code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import collections
  
  import jax
  import jax.numpy as jnp
  import torch
  import torch.nn as nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  
  from transformers import (
      CLIPTokenizer,
      OwlViTConfig,
      OwlViTForObjectDetection,
      OwlViTImageProcessor,
      OwlViTModel,
      OwlViTProcessor,
  )
  
  
  _lowerCAmelCase       :      int							=							{
      "vit_b32": {
          "embed_dim": 5_12,
          "image_resolution": 7_68,
          "context_length": 16,
          "vocab_size": 4_94_08,
          "vision_layers": 12,
          "vision_width": 7_68,
          "vision_patch_size": 32,
          "transformer_width": 5_12,
          "transformer_heads": 8,
          "transformer_layers": 12,
      },
      "vit_b16": {
          "embed_dim": 5_12,
          "image_resolution": 7_68,
          "context_length": 16,
          "vocab_size": 4_94_08,
          "vision_layers": 12,
          "vision_width": 7_68,
          "vision_patch_size": 16,
          "transformer_width": 5_12,
          "transformer_heads": 8,
          "transformer_layers": 12,
      },
      "vit_l14": {
          "embed_dim": 7_68,
          "image_resolution": 8_40,
          "context_length": 16,
          "vocab_size": 4_94_08,
          "vision_layers": 24,
          "vision_width": 10_24,
          "vision_patch_size": 14,
          "transformer_width": 7_68,
          "transformer_heads": 12,
          "transformer_layers": 12,
      },
  }
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Dict=""		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]="/"       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Union[str, Any]              =					[]
  
       for k, v in params.items():
            _UpperCAmelCase    :			Optional[int]              =					parent_key + sep + k if parent_key else k
  
            if isinstance(SCREAMING_SNAKE_CASE__		,						collections.MutableMapping       ):
                 items.extend(flatten_nested_dict(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						sep=SCREAMING_SNAKE_CASE__       ).items()       )
            else:
                 items.append((new_key, v)       )
       return dict(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[str]       )						->     Optional[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       return jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__       : x.astype(jnp.floataa       ) if x.dtype == jnp.bfloataa else x		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[str]		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					pt_attn_layer.in_proj_weight.chunk(3		,						dim=0       )
       _UpperCAmelCase    :			Union[str, Any]              =					pt_attn_layer.in_proj_bias.chunk(3		,						dim=0       )
  
       _UpperCAmelCase    :			int              =					pt_attn_layer.out_proj.weight
       _UpperCAmelCase    :			List[str]              =					pt_attn_layer.out_proj.bias
  
       _UpperCAmelCase    :			Optional[Any]              =					q_proj
       _UpperCAmelCase    :			Optional[Any]              =					q_proj_bias
  
       _UpperCAmelCase    :			Union[str, Any]              =					k_proj
       _UpperCAmelCase    :			Union[str, Any]              =					k_proj_bias
  
       _UpperCAmelCase    :			int              =					v_proj
       _UpperCAmelCase    :			Optional[int]              =					v_proj_bias
  
       _UpperCAmelCase    :			Tuple              =					out_proj_weights
       _UpperCAmelCase    :			str              =					out_proj_bias
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[str]		,						SCREAMING_SNAKE_CASE__			:		List[str]       )						->     Any:
  
  
  
  
  
       '''simple docstring'''
  
       copy_linear(hf_mlp.fca		,						pt_mlp.c_fc       )
       copy_linear(hf_mlp.fca		,						pt_mlp.c_proj       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Optional[int]              =					pt_linear.weight
       _UpperCAmelCase    :			Union[str, Any]              =					pt_linear.bias
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Any		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       copy_linear(hf_layer.layer_norma		,						pt_layer.ln_a       )
       copy_linear(hf_layer.layer_norma		,						pt_layer.ln_a       )
  
       # copy MLP
       copy_mlp(hf_layer.mlp		,						pt_layer.mlp       )
  
       # copy attn
       copy_attn_layer(hf_layer.self_attn		,						pt_layer.attn       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            copy_layer(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			str              =					pt_model.token_embedding.weight
       _UpperCAmelCase    :			List[Any]              =					pt_model.positional_embedding
  
       # copy layer norm
       copy_linear(hf_encoder.final_layer_norm		,						pt_model.ln_final       )
  
       # copy hidden layers
       copy_layers(hf_encoder.encoder.layers		,						pt_model.transformer.resblocks       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Any		,						SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			List[Any]              =					pt_model.text_projection.data.T
  
       # copy text encoder
       copy_encoder(hf_model.text_model		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     List[str]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					pt_model.visual.proj.data.T
  
       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layernorm		,						pt_model.visual.ln_pre       )
       copy_linear(hf_model.vision_model.post_layernorm		,						pt_model.visual.ln_post       )
  
       # copy embeds
       _UpperCAmelCase    :			List[Any]              =					pt_model.visual.conva.weight.data
       _UpperCAmelCase    :			Any              =					pt_model.visual.class_embedding
       _UpperCAmelCase    :			Tuple              =					pt_model.visual.positional_embedding.data
  
       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers		,						pt_model.visual.transformer.resblocks       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     Tuple:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Optional[Any]              =					flatten_nested_dict(flax_params["backbone"]["merged_class_token"]       )
  
       _UpperCAmelCase    :			Tuple              =					torch.from_numpy(flax_class_token_params["scale"]       )
       _UpperCAmelCase    :			Optional[Any]              =					torch.from_numpy(flax_class_token_params["bias"]       )
       _UpperCAmelCase    :			Dict              =					nn.Parameter(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Tuple              =					nn.Parameter(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Tuple              =					hf_model.state_dict()
       _UpperCAmelCase    :			Dict              =					{}
  
       # Rename class prediction head flax params to pytorch HF
       _UpperCAmelCase    :			Dict              =					flatten_nested_dict(flax_params["class_head"]       )
  
       for flax_key, v in flax_class_params.items():
            _UpperCAmelCase    :			int              =					flax_key.replace("/"		,						"."       )
            _UpperCAmelCase    :			Union[str, Any]              =					torch_key.replace(".kernel"		,						".weight"       )
            _UpperCAmelCase    :			Optional[int]              =					torch_key.replace("Dense_0"		,						"dense0"       )
            _UpperCAmelCase    :			List[str]              =					"class_head." + torch_key
  
            if "weight" in torch_key and v.ndim == 2:
                 _UpperCAmelCase    :			Optional[int]              =					v.T
  
            _UpperCAmelCase    :			List[Any]              =					nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE__       )       )
  
       # Rename box prediction box flax params to pytorch HF
       _UpperCAmelCase    :			Dict              =					flatten_nested_dict(flax_params["obj_box_head"]       )
  
       for flax_key, v in flax_box_params.items():
            _UpperCAmelCase    :			Optional[int]              =					flax_key.replace("/"		,						"."       )
            _UpperCAmelCase    :			Optional[int]              =					torch_key.replace(".kernel"		,						".weight"       )
            _UpperCAmelCase    :			Tuple              =					torch_key.replace("_"		,						""       ).lower()
            _UpperCAmelCase    :			int              =					"box_head." + torch_key
  
            if "weight" in torch_key and v.ndim == 2:
                 _UpperCAmelCase    :			Optional[int]              =					v.T
  
            _UpperCAmelCase    :			Optional[int]              =					nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE__       )       )
  
       # Copy flax params to PyTorch params
       for name, param in new_params.items():
            if name in pt_params.keys():
                 pt_params[name].copy_(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[str]		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for k, v in flax_attn_params.items():
            if k.startswith("transformer"       ):
                 _UpperCAmelCase    :			Any              =					k.replace("transformer.resblocks"		,						"text_model.encoder.layers"       )
            else:
                 _UpperCAmelCase    :			Optional[int]              =					k.replace("visual.transformer.resblocks"		,						"vision_model.encoder.layers"       )
  
            _UpperCAmelCase    :			Union[str, Any]              =					torch_key.replace("attn"		,						"self_attn"       )
            _UpperCAmelCase    :			Dict              =					torch_key.replace("key"		,						"k_proj"       )
            _UpperCAmelCase    :			Optional[Any]              =					torch_key.replace("value"		,						"v_proj"       )
            _UpperCAmelCase    :			List[Any]              =					torch_key.replace("query"		,						"q_proj"       )
            _UpperCAmelCase    :			List[str]              =					torch_key.replace("out"		,						"out_proj"       )
  
            if "bias" in torch_key and v.ndim == 2:
                 _UpperCAmelCase    :			Dict              =					v.shape[0] * v.shape[1]
                 _UpperCAmelCase    :			int              =					v.reshape(SCREAMING_SNAKE_CASE__       )
  
            if "weight" in torch_key and "out" in torch_key:
                 _UpperCAmelCase    :			Tuple              =					(v.shape[0] * v.shape[1], v.shape[2])
                 _UpperCAmelCase    :			Optional[int]              =					v.reshape(SCREAMING_SNAKE_CASE__       ).T
  
            if "weight" in torch_key and "out" not in torch_key:
                 _UpperCAmelCase    :			List[Any]              =					(v.shape[0], v.shape[1] * v.shape[2])
                 _UpperCAmelCase    :			Optional[Any]              =					v.reshape(SCREAMING_SNAKE_CASE__       ).T
  
            # Copy flax CLIP attn params to HF PyTorch params
            _UpperCAmelCase    :			Tuple              =					torch.from_numpy(SCREAMING_SNAKE_CASE__       )
            hf_backbone.state_dict()[torch_key].copy_(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Any       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			int              =					{}
       _UpperCAmelCase    :			Tuple              =					[]
  
       for k, v in params.items():
            if "attn." in k:
                 _UpperCAmelCase    :			Tuple              =					k[: k.rindex("attn."       ) + 5]
                 if base in processed_attn_layers:
                      continue
  
                 processed_attn_layers.append(SCREAMING_SNAKE_CASE__       )
                 _UpperCAmelCase    :			int              =					params[base + "out.weight"].shape[-1]
                 _UpperCAmelCase    :			Tuple              =					params[base + "out.weight"].reshape(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ).T
                 _UpperCAmelCase    :			List[Any]              =					params[base + "out.bias"]
            else:
                 _UpperCAmelCase    :			Optional[Any]              =					v
       return new_params
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]       )						->     Any:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					CLIP(**SCREAMING_SNAKE_CASE__       )
       torch_model.eval()
       _UpperCAmelCase    :			Union[str, Any]              =					torch_model.state_dict()
  
       _UpperCAmelCase    :			Any              =					flatten_nested_dict(flax_params["backbone"]["clip"]       )
       _UpperCAmelCase    :			Tuple              =					{}
  
       for flax_key, v in flax_clip_params.items():
            _UpperCAmelCase    :			Optional[Any]              =					flax_key.replace("/"		,						"."       )
            _UpperCAmelCase    :			int              =					torch_key.replace("text.token_embedding.embedding"		,						"token_embedding.kernel"       )
  
            if (
                torch_key.startswith("text.transformer"       )
                or torch_key.startswith("text.text_projection"       )
                or torch_key.startswith("text.ln_final"       )
                or torch_key.startswith("text.positional_embedding"       )
            ):
                 _UpperCAmelCase    :			List[Any]              =					torch_key[5:]
  
            _UpperCAmelCase    :			List[str]              =					torch_key.replace("text_projection.kernel"		,						"text_projection"       )
            _UpperCAmelCase    :			int              =					torch_key.replace("visual.proj.kernel"		,						"visual.proj"       )
            _UpperCAmelCase    :			Dict              =					torch_key.replace(".scale"		,						".weight"       )
            _UpperCAmelCase    :			Tuple              =					torch_key.replace(".kernel"		,						".weight"       )
  
            if "conv" in torch_key or "downsample.0.weight" in torch_key:
                 _UpperCAmelCase    :			str              =					v.transpose(3		,						2		,						0		,						1       )
  
            elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
                 # Fully connected layers are transposed, embeddings are not
                 _UpperCAmelCase    :			Optional[int]              =					v.T
  
            _UpperCAmelCase    :			Optional[int]              =					v
  
       _UpperCAmelCase    :			Dict              =					_convert_attn_layers(SCREAMING_SNAKE_CASE__       )
       new_torch_params.update(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			str              =					{}
  
       # Copy flax CLIP backbone params to PyTorch params
       for name, param in new_torch_params.items():
            if name in torch_clip_params.keys():
                 _UpperCAmelCase    :			Any              =					torch.from_numpy(new_torch_params[name]       )
                 torch_clip_params[name].copy_(SCREAMING_SNAKE_CASE__       )
            else:
                 _UpperCAmelCase    :			Optional[Any]              =					param
  
       return torch_clip_params, torch_model, attn_params
  
  @torch.no_grad()
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		List[str]		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]=None       )						->     Optional[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					Repository(SCREAMING_SNAKE_CASE__		,						clone_from=f'google/{pytorch_dump_folder_path}'       )
       repo.git_pull()
  
       if config_path is not None:
            _UpperCAmelCase    :			Dict              =					OwlViTConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
       else:
            _UpperCAmelCase    :			Any              =					OwlViTConfig()
  
       _UpperCAmelCase    :			List[str]              =					OwlViTModel(SCREAMING_SNAKE_CASE__       ).eval()
       _UpperCAmelCase    :			Optional[int]              =					OwlViTForObjectDetection(SCREAMING_SNAKE_CASE__       ).eval()
  
       copy_text_model_and_projection(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
       copy_vision_model_and_projection(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Optional[Any]              =					pt_backbone.logit_scale
       copy_flax_attn_params(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       _UpperCAmelCase    :			Optional[int]              =					hf_backbone
       copy_class_merge_token(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
       copy_class_box_heads(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       # Save HF model
       hf_model.save_pretrained(repo.local_dir       )
  
       # Initialize image processor
       _UpperCAmelCase    :			int              =					OwlViTImageProcessor(
           size=config.vision_config.image_size		,						crop_size=config.vision_config.image_size       )
       # Initialize tokenizer
       _UpperCAmelCase    :			Tuple              =					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"		,						pad_token="!"		,						model_max_length=16       )
  
       # Initialize processor
       _UpperCAmelCase    :			Any              =					OwlViTProcessor(image_processor=SCREAMING_SNAKE_CASE__		,						tokenizer=SCREAMING_SNAKE_CASE__       )
       image_processor.save_pretrained(repo.local_dir       )
       processor.save_pretrained(repo.local_dir       )
  
       repo.git_add()
       repo.git_commit("Upload model and processor"       )
       repo.git_push()
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      int							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--owlvit_version",
       default=None,
       type=str,
       required=True,
       help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
   )
   parser.add_argument(
       "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
   )
   parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
   parser.add_argument(
       "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
   )
   _lowerCAmelCase       :      Optional[Any]							=							parser.parse_args()
  
   # Initialize PyToch clip model
   _lowerCAmelCase       :      Dict							=							args.owlvit_version
   if model_name == "clip_b16":
    _lowerCAmelCase       :      Union[str, Any]							=							CONFIGS["vit_b16"]
   elif model_name == "clip_b32":
    _lowerCAmelCase       :      Dict							=							CONFIGS["vit_b32"]
   elif model_name == "clip_l14":
    _lowerCAmelCase       :      List[Any]							=							CONFIGS["vit_l14"]
  
   # Load from checkpoint and convert params to float-32
   _lowerCAmelCase       :      Union[str, Any]							=							checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
   _lowerCAmelCase       :      str							=							jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
   del variables
  
   # Convert CLIP backbone
   _lowerCAmelCase       :      List[Any]							=							convert_clip_backbone(flax_params, torch_config)
  
   convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)
  
style_context: 
  
  
  
  """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)
  