
'''simple docstring'''





import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a__						:      Union[str, Any]   				=				logging.get_logger(__name__)





def 				snake_case							(						UpperCAmelCase		)->	Dict:







      """simple docstring"""
      __A						=	SwinConfig.from_pretrained(
          'microsoft/swin-tiny-patch4-window7-224'					,     out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
      __A						=	MaskFormerConfig(backbone_config=_UpperCamelCase		)

      __A						=	'''huggingface/label-files'''
      if "ade20k-full" in model_name:
            # this should be ok
            __A						=	8_4_7
            __A						=	'''maskformer-ade20k-full-id2label.json'''
      elif "ade" in model_name:
            # this should be ok
            __A						=	1_5_0
            __A						=	'''ade20k-id2label.json'''
      elif "coco-stuff" in model_name:
            # this should be ok
            __A						=	1_7_1
            __A						=	'''maskformer-coco-stuff-id2label.json'''
      elif "coco" in model_name:
            # TODO
            __A						=	1_3_3
            __A						=	'''coco-panoptic-id2label.json'''
      elif "cityscapes" in model_name:
            # this should be ok
            __A						=	1_9
            __A						=	'''cityscapes-id2label.json'''
      elif "vistas" in model_name:
            # this should be ok
            __A						=	6_5
            __A						=	'''mapillary-vistas-id2label.json'''

      __A						=	json.load(open(hf_hub_download(_UpperCamelCase					,     _UpperCamelCase					,     repo_type='dataset'		)					,     'r'		)		)
      __A						=	{int(_UpperCamelCase		): v for k, v in idalabel.items()}

      return config





def 				snake_case							(						UpperCAmelCase		)->	Optional[int]:







      """simple docstring"""
      __A						=	[]
      # stem
      # fmt: off
      rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')		)
      rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')		)
      rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')		)
      rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')		)
      # stages
      for i in range(len(config.backbone_config.depths		)		):
            for j in range(config.backbone_config.depths[i]		):
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')		)
                  rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')		)

            if i < 3:
                  rename_keys.append((f'backbone.layers.{i}.downsample.reduction.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')		)
                  rename_keys.append((f'backbone.layers.{i}.downsample.norm.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')		)
                  rename_keys.append((f'backbone.layers.{i}.downsample.norm.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')		)
            rename_keys.append((f'backbone.norm{i}.weight', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')		)
            rename_keys.append((f'backbone.norm{i}.bias', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')		)

      # FPN
      rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')		)
      rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')		)
      rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')		)
      for source_index, target_index in zip(range(3					,     0					,     -1		)					,     range(0					,     3		)		):
            rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')		)
            rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')		)
            rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')		)
            rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')		)
            rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')		)
            rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')		)
      rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')		)
      rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')		)

      # Transformer decoder
      for idx in range(config.decoder_config.decoder_layers		):
            # self-attention out projection
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')		)
            # cross-attention out projection
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')		)
            # MLP 1
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias')		)
            # MLP 2
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias')		)
            # layernorm 1 (self-attention layernorm)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')		)
            # layernorm 2 (cross-attention layernorm)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')		)
            # layernorm 3 (final layernorm)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')		)

      rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')		)
      rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')		)

      # heads on top
      rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')		)

      rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')		)
      rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')		)

      rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')		)
      rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')		)

      for i in range(3		):
            rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight')		)
            rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias')		)
      # fmt: on

      return rename_keys





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase		)->	Union[str, Any]:







      """simple docstring"""
      __A						=	dct.pop(_UpperCamelCase		)
      __A						=	val





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	Any:







      """simple docstring"""
      __A						=	[int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
      for i in range(len(backbone_config.depths		)		):
            __A						=	num_features[i]
            for j in range(backbone_config.depths[i]		):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  __A						=	state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'		)
                  __A						=	state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'		)
                  # next, add query, keys and values (in that order) to the state dict
                  __A						=	in_proj_weight[:dim, :]
                  __A						=	in_proj_bias[: dim]
                  __A						=	in_proj_weight[
                      dim : dim * 2, :
                  ]
                  __A						=	in_proj_bias[
                      dim : dim * 2
                  ]
                  __A						=	in_proj_weight[
                      -dim :, :
                  ]
                  __A						=	in_proj_bias[-dim :]
                  # fmt: on





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	str:







      """simple docstring"""
      __A						=	config.decoder_config.hidden_size
      for idx in range(config.decoder_config.decoder_layers		):
            # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
            __A						=	state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'		)
            __A						=	state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'		)
            # next, add query, keys and values (in that order) to the state dict
            __A						=	in_proj_weight[: hidden_size, :]
            __A						=	in_proj_bias[:config.hidden_size]
            __A						=	in_proj_weight[hidden_size : hidden_size * 2, :]
            __A						=	in_proj_bias[hidden_size : hidden_size * 2]
            __A						=	in_proj_weight[-hidden_size :, :]
            __A						=	in_proj_bias[-hidden_size :]
            # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
            __A						=	state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'		)
            __A						=	state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'		)
            # next, add query, keys and values (in that order) to the state dict
            __A						=	in_proj_weight[: hidden_size, :]
            __A						=	in_proj_bias[:config.hidden_size]
            __A						=	in_proj_weight[hidden_size : hidden_size * 2, :]
            __A						=	in_proj_bias[hidden_size : hidden_size * 2]
            __A						=	in_proj_weight[-hidden_size :, :]
            __A						=	in_proj_bias[-hidden_size :]
      # fmt: on





def 				snake_case							(						)->	torch.Tensor:







      """simple docstring"""
      __A						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __A						=	Image.open(requests.get(_UpperCamelCase					,     stream=_UpperCamelCase		).raw		)
      return im





@torch.no_grad()
def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase = False		)->	int:







      """simple docstring"""
      __A						=	get_maskformer_config(_UpperCamelCase		)

      # load original state_dict
      with open(_UpperCamelCase					,     'rb'		) as f:
            __A						=	pickle.load(_UpperCamelCase		)
      __A						=	data['''model''']

      # for name, param in state_dict.items():
      #     print(name, param.shape)

      # rename keys
      __A						=	create_rename_keys(_UpperCamelCase		)
      for src, dest in rename_keys:
            rename_key(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase		)
      read_in_swin_q_k_v(_UpperCamelCase					,     config.backbone_config		)
      read_in_decoder_q_k_v(_UpperCamelCase					,     _UpperCamelCase		)

      # update to torch tensors
      for key, value in state_dict.items():
            __A						=	torch.from_numpy(_UpperCamelCase		)

      # load 🤗 model
      __A						=	MaskFormerForInstanceSegmentation(_UpperCamelCase		)
      model.eval()

      for name, param in model.named_parameters():
            print(_UpperCamelCase					,     param.shape		)

      __A						=	model.load_state_dict(_UpperCamelCase					,     strict=_UpperCamelCase		)
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(_UpperCamelCase		) == 0, f'Unexpected keys: {unexpected_keys}'

      # verify results
      __A						=	prepare_img()
      if "vistas" in model_name:
            __A						=	6_5
      elif "cityscapes" in model_name:
            __A						=	6_5_5_3_5
      else:
            __A						=	2_5_5
      __A						=	True if '''ade''' in model_name else False
      __A						=	MaskFormerImageProcessor(ignore_index=_UpperCamelCase					,     reduce_labels=_UpperCamelCase		)

      __A						=	image_processor(_UpperCamelCase					,     return_tensors='pt'		)

      __A						=	model(**_UpperCamelCase		)

      print('Logits:'					,     outputs.class_queries_logits[0, :3, :3]		)

      if model_name == "maskformer-swin-tiny-ade":
            __A						=	torch.tensor(
                [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]		)
      assert torch.allclose(outputs.class_queries_logits[0, :3, :3]					,     _UpperCamelCase					,     atol=1e-4		)
      print('Looks ok!'		)

      if pytorch_dump_folder_path is not None:
            print(f'Saving model and image processor to {pytorch_dump_folder_path}'		)
            Path(_UpperCamelCase		).mkdir(exist_ok=_UpperCamelCase		)
            model.save_pretrained(_UpperCamelCase		)
            image_processor.save_pretrained(_UpperCamelCase		)

      if push_to_hub:
            print('Pushing model and image processor to the hub...'		)
            model.push_to_hub(f'nielsr/{model_name}'		)
            image_processor.push_to_hub(f'nielsr/{model_name}'		)


if __name__ == "__main__":
 a__						:      Tuple   				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="maskformer-swin-tiny-ade",
     type=str,
     help=("Name of the MaskFormer model you\'d like to convert",),
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
     type=str,
     help="Path to the original state dict (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 a__						:      int   				=				parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )













import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase_    =				datasets.logging.get_logger(__name__)


lowerCAmelCase_    =				'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

lowerCAmelCase_    =				'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

lowerCAmelCase_    =				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

lowerCAmelCase_    =				{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}



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






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION					,							citation=_CITATION					,							homepage='''https://github.com/google-research/bleurt'''					,							inputs_description=_KWARGS_DESCRIPTION					,							features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''					,							id='''sequence'''    ),
							            '''references''': datasets.Value('''string'''					,							id='''sequence'''    ),
							        }    )					,							codebase_urls=['''https://github.com/google-research/bleurt''']					,							reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']					,							)






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							if self.config_name == "default":
											logger.warning(
											    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
											    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''    )
											snake_case_						:    Dict				=  '''bleurt-base-128'''

							if self.config_name.lower() in CHECKPOINT_URLS:
											snake_case_						:    Optional[int]				=  self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
											snake_case_						:    Union[str, Any]				=  self.config_name.upper()

							else:
											raise KeyError(
											    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''    )

							# download the model checkpoint specified by self.config_name and set up the scorer
							snake_case_						:    Any				=  dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
							snake_case_						:    Dict				=  score.BleurtScorer(os.path.join(__magic_name__					,							__magic_name__    )    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.scorer.score(references=__magic_name__					,							candidates=__magic_name__    )
							return {"scores": scores}
