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






def 						lowerCamelCase__							(      a	,	a=False	,	a=False   )   ->     Any:
   _A:						int	    = '''backbone.''' if is_semantic else ''''''

   _A:						Any	    = []
   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 						lowerCamelCase__							(      a	,	a	,	a=False	,	a=False   )   ->     str:
   for i in range(config.num_hidden_layers   ):
      _A:						str	    = '''backbone.''' if is_semantic else ''''''
      # queries, keys and values
      _A:						Tuple	    = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""   )
      _A:						Tuple	    = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""   )
      _A:						List[Any]	    = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""   )

      _A:						Optional[int]	    = in_proj_weight[
          : config.hidden_size, :
      ]
      _A:						List[Any]	    = q_bias
      _A:						Any	    = in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _A:						Optional[Any]	    = in_proj_weight[
          -config.hidden_size :, :
      ]
      _A:						List[Any]	    = v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      _A:						Tuple	    = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""   )
      _A:						List[str]	    = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""   )

      _A:						Dict	    = gamma_a
      _A:						List[Any]	    = gamma_a

      # relative_position bias table + index
      if not has_lm_head:
         # each layer has its own relative position bias
         _A:						Any	    = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""   )
         _A:						Optional[Any]	    = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""   )

         _A:						Optional[Any]	    = table
         _A:						Optional[Any]	    = index






def 						lowerCamelCase__							(      a	,	a	,	a   )   ->     Optional[int]:
   _A:						Any	    = dct.pop(a   )
   _A:						Union[str, Any]	    = val






def 						lowerCamelCase__							(      )   ->     int:
   _A:						List[Any]	    = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A:						Optional[int]	    = Image.open(requests.get(a	,	stream=a   ).raw   )
   return im






@torch.no_grad()
def 						lowerCamelCase__							(      a	,	a   )   ->     Tuple:
   _A:						Union[str, Any]	    = BeitConfig()
   _A:						List[Any]	    = False
   _A:						Dict	    = False
   _A:						int	    = '''huggingface/label-files'''
   # set config parameters based on URL
   if checkpoint_url[-9:-4] == "pt22k":
      # masked image modeling
      _A:						List[str]	    = True
      _A:						int	    = True
      _A:						Union[str, Any]	    = True
   elif checkpoint_url[-9:-4] == "ft22k":
      # intermediate fine-tuning on ImageNet-22k
      _A:						List[Any]	    = True
      _A:						List[Any]	    = 2_18_41
      _A:						Union[str, Any]	    = '''imagenet-22k-id2label.json'''
      _A:						Dict	    = json.load(open(hf_hub_download(a	,	a	,	repo_type='''dataset'''   )	,	'''r'''   )   )
      _A:						Any	    = {int(a   ): 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[92_05]
      del idalabel[1_50_27]
      _A:						Union[str, Any]	    = idalabel
      _A:						Any	    = {v: k for k, v in idalabel.items()}
   elif checkpoint_url[-8:-4] == "to1k":
      # fine-tuning on ImageNet-1k
      _A:						str	    = True
      _A:						Any	    = 10_00
      _A:						Optional[Any]	    = '''imagenet-1k-id2label.json'''
      _A:						Optional[int]	    = json.load(open(hf_hub_download(a	,	a	,	repo_type='''dataset'''   )	,	'''r'''   )   )
      _A:						Tuple	    = {int(a   ): v for k, v in idalabel.items()}
      _A:						Tuple	    = idalabel
      _A:						Union[str, Any]	    = {v: k for k, v in idalabel.items()}
      if "384" in checkpoint_url:
         _A:						Optional[int]	    = 3_84
      if "512" in checkpoint_url:
         _A:						int	    = 5_12
   elif "ade20k" in checkpoint_url:
      # fine-tuning
      _A:						str	    = True
      _A:						Tuple	    = 1_50
      _A:						Tuple	    = '''ade20k-id2label.json'''
      _A:						Union[str, Any]	    = json.load(open(hf_hub_download(a	,	a	,	repo_type='''dataset'''   )	,	'''r'''   )   )
      _A:						int	    = {int(a   ): v for k, v in idalabel.items()}
      _A:						Dict	    = idalabel
      _A:						int	    = {v: k for k, v in idalabel.items()}
      _A:						Optional[int]	    = 6_40
      _A:						Dict	    = 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:
      _A:						Tuple	    = 10_24
      _A:						Dict	    = 40_96
      _A:						str	    = 24
      _A:						str	    = 16
      if "ade20k" in checkpoint_url:
         _A:						List[Any]	    = 6_40
         _A:						Union[str, Any]	    = [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
   _A:						str	    = torch.hub.load_state_dict_from_url(a	,	map_location='''cpu'''	,	check_hash=a   )
   _A:						str	    = state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

   _A:						Dict	    = create_rename_keys(a	,	has_lm_head=a	,	is_semantic=a   )
   for src, dest in rename_keys:
      rename_key(a	,	a	,	a   )
   read_in_q_k_v(a	,	a	,	has_lm_head=a	,	is_semantic=a   )
   if is_semantic:
      # add prefix to decoder keys
      for key, val in state_dict.copy().items():
         _A:						Any	    = state_dict.pop(a   )
         if key.startswith('''backbone.fpn'''   ):
            _A:						List[Any]	    = key.replace('''backbone.fpn'''	,	'''fpn'''   )
         _A:						str	    = val

    # load HuggingFace model
   if checkpoint_url[-9:-4] == "pt22k":
      _A:						Optional[int]	    = BeitForMaskedImageModeling(a   )
   elif "ade20k" in checkpoint_url:
      _A:						Optional[Any]	    = BeitForSemanticSegmentation(a   )
   else:
      _A:						Tuple	    = BeitForImageClassification(a   )
   model.eval()
   model.load_state_dict(a   )

   # Check outputs on an image
   if is_semantic:
      _A:						List[str]	    = BeitImageProcessor(size=config.image_size	,	do_center_crop=a   )
      _A:						Any	    = load_dataset('''hf-internal-testing/fixtures_ade20k'''	,	split='''test'''   )
      _A:						Tuple	    = Image.open(ds[0]['''file''']   )
   else:
      _A:						int	    = BeitImageProcessor(
          size=config.image_size	,	resample=PILImageResampling.BILINEAR	,	do_center_crop=a   )
      _A:						Union[str, Any]	    = prepare_img()

   _A:						Dict	    = image_processor(images=a	,	return_tensors='''pt'''   )
   _A:						Any	    = encoding['''pixel_values''']

   _A:						Optional[Any]	    = model(a   )
   _A:						Optional[Any]	    = outputs.logits

   # verify logits
   _A:						str	    = torch.Size([1, 10_00]   )
   if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''   ):
      _A:						Any	    = torch.Size([1, 1_96, 81_92]   )
   elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''   ):
      _A:						List[Any]	    = torch.Size([1, 1_96, 81_92]   )
   elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''   ):
      _A:						Dict	    = torch.Size([1, 2_18_41]   )
      _A:						Optional[Any]	    = torch.tensor([2.2288, 2.4671, 0.7395]   )
      _A:						Optional[int]	    = 23_97
   elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''   ):
      _A:						Tuple	    = torch.Size([1, 2_18_41]   )
      _A:						Union[str, Any]	    = torch.tensor([1.6881, -0.2787, 0.5901]   )
      _A:						Union[str, Any]	    = 23_96
   elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''   ):
      _A:						List[Any]	    = torch.tensor([0.1241, 0.0798, -0.6569]   )
      _A:						str	    = 2_85
   elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''   ):
      _A:						Dict	    = torch.tensor([-1.2385, -1.0987, -1.0108]   )
      _A:						int	    = 2_81
   elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''   ):
      _A:						List[str]	    = torch.tensor([-1.5303, -0.9484, -0.3147]   )
      _A:						Optional[int]	    = 7_61
   elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''   ):
      _A:						Optional[int]	    = torch.tensor([0.4610, -0.0928, 0.2086]   )
      _A:						Optional[Any]	    = 7_61
   elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''   ):
      _A:						Optional[int]	    = torch.tensor([-0.4804, 0.6257, -0.1837]   )
      _A:						Tuple	    = 7_61
   elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''   ):
      _A:						Any	    = torch.tensor([[-0.5122, 0.5117, -0.2113]]   )
      _A:						Any	    = 7_61
   elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''   ):
      _A:						Union[str, Any]	    = torch.tensor([-0.3062, 0.7261, 0.4852]   )
      _A:						Tuple	    = 7_61
   elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''   ):
      _A:						Tuple	    = (1, 1_50, 1_60, 1_60)
      _A:						List[Any]	    = 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'''   ):
      _A:						Optional[int]	    = (1, 1_50, 1_60, 1_60)
      _A:						Union[str, Any]	    = 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]	,	a	,	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]	,	a	,	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(a   ).mkdir(exist_ok=a   )
   print(f"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(a   )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
   image_processor.save_pretrained(a   )


if __name__ == "__main__":
   UpperCAmelCase__	:  Union[str, 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.'
   )
   UpperCAmelCase__	:  str      =   parser.parse_args()
   convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:  Optional[Any]      =   '\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

UpperCAmelCase__	:  List[Any]      =   '\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


UpperCAmelCase__	:  List[str]      =   '\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION     )
class UpperCAmelCase     (		datasets.Metric     ):





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,    citation=_CITATION    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string'''  ),
                  '''references''': datasets.Value('''string'''  ),
              }  )    ,    homepage='''https://github.com/moussaKam/FrugalScore'''    ,    )


   def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      if self.config_name == "default":
         _A:						Dict	    = '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
      else:
         _A:						str	    = self.config_name
      _A:						Any	    = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_  )
      _A:						Tuple	    = AutoTokenizer.from_pretrained(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Dict    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Any=3_2    ,    lowerCAmelCase_  :							str=1_2_8    ,    lowerCAmelCase_  :							str=None    ,    ):

      """simple docstring"""
      assert len(lowerCAmelCase_  ) == len(
          lowerCAmelCase_  ), "predictions and references should have the same number of sentences."
      if device is not None:
         assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
      else:
         _A:						Union[str, Any]	    = '''gpu''' if torch.cuda.is_available() else '''cpu'''
      _A:						Optional[int]	    = TrainingArguments(
          '''trainer'''    ,    fpaa=(device == '''gpu''')    ,    per_device_eval_batch_size=lowerCAmelCase_    ,    report_to='''all'''    ,    no_cuda=(device == '''cpu''')    ,    log_level='''warning'''    ,    )
      _A:						Optional[int]	    = {'''sentence1''': predictions, '''sentence2''': references}
      _A:						Dict	    = datasets.Dataset.from_dict(lowerCAmelCase_  )

      def tokenize_function(lowerCAmelCase_  :							Any  ):
         return self.tokenizer(
             data['''sentence1''']    ,    data['''sentence2''']    ,    max_length=lowerCAmelCase_    ,    truncation=lowerCAmelCase_    ,    padding=lowerCAmelCase_  )

      _A:						Dict	    = raw_datasets.map(lowerCAmelCase_    ,    batched=lowerCAmelCase_  )
      tokenized_datasets.remove_columns(['''sentence1''', '''sentence2''']  )
      _A:						Optional[int]	    = Trainer(self.model    ,    lowerCAmelCase_    ,    tokenizer=self.tokenizer  )
      _A:						int	    = trainer.predict(lowerCAmelCase_  )
      return {"scores": list(predictions.predictions.squeeze(-1  )  )}



