






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()
__snake_case      :      Tuple             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				int:

      """simple docstring"""



      lowerCAmelCase__        =  SwinConfig.from_pretrained(
          'microsoft/swin-tiny-patch4-window7-224'      ,    out_features=['stage1', 'stage2', 'stage3', 'stage4']						)
      lowerCAmelCase__        =  MaskFormerConfig(backbone_config=UpperCamelCase_						)

      lowerCAmelCase__        =  'huggingface/label-files'
      if "ade20k-full" in model_name:
            # this should be ok
            lowerCAmelCase__        =  847
            lowerCAmelCase__        =  'maskformer-ade20k-full-id2label.json'
      elif "ade" in model_name:
            # this should be ok
            lowerCAmelCase__        =  150
            lowerCAmelCase__        =  'ade20k-id2label.json'
      elif "coco-stuff" in model_name:
            # this should be ok
            lowerCAmelCase__        =  171
            lowerCAmelCase__        =  'maskformer-coco-stuff-id2label.json'
      elif "coco" in model_name:
            # TODO
            lowerCAmelCase__        =  133
            lowerCAmelCase__        =  'coco-panoptic-id2label.json'
      elif "cityscapes" in model_name:
            # this should be ok
            lowerCAmelCase__        =  19
            lowerCAmelCase__        =  'cityscapes-id2label.json'
      elif "vistas" in model_name:
            # this should be ok
            lowerCAmelCase__        =  65
            lowerCAmelCase__        =  'mapillary-vistas-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()}

      return config







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple						)					->				Union[str, Any]:

      """simple docstring"""



      lowerCAmelCase__        =  []
      # 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 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   str						)					->				int:

      """simple docstring"""



      lowerCAmelCase__        =  dct.pop(UpperCamelCase_						)
      lowerCAmelCase__        =  val







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Dict						)					->				Dict:

      """simple docstring"""



      lowerCAmelCase__        =  [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
      for i in range(len(backbone_config.depths						)						):
            lowerCAmelCase__        =  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)
                  lowerCAmelCase__        =  state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"						)
                  lowerCAmelCase__        =  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
                  lowerCAmelCase__        =  in_proj_weight[:dim, :]
                  lowerCAmelCase__        =  in_proj_bias[: dim]
                  lowerCAmelCase__        =  in_proj_weight[
                      dim : dim * 2, :
                  ]
                  lowerCAmelCase__        =  in_proj_bias[
                      dim : dim * 2
                  ]
                  lowerCAmelCase__        =  in_proj_weight[
                      -dim :, :
                  ]
                  lowerCAmelCase__        =  in_proj_bias[-dim :]
                  # fmt: on







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str						)					->				Any:

      """simple docstring"""



      lowerCAmelCase__        =  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)
            lowerCAmelCase__        =  state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"						)
            lowerCAmelCase__        =  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
            lowerCAmelCase__        =  in_proj_weight[: hidden_size, :]
            lowerCAmelCase__        =  in_proj_bias[:config.hidden_size]
            lowerCAmelCase__        =  in_proj_weight[hidden_size : hidden_size * 2, :]
            lowerCAmelCase__        =  in_proj_bias[hidden_size : hidden_size * 2]
            lowerCAmelCase__        =  in_proj_weight[-hidden_size :, :]
            lowerCAmelCase__        =  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)
            lowerCAmelCase__        =  state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"						)
            lowerCAmelCase__        =  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
            lowerCAmelCase__        =  in_proj_weight[: hidden_size, :]
            lowerCAmelCase__        =  in_proj_bias[:config.hidden_size]
            lowerCAmelCase__        =  in_proj_weight[hidden_size : hidden_size * 2, :]
            lowerCAmelCase__        =  in_proj_bias[hidden_size : hidden_size * 2]
            lowerCAmelCase__        =  in_proj_weight[-hidden_size :, :]
            lowerCAmelCase__        =  in_proj_bias[-hidden_size :]
      # fmt: on







def 		_UpperCamelCase				(      )					->				torch.Tensor:

      """simple docstring"""



      lowerCAmelCase__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCAmelCase__        =  Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_						).raw						)
      return im







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   bool = False						)					->				int:

      """simple docstring"""



      lowerCAmelCase__        =  get_maskformer_config(UpperCamelCase_						)

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

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

      # rename keys
      lowerCAmelCase__        =  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():
            lowerCAmelCase__        =  torch.from_numpy(UpperCamelCase_						)

      # load 🤗 model
      lowerCAmelCase__        =  MaskFormerForInstanceSegmentation(UpperCamelCase_						)
      model.eval()

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

      lowerCAmelCase__  ,    lowerCAmelCase__        =  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
      lowerCAmelCase__        =  prepare_img()
      if "vistas" in model_name:
            lowerCAmelCase__        =  65
      elif "cityscapes" in model_name:
            lowerCAmelCase__        =  6_5535
      else:
            lowerCAmelCase__        =  255
      lowerCAmelCase__        =  True if 'ade' in model_name else False
      lowerCAmelCase__        =  MaskFormerImageProcessor(ignore_index=UpperCamelCase_      ,    reduce_labels=UpperCamelCase_						)

      lowerCAmelCase__        =  image_processor(UpperCamelCase_      ,    return_tensors='pt'						)

      lowerCAmelCase__        =  model(**UpperCamelCase_						)

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

      if model_name == "maskformer-swin-tiny-ade":
            lowerCAmelCase__        =  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__":
     __snake_case      :      Optional[int]             =       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."""
     )

     __snake_case      :      Union[str, Any]             =       parser.parse_args()
     convert_maskformer_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )













import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__snake_case      :      Optional[int]             =       """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__snake_case      :      List[str]             =       """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

__snake_case      :      Dict             =       """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""


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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if version.parse(scb.__version__				) < version.parse('1.4.12'				):
																		raise ImportWarning(
																		    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
																		    'You can install it with `pip install "sacrebleu>=1.4.12"`.'				)
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,  citation=_CITATION       ,  homepage='http://www.cs.umd.edu/~snover/tercom/'       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
												        {
												            'predictions': datasets.Value('string'       ,  id='sequence'				),
												            'references': datasets.Sequence(datasets.Value('string'       ,  id='sequence'				)       ,  id='references'				),
												        }				)       ,  codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']       ,  reference_urls=[
												        'https://github.com/jhclark/tercom',
												    ]       ,  )







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  len(references[0]				)
												if any(len(_UpperCamelCase				) != references_per_prediction for refs in references				):
																		raise ValueError('Sacrebleu requires the same number of references for each prediction'				)
												lowerCAmelCase__        =  [[refs[i] for refs in references] for i in range(_UpperCamelCase				)]

												lowerCAmelCase__        =  TER(
												    normalized=_UpperCamelCase       ,  no_punct=_UpperCamelCase       ,  asian_support=_UpperCamelCase       ,  case_sensitive=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  sb_ter.corpus_score(_UpperCamelCase       ,  _UpperCamelCase				)

												return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}





