

import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 							__a       (					__A ,       __A=1	):
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("."	)[n_shave_prefix_segments:]	)
    else:
        return ".".join(path.split("."	)[:n_shave_prefix_segments]	)





def 							__a       (					__A ,       __A=0	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for old_item in old_list:
        SCREAMING_SNAKE_CASE__					     =						old_item
        SCREAMING_SNAKE_CASE__					     =						new_item.replace("block." ,       "resnets."	)
        SCREAMING_SNAKE_CASE__					     =						new_item.replace("conv_shorcut" ,       "conv1"	)
        SCREAMING_SNAKE_CASE__					     =						new_item.replace("in_shortcut" ,       "conv_shortcut"	)
        SCREAMING_SNAKE_CASE__					     =						new_item.replace("temb_proj" ,       "time_emb_proj"	)

        SCREAMING_SNAKE_CASE__					     =						shave_segments(__lowerCAmelCase ,       n_shave_prefix_segments=__lowerCAmelCase	)

        mapping.append({"old": old_item, "new": new_item}	)

    return mapping





def 							__a       (					__A ,       __A=0 ,       __A=False	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for old_item in old_list:
        SCREAMING_SNAKE_CASE__					     =						old_item

        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
            SCREAMING_SNAKE_CASE__					     =						new_item.replace("attn" ,       "attentions"	)
        SCREAMING_SNAKE_CASE__					     =						new_item.replace(".k." ,       ".key."	)
        SCREAMING_SNAKE_CASE__					     =						new_item.replace(".v." ,       ".value."	)
        SCREAMING_SNAKE_CASE__					     =						new_item.replace(".q." ,       ".query."	)

        SCREAMING_SNAKE_CASE__					     =						new_item.replace("proj_out" ,       "proj_attn"	)
        SCREAMING_SNAKE_CASE__					     =						new_item.replace("norm" ,       "group_norm"	)

        SCREAMING_SNAKE_CASE__					     =						shave_segments(__lowerCAmelCase ,       n_shave_prefix_segments=__lowerCAmelCase	)
        mapping.append({"old": old_item, "new": new_item}	)

    return mapping





def 							__a       (					__A ,       __A ,       __A ,       __A=None ,       __A=None ,       __A=None	):
    assert isinstance(__lowerCAmelCase ,       __lowerCAmelCase	), "Paths should be a list of dicts containing 'old' and 'new' keys."

    if attention_paths_to_split is not None:
        if config is None:
            raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."	)

        for path, path_map in attention_paths_to_split.items():
            SCREAMING_SNAKE_CASE__					     =						old_checkpoint[path]
            SCREAMING_SNAKE_CASE__					     =						old_tensor.shape[0] // 3

            SCREAMING_SNAKE_CASE__					     =						(-1, channels) if len(old_tensor.shape	) == 3 else (-1)

            SCREAMING_SNAKE_CASE__					     =						old_tensor.shape[0] // config.get("num_head_channels" ,       1	) // 3

            SCREAMING_SNAKE_CASE__					     =						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
            SCREAMING_SNAKE_CASE__					     =						old_tensor.split(channels // num_heads ,       dim=1	)

            SCREAMING_SNAKE_CASE__					     =						query.reshape(__lowerCAmelCase	).squeeze()
            SCREAMING_SNAKE_CASE__					     =						key.reshape(__lowerCAmelCase	).squeeze()
            SCREAMING_SNAKE_CASE__					     =						value.reshape(__lowerCAmelCase	).squeeze()

    for path in paths:
        SCREAMING_SNAKE_CASE__					     =						path["new"]

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        SCREAMING_SNAKE_CASE__					     =						new_path.replace("down." ,       "down_blocks."	)
        SCREAMING_SNAKE_CASE__					     =						new_path.replace("up." ,       "up_blocks."	)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                SCREAMING_SNAKE_CASE__					     =						new_path.replace(replacement["old"] ,       replacement["new"]	)

        if "attentions" in new_path:
            SCREAMING_SNAKE_CASE__					     =						old_checkpoint[path["old"]].squeeze()
        else:
            SCREAMING_SNAKE_CASE__					     =						old_checkpoint[path["old"]]





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						{}

    SCREAMING_SNAKE_CASE__					     =						checkpoint["temb.dense.0.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["temb.dense.0.bias"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["temb.dense.1.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["temb.dense.1.bias"]

    SCREAMING_SNAKE_CASE__					     =						checkpoint["norm_out.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["norm_out.bias"]

    SCREAMING_SNAKE_CASE__					     =						checkpoint["conv_in.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["conv_in.bias"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["conv_out.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["conv_out.bias"]

    SCREAMING_SNAKE_CASE__					     =						len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "down" in layer}	)
    SCREAMING_SNAKE_CASE__					     =						{
        layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)
    }

    SCREAMING_SNAKE_CASE__					     =						len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "up" in layer}	)
    SCREAMING_SNAKE_CASE__					     =						{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)}

    for i in range(__lowerCAmelCase	):
        SCREAMING_SNAKE_CASE__					     =						(i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						checkpoint[
                f"down.{i}.downsample.op.weight"
            ]
            SCREAMING_SNAKE_CASE__					     =						checkpoint[f"down.{i}.downsample.op.bias"]
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

        if any("block" in layer for layer in down_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       2	).split("."	)[:2]	) for layer in down_blocks[i] if "block" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                for layer_id in range(__lowerCAmelCase	)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]	):
                    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase	)

        if any("attn" in layer for layer in down_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       2	).split("."	)[:2]	) for layer in down_blocks[i] if "attn" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                for layer_id in range(__lowerCAmelCase	)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]	):
                    SCREAMING_SNAKE_CASE__					     =						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       config=__lowerCAmelCase	)

    SCREAMING_SNAKE_CASE__					     =						[key for key in checkpoint if "mid.block_1" in key]
    SCREAMING_SNAKE_CASE__					     =						[key for key in checkpoint if "mid.block_2" in key]
    SCREAMING_SNAKE_CASE__					     =						[key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(__lowerCAmelCase	)
    assign_to_checkpoint(
        __lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,       )

    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(__lowerCAmelCase	)
    assign_to_checkpoint(
        __lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,       )

    SCREAMING_SNAKE_CASE__					     =						renew_attention_paths(__lowerCAmelCase ,       in_mid=__lowerCAmelCase	)
    assign_to_checkpoint(
        __lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,       )

    for i in range(__lowerCAmelCase	):
        SCREAMING_SNAKE_CASE__					     =						num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						checkpoint[
                f"up.{i}.upsample.conv.weight"
            ]
            SCREAMING_SNAKE_CASE__					     =						checkpoint[f"up.{i}.upsample.conv.bias"]

        if any("block" in layer for layer in up_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       2	).split("."	)[:2]	) for layer in up_blocks[i] if "block" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1	):
                    SCREAMING_SNAKE_CASE__					     =						{"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[replace_indices]	)

        if any("attn" in layer for layer in up_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       2	).split("."	)[:2]	) for layer in up_blocks[i] if "attn" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1	):
                    SCREAMING_SNAKE_CASE__					     =						{"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    SCREAMING_SNAKE_CASE__					     =						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[replace_indices]	)

    SCREAMING_SNAKE_CASE__					     =						{k.replace("mid_new_2" ,       "mid_block"	): v for k, v in new_checkpoint.items()}
    return new_checkpoint





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						{}

    SCREAMING_SNAKE_CASE__					     =						checkpoint["encoder.norm_out.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["encoder.norm_out.bias"]

    SCREAMING_SNAKE_CASE__					     =						checkpoint["encoder.conv_in.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["encoder.conv_in.bias"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["encoder.conv_out.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["encoder.conv_out.bias"]

    SCREAMING_SNAKE_CASE__					     =						checkpoint["decoder.norm_out.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["decoder.norm_out.bias"]

    SCREAMING_SNAKE_CASE__					     =						checkpoint["decoder.conv_in.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["decoder.conv_in.bias"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["decoder.conv_out.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["decoder.conv_out.bias"]

    SCREAMING_SNAKE_CASE__					     =						len({".".join(layer.split("."	)[:3]	) for layer in checkpoint if "down" in layer}	)
    SCREAMING_SNAKE_CASE__					     =						{
        layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)
    }

    SCREAMING_SNAKE_CASE__					     =						len({".".join(layer.split("."	)[:3]	) for layer in checkpoint if "up" in layer}	)
    SCREAMING_SNAKE_CASE__					     =						{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)}

    for i in range(__lowerCAmelCase	):
        SCREAMING_SNAKE_CASE__					     =						(i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						checkpoint[
                f"encoder.down.{i}.downsample.conv.weight"
            ]
            SCREAMING_SNAKE_CASE__					     =						checkpoint[
                f"encoder.down.{i}.downsample.conv.bias"
            ]

        if any("block" in layer for layer in down_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       3	).split("."	)[:3]	) for layer in down_blocks[i] if "block" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                for layer_id in range(__lowerCAmelCase	)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]	):
                    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase	)

        if any("attn" in layer for layer in down_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       3	).split("."	)[:3]	) for layer in down_blocks[i] if "attn" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                for layer_id in range(__lowerCAmelCase	)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]	):
                    SCREAMING_SNAKE_CASE__					     =						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       config=__lowerCAmelCase	)

    SCREAMING_SNAKE_CASE__					     =						[key for key in checkpoint if "mid.block_1" in key]
    SCREAMING_SNAKE_CASE__					     =						[key for key in checkpoint if "mid.block_2" in key]
    SCREAMING_SNAKE_CASE__					     =						[key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(__lowerCAmelCase	)
    assign_to_checkpoint(
        __lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,       )

    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(__lowerCAmelCase	)
    assign_to_checkpoint(
        __lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,       )

    SCREAMING_SNAKE_CASE__					     =						renew_attention_paths(__lowerCAmelCase ,       in_mid=__lowerCAmelCase	)
    assign_to_checkpoint(
        __lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,       )

    for i in range(__lowerCAmelCase	):
        SCREAMING_SNAKE_CASE__					     =						num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						checkpoint[
                f"decoder.up.{i}.upsample.conv.weight"
            ]
            SCREAMING_SNAKE_CASE__					     =						checkpoint[
                f"decoder.up.{i}.upsample.conv.bias"
            ]

        if any("block" in layer for layer in up_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       3	).split("."	)[:3]	) for layer in up_blocks[i] if "block" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1	):
                    SCREAMING_SNAKE_CASE__					     =						{"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    SCREAMING_SNAKE_CASE__					     =						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[replace_indices]	)

        if any("attn" in layer for layer in up_blocks[i]	):
            SCREAMING_SNAKE_CASE__					     =						len(
                {".".join(shave_segments(__lowerCAmelCase ,       3	).split("."	)[:3]	) for layer in up_blocks[i] if "attn" in layer}	)
            SCREAMING_SNAKE_CASE__					     =						{
                layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__lowerCAmelCase	)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1	):
                    SCREAMING_SNAKE_CASE__					     =						{"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    SCREAMING_SNAKE_CASE__					     =						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(__lowerCAmelCase ,       __lowerCAmelCase ,       __lowerCAmelCase ,       additional_replacements=[replace_indices]	)

    SCREAMING_SNAKE_CASE__					     =						{k.replace("mid_new_2" ,       "mid_block"	): v for k, v in new_checkpoint.items()}
    SCREAMING_SNAKE_CASE__					     =						checkpoint["quant_conv.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["quant_conv.bias"]
    if "quantize.embedding.weight" in checkpoint:
        SCREAMING_SNAKE_CASE__					     =						checkpoint["quantize.embedding.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["post_quant_conv.weight"]
    SCREAMING_SNAKE_CASE__					     =						checkpoint["post_quant_conv.bias"]

    return new_checkpoint


if __name__ == "__main__":
  A__        =			argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  )

  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the architecture.",
  )

  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

  A__        =			parser.parse_args()
  A__        =			torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    A__        =			json.loads(f.read())

  # unet case
  A__        =			{key.split(".")[0] for key in checkpoint.keys()}
  if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    A__        =			convert_vq_autoenc_checkpoint(checkpoint, config)
  else:
    A__        =			convert_ddpm_checkpoint(checkpoint, config)

  if "ddpm" in config:
    del config["ddpm"]

  if config["_class_name"] == "VQModel":
    A__        =			VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  elif config["_class_name"] == "AutoencoderKL":
    A__        =			AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  else:
    A__        =			UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    A__        =			DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

    A__        =			DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)





import argparse
import json
import re
import string
import sys
from collections import Counter





def 							__a       (					__A	):

    def remove_articles(__A	):
        return re.sub(r"\b(a|an|the)\b" ,       " " ,       __A	)

    def white_space_fix(__A	):
        return " ".join(text.split()	)

    def remove_punc(__A	):
        SCREAMING_SNAKE_CASE__					     =						set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(__A	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__A	)	)	)	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	) & Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						sum(common.values()	)
    if num_same == 0:
        return 0
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						(2 * precision * recall) / (precision + recall)
    return fa





def 							__a       (					__A ,       __A	):
    return normalize_answer(__A	) == normalize_answer(__A	)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for ground_truth in ground_truths:
        SCREAMING_SNAKE_CASE__					     =						metric_fn(__A ,       __A	)
        scores_for_ground_truths.append(__A	)
    return max(__A	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						SCREAMING_SNAKE_CASE__					     =						SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_SNAKE_CASE__					     =						[]
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                SCREAMING_SNAKE_CASE__					     =						f"Unanswered question {qa['id']} will receive score 0."
                print(__A ,       file=sys.stderr	)
                continue

            SCREAMING_SNAKE_CASE__					     =						[x["text"] for x in qa["answers"]]
            SCREAMING_SNAKE_CASE__					     =						predictions[qa["id"]]

            SCREAMING_SNAKE_CASE__					     =						metric_max_over_ground_truths(__A ,       __A ,       __A	)
            if int(_exact_match	) == 1:
                correct_ids.append(qa["id"]	)
            exact_match += _exact_match

            fa += metric_max_over_ground_truths(__A ,       __A ,       __A	)

    SCREAMING_SNAKE_CASE__					     =						exact_match / total
    SCREAMING_SNAKE_CASE__					     =						fa / total

    return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
  A__        =			"1.0"
  A__        =			argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
  parser.add_argument("data_file", help="The dataset file in JSON format.")
  parser.add_argument("pred_file", help="The model prediction file in JSON format.")
  parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
  A__        =			parser.parse_args()

  with open(args.data_file) as data_file:
    A__        =			json.load(data_file)
    if dataset_json["version"] != expected_version:
      print(
          f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}''',
          file=sys.stderr,
      )
    A__        =			dataset_json["data"]

  with open(args.pred_file) as pred_file:
    A__        =			json.load(pred_file)

  A__       ,    A__        =			evaluate(dataset, predictions)

  if args.output_correct_ids:
    print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
    with open("correct_ids.json", "w") as f:
      json.dump(correct_ids, f)


