



"""simple docstring"""




import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
 import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME






class 			UpperCAmelCase_						(       _UpperCamelCase		):



      def __init__(  self  :			Optional[int]		):
           self.register_to_config()
           super().__init__()



      def 		snake_case_      (  self  :			List[Any]  ,			A  :			Union[str, Any]  ,			A  :			int		):
           if dicta == dicta:
                return True
           else:
                _UpperCAmelCase    :			Dict              =					self._remove_meta_keys(A		)
                _UpperCAmelCase    :			Any              =					self._remove_meta_keys(A		)
                if configa == configa:
                     print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'		)
                     return True
           return False



      def 		snake_case_      (  self  :			Tuple  ,			A  :			Dict		):
           _UpperCAmelCase    :			Any              =					[]
           _UpperCAmelCase    :			str              =					config_dict.copy()
           for key in config_dict.keys():
                if key.startswith("_"		):
                     temp_dict.pop(A		)
                     meta_keys.append(A		)
           return (temp_dict, meta_keys)



      @torch.no_grad()
      def 		snake_case_      (  self  :			Tuple  ,			A  :			List[Union[str, os.PathLike]]  ,			**A  :			Optional[Any]		):
           _UpperCAmelCase    :			Dict              =					kwargs.pop("cache_dir"  ,			A		)
           _UpperCAmelCase    :			List[Any]              =					kwargs.pop("resume_download"  ,			A		)
           _UpperCAmelCase    :			Any              =					kwargs.pop("force_download"  ,			A		)
           _UpperCAmelCase    :			Optional[int]              =					kwargs.pop("proxies"  ,			A		)
           _UpperCAmelCase    :			Tuple              =					kwargs.pop("local_files_only"  ,			A		)
           _UpperCAmelCase    :			List[str]              =					kwargs.pop("use_auth_token"  ,			A		)
           _UpperCAmelCase    :			int              =					kwargs.pop("revision"  ,			A		)
           _UpperCAmelCase    :			List[Any]              =					kwargs.pop("torch_dtype"  ,			A		)
           _UpperCAmelCase    :			Union[str, Any]              =					kwargs.pop("device_map"  ,			A		)

           _UpperCAmelCase    :			Dict              =					kwargs.pop("alpha"  ,			0.5		)
           _UpperCAmelCase    :			Tuple              =					kwargs.pop("interp"  ,			A		)

           print("Received list"  ,			A		)
           print(f'Combining with alpha={alpha}, interpolation mode={interp}'		)

           _UpperCAmelCase    :			List[Any]              =					len(A		)
           # Ignore result from model_index_json comparision of the two checkpoints
           _UpperCAmelCase    :			Union[str, Any]              =					kwargs.pop("force"  ,			A		)

           # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
           if checkpoint_count > 3 or checkpoint_count < 2:
                raise ValueError(
                    "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
                    " passed."		)

           print("Received the right number of checkpoints"		)
           # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
           # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

           # Validate that the checkpoints can be merged
           # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
           _UpperCAmelCase    :			Optional[Any]              =					[]
           for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                _UpperCAmelCase    :			Union[str, Any]              =					DiffusionPipeline.load_config(
                    A  ,			cache_dir=A  ,			resume_download=A  ,			force_download=A  ,			proxies=A  ,			local_files_only=A  ,			use_auth_token=A  ,			revision=A  ,			)
                config_dicts.append(A		)

           _UpperCAmelCase    :			Optional[int]              =					True
           for idx in range(1  ,			len(A		)		):
                comparison_result &= self._compare_model_configs(config_dicts[idx - 1]  ,			config_dicts[idx]		)
                if not force and comparison_result is False:
                     raise ValueError("Incompatible checkpoints. Please check model_index.json for the models."		)
                     print(config_dicts[0]  ,			config_dicts[1]		)
           print("Compatible model_index.json files found"		)
           # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
           _UpperCAmelCase    :			List[str]              =					[]
           for pretrained_model_name_or_path, config_dict in zip(A  ,			A		):
                _UpperCAmelCase    :			Optional[int]              =					[k for k in config_dict.keys() if not k.startswith("_"		)]
                _UpperCAmelCase    :			Union[str, Any]              =					[os.path.join(A  ,			"*"		) for k in folder_names]
                allow_patterns += [
                    WEIGHTS_NAME,
                    SCHEDULER_CONFIG_NAME,
                    CONFIG_NAME,
                    ONNX_WEIGHTS_NAME,
                    DiffusionPipeline.config_name,
                ]
                _UpperCAmelCase    :			Any              =					config_dict.get("_class_name"		)
                _UpperCAmelCase    :			Union[str, Any]              =					{"diffusers": __version__, "pipeline_class": requested_pipeline_class}

                _UpperCAmelCase    :			Any              =					(
                    pretrained_model_name_or_path
                    if os.path.isdir(A		)
                    else snapshot_download(
                        A  ,			cache_dir=A  ,			resume_download=A  ,			proxies=A  ,			local_files_only=A  ,			use_auth_token=A  ,			revision=A  ,			allow_patterns=A  ,			user_agent=A  ,			)
                )
                print("Cached Folder"  ,			A		)
                cached_folders.append(A		)

           # Step 3:-
           # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
           _UpperCAmelCase    :			List[str]              =					DiffusionPipeline.from_pretrained(
               cached_folders[0]  ,			torch_dtype=A  ,			device_map=A		)
           final_pipe.to(self.device		)

           _UpperCAmelCase    :			Tuple              =					None
           if len(A		) > 2:
                _UpperCAmelCase    :			Dict              =					os.path.join(cached_folders[2]		)

           if interp == "sigmoid":
                _UpperCAmelCase    :			Union[str, Any]              =					CheckpointMergerPipeline.sigmoid
           elif interp == "inv_sigmoid":
                _UpperCAmelCase    :			Tuple              =					CheckpointMergerPipeline.inv_sigmoid
           elif interp == "add_diff":
                _UpperCAmelCase    :			Union[str, Any]              =					CheckpointMergerPipeline.add_difference
           else:
                _UpperCAmelCase    :			Optional[int]              =					CheckpointMergerPipeline.weighted_sum

           # Find each module's state dict.
           for attr in final_pipe.config.keys():
                if not attr.startswith("_"		):
                     _UpperCAmelCase    :			str              =					os.path.join(cached_folders[1]  ,			A		)
                     if os.path.exists(A		):
                          _UpperCAmelCase    :			Tuple              =					[
                              *glob.glob(os.path.join(A  ,			"*.safetensors"		)		),
                              *glob.glob(os.path.join(A  ,			"*.bin"		)		),
                          ]
                          _UpperCAmelCase    :			Optional[Any]              =					files[0] if len(A		) > 0 else None
                     if len(A		) < 3:
                          _UpperCAmelCase    :			Dict              =					None
                     else:
                          _UpperCAmelCase    :			Union[str, Any]              =					os.path.join(cached_folders[2]  ,			A		)
                          if os.path.exists(A		):
                               _UpperCAmelCase    :			Dict              =					[
                                   *glob.glob(os.path.join(A  ,			"*.safetensors"		)		),
                                   *glob.glob(os.path.join(A  ,			"*.bin"		)		),
                               ]
                               _UpperCAmelCase    :			Tuple              =					files[0] if len(A		) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
                     if checkpoint_path_a is None and checkpoint_path_a is None:
                          print(f'Skipping {attr}: not present in 2nd or 3d model'		)
                          continue
                     try:
                          _UpperCAmelCase    :			List[str]              =					getattr(A  ,			A		)
                          if isinstance(A  ,			A		):  # ignore requires_safety_checker boolean
                               continue
                          _UpperCAmelCase    :			Optional[int]              =					getattr(A  ,			"state_dict"		)
                          _UpperCAmelCase    :			Tuple              =					theta_a()

                          _UpperCAmelCase    :			Any              =					getattr(A  ,			"load_state_dict"		)
                          _UpperCAmelCase    :			Optional[Any]              =					(
                              safetensors.torch.load_file(A		)
                              if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"		))
                              else torch.load(A  ,			map_location="cpu"		)
                          )
                          _UpperCAmelCase    :			int              =					None
                          if checkpoint_path_a:
                               _UpperCAmelCase    :			str              =					(
                                   safetensors.torch.load_file(A		)
                                   if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"		))
                                   else torch.load(A  ,			map_location="cpu"		)
                               )

                          if not theta_a.keys() == theta_a.keys():
                               print(f'Skipping {attr}: key mismatch'		)
                               continue
                          if theta_a and not theta_a.keys() == theta_a.keys():
                               print(f'Skipping {attr}:y mismatch'		)
                     except Exception as e:
                          print(f'Skipping {attr} do to an unexpected error: {str(A		)}'		)
                          continue
                     print(f'MERGING {attr}'		)

                     for key in theta_a.keys():
                          if theta_a:
                               _UpperCAmelCase    :			Optional[int]              =					theta_func(theta_a[key]  ,			theta_a[key]  ,			theta_a[key]  ,			A		)
                          else:
                               _UpperCAmelCase    :			Dict              =					theta_func(theta_a[key]  ,			theta_a[key]  ,			A  ,			A		)

                     del theta_a
                     del theta_a
                     update_theta_a(A		)

                     del theta_a
           return final_pipe



      @staticmethod
      def 		snake_case_      (  A  :			Tuple  ,			A  :			Dict  ,			A  :			Dict  ,			A  :			int		):
           return ((1 - alpha) * thetaa) + (alpha * thetaa)



      @staticmethod
      def 		snake_case_      (  A  :			Tuple  ,			A  :			int  ,			A  :			Optional[Any]  ,			A  :			int		):
           _UpperCAmelCase    :			str              =					alpha * alpha * (3 - (2 * alpha))
           return thetaa + ((thetaa - thetaa) * alpha)



      @staticmethod
      def 		snake_case_      (  A  :			Any  ,			A  :			int  ,			A  :			Optional[int]  ,			A  :			Union[str, Any]		):
           import math

           _UpperCAmelCase    :			Optional[Any]              =					0.5 - math.sin(math.asin(1.0 - 2.0 * alpha		) / 3.0		)
           return thetaa + ((thetaa - thetaa) * alpha)



      @staticmethod
      def 		snake_case_      (  A  :			Union[str, Any]  ,			A  :			Any  ,			A  :			Optional[int]  ,			A  :			str		):
           return thetaa + (thetaa - thetaa) * (1.0 - alpha)





"""simple docstring"""




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

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:





     '''simple docstring'''

     def remove_articles(SCREAMING_SNAKE_CASE__			:		Tuple       ):
          return re.sub(R"\b(a|an|the)\b"		,						" "		,						SCREAMING_SNAKE_CASE__       )

     def white_space_fix(SCREAMING_SNAKE_CASE__			:		Any       ):
          return " ".join(text.split()       )

     def remove_punc(SCREAMING_SNAKE_CASE__			:		Optional[int]       ):
          _UpperCAmelCase    :			List[str]              =					set(string.punctuation       )
          return "".join(ch for ch in text if ch not in exclude       )

     def lower(SCREAMING_SNAKE_CASE__			:		List[str]       ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__       )       )       )       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			List[Any]              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
     _UpperCAmelCase    :			Tuple              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
     _UpperCAmelCase    :			List[Any]              =					Counter(SCREAMING_SNAKE_CASE__       ) & Counter(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			List[str]              =					sum(common.values()       )
     if num_same == 0:
          return 0
     _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Union[str, Any]              =					(2 * precision * recall) / (precision + recall)
     return fa

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:





     '''simple docstring'''

     return normalize_answer(SCREAMING_SNAKE_CASE__       ) == normalize_answer(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:





     '''simple docstring'''

     _UpperCAmelCase    :			int              =					[]
     for ground_truth in ground_truths:
          _UpperCAmelCase    :			Dict              =					metric_fn(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
          scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__       )
     return max(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Tuple:





     '''simple docstring'''

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

               _UpperCAmelCase    :			Union[str, Any]              =					[x["text"] for x in qa["answers"]]
               _UpperCAmelCase    :			Optional[int]              =					predictions[qa["id"]]

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

               fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			List[str]              =					exact_match / total
     _UpperCAmelCase    :			str              =					fa / total

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


if __name__ == "__main__":
 _lowerCAmelCase       :      Any							=							"1.0"
 _lowerCAmelCase       :      Any							=							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.")
 _lowerCAmelCase       :      Tuple							=							parser.parse_args()

 with open(args.data_file) as data_file:
  _lowerCAmelCase       :      int							=							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,
   )
  _lowerCAmelCase       :      List[str]							=							dataset_json["data"]

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

 _lowerCAmelCase,		_lowerCAmelCase       :      List[Any]							=							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)
