




'''simple docstring'''





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


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   def remove_articles(snake_case ):
      return re.sub(R'''\b(a|an|the)\b'''						,  ''' '''						,  snake_case )

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

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

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

   return white_space_fix(remove_articles(remove_punc(lower(snake_case ) ) ) )


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

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


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   return normalize_answer(snake_case ) == normalize_answer(snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
   for ground_truth in ground_truths:
      SCREAMING_SNAKE_CASE__      :					int							    =			metric_fn(snake_case						,  snake_case )
      scores_for_ground_truths.append(snake_case )
   return max(snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

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

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[x['''text'''] for x in qa['''answers''']]
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			predictions[qa['''id''']]

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			metric_max_over_ground_truths(snake_case						,  snake_case						,  snake_case )
         if int(_exact_match ) == 1:
            correct_ids.append(qa['''id'''] )
         exact_match += _exact_match

         fa += metric_max_over_ground_truths(snake_case						,  snake_case						,  snake_case )

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			exact_match / total
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			fa / total

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


if __name__ == "__main__":
       lowercase__								=						'1.0'
       lowercase__								=						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.''')
       lowercase__								=						parser.parse_args()

       with open(args.data_file) as data_file:
              lowercase__								=						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,
                     )
              lowercase__								=						dataset_json['data']

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

       lowercase__								=						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)







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def      __magic_name__							(					A						:   str,    A						:   List[str],    A						:   int  ):






			'''simple docstring'''







			a       ,						a       ,						a       =     pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3,    dim=0  )
			a       ,						a       ,						a       =     pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3,    dim=0  )

			a       =     pt_weights[F"""{prefix}.out_proj.weight"""]
			a       =     pt_weights[F"""{prefix}.out_proj.bias"""]

			a       =     q_proj
			a       =     q_proj_bias

			a       =     k_proj
			a       =     k_proj_bias

			a       =     v_proj
			a       =     v_proj_bias

			a       =     out_proj_weights
			a       =     out_proj_bias




def      __magic_name__							(					A						:   Optional[int],    A						:   str,    A						:   Union[str, Any]  ):






			'''simple docstring'''







			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_fc"""  )
			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_proj"""  )




def      __magic_name__							(					A						:   int,    A						:   Tuple,    A						:   Optional[int]  ):






			'''simple docstring'''







			a       =     pt_weights[F"""{prefix}.weight"""].data
			a       =     pt_weights[F"""{prefix}.bias"""].data




def      __magic_name__							(					A						:   List[Any],    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			copy_linear(hf_layer.layer_norma,    A,    F"""{prefix}.ln_1"""  )
			copy_linear(hf_layer.layer_norma,    A,    F"""{prefix}.ln_2"""  )

			# copy MLP
			copy_mlp(hf_layer.mlp,    A,    F"""{prefix}.mlp"""  )

			# copy attn
			copy_attn_layer(hf_layer.self_attn,    A,    F"""{prefix}.attn"""  )




def      __magic_name__							(					A						:   int,    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			for layer_id, hf_layer in enumerate(A  ):
						copy_layer(A,    A,    F"""{prefix}.{layer_id}"""  )




def      __magic_name__							(					A						:   Any,    A						:   Any  ):






			'''simple docstring'''







			a       =     pt_weights["text_projection"].data.T

			# copy text encoder
			for name, param in hf_model.text_model.named_parameters():
						a       =     pt_weights[F"""bert.{name}"""].data




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any]  ):






			'''simple docstring'''







			a       =     pt_weights["visual.proj"].data.T

			# copy layer norms
			copy_linear(hf_model.vision_model.pre_layrnorm,    A,    "visual.ln_pre"  )
			copy_linear(hf_model.vision_model.post_layernorm,    A,    "visual.ln_post"  )

			# copy embeddings
			a       =     pt_weights["visual.conv1.weight"].data
			a       =     pt_weights["visual.class_embedding"].data
			a       =     pt_weights["visual.positional_embedding"].data

			# copy encoder
			copy_layers(hf_model.vision_model.encoder.layers,    A,    "visual.transformer.resblocks"  )




@torch.no_grad()
def      __magic_name__							(					A						:   Optional[Any],    A						:   List[str],    A						:   Dict=None  ):






			'''simple docstring'''







			assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
			a       =     ChineseCLIPConfig.from_pretrained(A  )

			a       =     ChineseCLIPModel(A  ).eval()

			a       =     torch.load(A,    map_location="cpu"  )["state_dict"]
			a       =     {(name[7:] if name.startswith("module."  ) else name): value for name, value in pt_weights.items()}

			copy_text_model_and_projection(A,    A  )
			copy_vision_model_and_projection(A,    A  )
			a       =     pt_weights["logit_scale"].data

			hf_model.save_pretrained(A  )


if __name__ == "__main__":
		__lowerCAmelCase						:							Dict			     =						argparse.ArgumentParser()
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    help='Path to the output folder storing converted hf PyTorch model.',
		)
		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, help='Path to original github format ChineseCLIP checkpoint.'
		)
		parser.add_argument(
		    '--config_path', default=None, required=True, type=str, help='Path to hf config.json of model to convert.'
		)
		__lowerCAmelCase						:							Dict			     =						parser.parse_args()

		convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
		print('The conversion is finished!')






