


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a_  :    List[str]      =			logging.get_logger(__name__)





def __snake_case (				UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						str=False ,    UpperCAmelCase_						:						Tuple=False		):
 lowerCamelCase_								=  "backbone." if is_semantic else ""

 lowerCamelCase_								=  []
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F'''{prefix}cls_token''', "beit.embeddings.cls_token"),
         (F'''{prefix}patch_embed.proj.weight''', "beit.embeddings.patch_embeddings.projection.weight"),
         (F'''{prefix}patch_embed.proj.bias''', "beit.embeddings.patch_embeddings.projection.bias"),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ("mask_token", "beit.embeddings.mask_token"),
          (
              "rel_pos_bias.relative_position_bias_table",
              "beit.encoder.relative_position_bias.relative_position_bias_table",
          ),
          (
              "rel_pos_bias.relative_position_index",
              "beit.encoder.relative_position_bias.relative_position_index",
          ),
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
          ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
          ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
          ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("fc_norm.weight", "beit.pooler.layernorm.weight"),
          ("fc_norm.bias", "beit.pooler.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]		)

 return rename_keys





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Tuple=False ,    UpperCAmelCase_						:						List[str]=False		):
 for i in range(config.num_hidden_layers		):
  lowerCamelCase_								=  "backbone." if is_semantic else ""
  # queries, keys and values
  lowerCamelCase_								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  lowerCamelCase_								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  lowerCamelCase_								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  lowerCamelCase_								=  in_proj_weight[
      : config.hidden_size, :
  ]
  lowerCamelCase_								=  q_bias
  lowerCamelCase_								=  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowerCamelCase_								=  in_proj_weight[
      -config.hidden_size :, :
  ]
  lowerCamelCase_								=  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  lowerCamelCase_								=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
  lowerCamelCase_								=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

  lowerCamelCase_								=  gamma_a
  lowerCamelCase_								=  gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   lowerCamelCase_								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   lowerCamelCase_								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   lowerCamelCase_								=  table
   lowerCamelCase_								=  index





def __snake_case (				UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any] ,    UpperCAmelCase_						:						Dict		):
 lowerCamelCase_								=  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_								=  val





def __snake_case (				):
 lowerCamelCase_								=  "http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_								=  Image.open(requests.get(UpperCAmelCase_ ,    stream=UpperCAmelCase_		).raw		)
 return im





@torch.no_grad()
def __snake_case (				UpperCAmelCase_						:						Optional[Any] ,    UpperCAmelCase_						:						Any		):
 lowerCamelCase_								=  BeitConfig()
 lowerCamelCase_								=  False
 lowerCamelCase_								=  False
 lowerCamelCase_								=  "huggingface/label-files"
 # set config parameters based on URL
 if checkpoint_url[-9:-4] == "pt22k":
  # masked image modeling
  lowerCamelCase_								=  True
  lowerCamelCase_								=  True
  lowerCamelCase_								=  True
 elif checkpoint_url[-9:-4] == "ft22k":
  # intermediate fine-tuning on ImageNet-22k
  lowerCamelCase_								=  True
  lowerCamelCase_								=  21841
  lowerCamelCase_								=  "imagenet-22k-id2label.json"
  lowerCamelCase_								=  json.load(open(hf_hub_download(UpperCAmelCase_ ,    UpperCAmelCase_ ,    repo_type="dataset"		) ,    "r"		)		)
  lowerCamelCase_								=  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  # this dataset contains 21843 labels but the model only has 21841
  # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  del idalabel[9205]
  del idalabel[15027]
  lowerCamelCase_								=  idalabel
  lowerCamelCase_								=  {v: k for k, v in idalabel.items()}
 elif checkpoint_url[-8:-4] == "to1k":
  # fine-tuning on ImageNet-1k
  lowerCamelCase_								=  True
  lowerCamelCase_								=  1000
  lowerCamelCase_								=  "imagenet-1k-id2label.json"
  lowerCamelCase_								=  json.load(open(hf_hub_download(UpperCAmelCase_ ,    UpperCAmelCase_ ,    repo_type="dataset"		) ,    "r"		)		)
  lowerCamelCase_								=  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  lowerCamelCase_								=  idalabel
  lowerCamelCase_								=  {v: k for k, v in idalabel.items()}
  if "384" in checkpoint_url:
   lowerCamelCase_								=  384
  if "512" in checkpoint_url:
   lowerCamelCase_								=  512
 elif "ade20k" in checkpoint_url:
  # fine-tuning
  lowerCamelCase_								=  True
  lowerCamelCase_								=  150
  lowerCamelCase_								=  "ade20k-id2label.json"
  lowerCamelCase_								=  json.load(open(hf_hub_download(UpperCAmelCase_ ,    UpperCAmelCase_ ,    repo_type="dataset"		) ,    "r"		)		)
  lowerCamelCase_								=  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  lowerCamelCase_								=  idalabel
  lowerCamelCase_								=  {v: k for k, v in idalabel.items()}
  lowerCamelCase_								=  640
  lowerCamelCase_								=  True
 else:
  raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"		)

 # size of the architecture
 if "base" in checkpoint_url:
  pass
 elif "large" in checkpoint_url:
  lowerCamelCase_								=  1024
  lowerCamelCase_								=  4096
  lowerCamelCase_								=  24
  lowerCamelCase_								=  16
  if "ade20k" in checkpoint_url:
   lowerCamelCase_								=  640
   lowerCamelCase_								=  [7, 11, 15, 23]
 else:
  raise ValueError("Should either find 'base' or 'large' in checkpoint URL"		)

 # load state_dict of original model, remove and rename some keys
 lowerCamelCase_								=  torch.hub.load_state_dict_from_url(UpperCAmelCase_ ,    map_location="cpu" ,    check_hash=UpperCAmelCase_		)
 lowerCamelCase_								=  state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

 lowerCamelCase_								=  create_rename_keys(UpperCAmelCase_ ,    has_lm_head=UpperCAmelCase_ ,    is_semantic=UpperCAmelCase_		)
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)
 read_in_q_k_v(UpperCAmelCase_ ,    UpperCAmelCase_ ,    has_lm_head=UpperCAmelCase_ ,    is_semantic=UpperCAmelCase_		)
 if is_semantic:
  # add prefix to decoder keys
  for key, val in state_dict.copy().items():
   lowerCamelCase_								=  state_dict.pop(UpperCAmelCase_		)
   if key.startswith("backbone.fpn"		):
    lowerCamelCase_								=  key.replace("backbone.fpn" ,    "fpn"		)
   lowerCamelCase_								=  val

    # load HuggingFace model
 if checkpoint_url[-9:-4] == "pt22k":
  lowerCamelCase_								=  BeitForMaskedImageModeling(UpperCAmelCase_		)
 elif "ade20k" in checkpoint_url:
  lowerCamelCase_								=  BeitForSemanticSegmentation(UpperCAmelCase_		)
 else:
  lowerCamelCase_								=  BeitForImageClassification(UpperCAmelCase_		)
 model.eval()
 model.load_state_dict(UpperCAmelCase_		)

 # Check outputs on an image
 if is_semantic:
  lowerCamelCase_								=  BeitImageProcessor(size=config.image_size ,    do_center_crop=UpperCAmelCase_		)
  lowerCamelCase_								=  load_dataset("hf-internal-testing/fixtures_ade20k" ,    split="test"		)
  lowerCamelCase_								=  Image.open(ds[0]["file"]		)
 else:
  lowerCamelCase_								=  BeitImageProcessor(
      size=config.image_size ,    resample=PILImageResampling.BILINEAR ,    do_center_crop=UpperCAmelCase_		)
  lowerCamelCase_								=  prepare_img()

 lowerCamelCase_								=  image_processor(images=UpperCAmelCase_ ,    return_tensors="pt"		)
 lowerCamelCase_								=  encoding["pixel_values"]

 lowerCamelCase_								=  model(UpperCAmelCase_		)
 lowerCamelCase_								=  outputs.logits

 # verify logits
 lowerCamelCase_								=  torch.Size([1, 1000]		)
 if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"		):
  lowerCamelCase_								=  torch.Size([1, 196, 8192]		)
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"		):
  lowerCamelCase_								=  torch.Size([1, 196, 8192]		)
 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"		):
  lowerCamelCase_								=  torch.Size([1, 21841]		)
  lowerCamelCase_								=  torch.tensor([2.2288, 2.4671, 0.7395]		)
  lowerCamelCase_								=  2397
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"		):
  lowerCamelCase_								=  torch.Size([1, 21841]		)
  lowerCamelCase_								=  torch.tensor([1.6881, -0.2787, 0.5901]		)
  lowerCamelCase_								=  2396
 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"		):
  lowerCamelCase_								=  torch.tensor([0.1241, 0.0798, -0.6569]		)
  lowerCamelCase_								=  285
 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"		):
  lowerCamelCase_								=  torch.tensor([-1.2385, -1.0987, -1.0108]		)
  lowerCamelCase_								=  281
 elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"		):
  lowerCamelCase_								=  torch.tensor([-1.5303, -0.9484, -0.3147]		)
  lowerCamelCase_								=  761
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"		):
  lowerCamelCase_								=  torch.tensor([0.4610, -0.0928, 0.2086]		)
  lowerCamelCase_								=  761
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"		):
  lowerCamelCase_								=  torch.tensor([-0.4804, 0.6257, -0.1837]		)
  lowerCamelCase_								=  761
 elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"		):
  lowerCamelCase_								=  torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
  lowerCamelCase_								=  761
 elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"		):
  lowerCamelCase_								=  torch.tensor([-0.3062, 0.7261, 0.4852]		)
  lowerCamelCase_								=  761
 elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"		):
  lowerCamelCase_								=  (1, 150, 160, 160)
  lowerCamelCase_								=  torch.tensor(
      [
          [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
          [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
          [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
      ]		)
 elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"		):
  lowerCamelCase_								=  (1, 150, 160, 160)
  lowerCamelCase_								=  torch.tensor(
      [
          [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
          [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
          [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
      ]		)
 else:
  raise ValueError("Can't verify logits as model is not supported"		)

 assert logits.shape == expected_shape, "Shape of logits not as expected"
 if not has_lm_head:
  if is_semantic:
   assert torch.allclose(
       logits[0, :3, :3, :3] ,    UpperCAmelCase_ ,    atol=1E-3		), "First elements of logits not as expected"
  else:
   print("Predicted class idx:" ,    logits.argmax(-1		).item()		)
   assert torch.allclose(
       logits[0, :3] ,    UpperCAmelCase_ ,    atol=1E-3		), "First elements of logits not as expected"
   assert logits.argmax(-1		).item() == expected_class_idx, "Predicted class index not as expected"

 Path(UpperCAmelCase_		).mkdir(exist_ok=UpperCAmelCase_		)
 print(F'''Saving model to {pytorch_dump_folder_path}'''		)
 model.save_pretrained(UpperCAmelCase_		)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
 image_processor.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     a_  :    List[Any]      =			argparse.ArgumentParser()

     parser.add_argument(
         """--checkpoint_url""",
         default="""https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth""",
         type=str,
         help="""URL to the original PyTorch checkpoint (.pth file).""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
     )
     a_  :    Tuple      =			parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''

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





def __snake_case (				UpperCAmelCase_						:						str		):

 def remove_articles(UpperCAmelCase_						:						Optional[Any]		):
  return re.sub(r"\b(a|an|the)\b" ,    " " ,    UpperCAmelCase_		)

 def white_space_fix(UpperCAmelCase_						:						List[str]		):
  return " ".join(text.split()		)

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

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

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_		)		)		)		)





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						int		):
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  Counter(UpperCAmelCase_		) & Counter(UpperCAmelCase_		)
 lowerCamelCase_								=  sum(common.values()		)
 if num_same == 0:
  return 0
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  (2 * precision * recall) / (precision + recall)
 return fa





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						List[Any]		):
 return normalize_answer(UpperCAmelCase_		) == normalize_answer(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  []
 for ground_truth in ground_truths:
  lowerCamelCase_								=  metric_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
  scores_for_ground_truths.append(UpperCAmelCase_		)
 return max(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Dict		):
 lowerCamelCase_								=  lowerCamelCase_								=  lowerCamelCase_								=  0
 lowerCamelCase_								=  []
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    lowerCamelCase_								=  F'''Unanswered question {qa["id"]} will receive score 0.'''
    print(UpperCAmelCase_ ,    file=sys.stderr		)
    continue

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

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

   fa += metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  exact_match / total
 lowerCamelCase_								=  fa / total

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


if __name__ == "__main__":
     a_  :    Optional[int]      =			"""1.0"""
     a_  :    Dict      =			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_  :    Optional[int]      =			parser.parse_args()

     with open(args.data_file) as data_file:
          a_  :    Any      =			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_  :    Any      =			dataset_json["""data"""]

     with open(args.pred_file) as pred_file:
          a_  :    List[str]      =			json.load(pred_file)

     a_     ,       a_  :    int      =			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)



