=__A				,					up_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					norm_type=__A				,					)


							@apply_forward_hook
							def   _snake_case	(				self     :Union[str, Any]				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> VQEncoderOutput:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.encoder(__A		)
									SCREAMING_SNAKE_CASE__								=   self.quant_conv(__A		)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=__A		)


							@apply_forward_hook
							def   _snake_case	(				self     :Tuple				,					__A     :torch.FloatTensor				,					__A     :bool = False				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									if not force_not_quantize:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.quantize(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   h
									SCREAMING_SNAKE_CASE__								=   self.post_quant_conv(__A		)
									SCREAMING_SNAKE_CASE__								=   self.decoder(__A				,					quant if self.config.norm_type == """spatial""" else None		)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)


							def   _snake_case	(				self     :int				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   sample
									SCREAMING_SNAKE_CASE__								=   self.encode(__A		).latents
									SCREAMING_SNAKE_CASE__								=   self.decode(__A		).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)