




"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


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



def 					a__				(       snake_case__       )		->      int:
			lowerCamelCase       =      SwinConfig(
			    embed_dim=1_92 ,				depths=(2, 2, 18, 2) ,				num_heads=(6, 12, 24, 48) ,				window_size=12 ,				out_features=["""stage2""", """stage3""", """stage4"""] ,				)

			lowerCamelCase       =      DetaConfig(
			    backbone_config=snake_case__ ,				num_queries=9_00 ,				encoder_ffn_dim=20_48 ,				decoder_ffn_dim=20_48 ,				num_feature_levels=5 ,				assign_first_stage=snake_case__ ,				with_box_refine=snake_case__ ,				two_stage=snake_case__ ,				)

			# set labels
			lowerCamelCase       =      """huggingface/label-files"""
			if "o365" in model_name:
						lowerCamelCase       =      3_66
						lowerCamelCase       =      """object365-id2label.json"""
			else:
						lowerCamelCase       =      91
						lowerCamelCase       =      """coco-detection-id2label.json"""

			lowerCamelCase       =      num_labels
			lowerCamelCase       =      json.load(open(cached_download(hf_hub_url(snake_case__ ,				snake_case__ ,				repo_type="""dataset"""       )       ) ,				"""r"""       )       )
			lowerCamelCase       =      {int(snake_case__       ): v for k, v in idalabel.items()}
			lowerCamelCase       =      idalabel
			lowerCamelCase       =      {v: k for k, v in idalabel.items()}

			return config



def 					a__				(       snake_case__       )		->      str:
			lowerCamelCase       =      []

			# stem
			# fmt: off
			rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")       )
			rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")       )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")       )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.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.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')       )

						if i < 3:
									rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')       )
									rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')       )

			rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")       )
			rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")       )
			rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")       )
			rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")       )
			rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")       )
			rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")       )

			# transformer encoder
			for i in range(config.encoder_layers       ):
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')       )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')       )

			# transformer decoder
			for i in range(config.decoder_layers       ):
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')       )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')       )

			# fmt: on

			return rename_keys



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[Any]:
			lowerCamelCase       =      dct.pop(snake_case__       )
			lowerCamelCase       =      val



def 					a__				(       snake_case__ ,				snake_case__       )		->      Optional[Any]:
			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.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'       )
									lowerCamelCase       =      state_dict.pop(F'backbone.0.body.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 					a__				(       snake_case__ ,				snake_case__       )		->      Union[str, Any]:
			# transformer decoder self-attention layers
			lowerCamelCase       =      config.d_model
			for i in range(config.decoder_layers       ):
						# read in weights + bias of input projection layer of self-attention
						lowerCamelCase       =      state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
						lowerCamelCase       =      state_dict.pop(F'transformer.decoder.layers.{i}.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[: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:]



def 					a__				(       )		->      Any:
			lowerCamelCase       =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase       =      Image.open(requests.get(snake_case__ ,				stream=snake_case__       ).raw       )

			return im



@torch.no_grad()
def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Tuple:
			lowerCamelCase       =      get_deta_config(snake_case__       )

			# load original state dict
			if model_name == "deta-swin-large":
						lowerCamelCase       =      hf_hub_download(repo_id="""nielsr/deta-checkpoints""" ,				filename="""adet_swin_ft.pth"""       )
			elif model_name == "deta-swin-large-o365":
						lowerCamelCase       =      hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""" ,				filename="""deta_swin_pt_o365.pth"""       )
			else:
						raise ValueError(F'Model name {model_name} not supported'       )

			lowerCamelCase       =      torch.load(snake_case__ ,				map_location="""cpu"""       )["""model"""]

			# original state dict
			for name, param in state_dict.items():
						print(snake_case__ ,				param.shape       )

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

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									lowerCamelCase       =      state_dict.pop(snake_case__       )
									lowerCamelCase       =      val
						if "input_proj" in key:
									lowerCamelCase       =      state_dict.pop(snake_case__       )
									lowerCamelCase       =      val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									lowerCamelCase       =      state_dict.pop(snake_case__       )
									lowerCamelCase       =      val

    # finally, create HuggingFace model and load state dict
			lowerCamelCase       =      DetaForObjectDetection(snake_case__       )
			model.load_state_dict(snake_case__       )
			model.eval()

			lowerCamelCase       =      """cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(snake_case__       )

			# load image processor
			lowerCamelCase       =      DetaImageProcessor(format="""coco_detection"""       )

			# verify our conversion on image
			lowerCamelCase       =      prepare_img()
			lowerCamelCase       =      processor(images=snake_case__ ,				return_tensors="""pt"""       )
			lowerCamelCase       =      encoding["""pixel_values"""]
			lowerCamelCase       =      model(pixel_values.to(snake_case__       )       )

			# verify logits
			print("""Logits:""" ,				outputs.logits[0, :3, :3]       )
			print("""Boxes:""" ,				outputs.pred_boxes[0, :3, :3]       )
			if model_name == "deta-swin-large":
						lowerCamelCase       =      torch.tensor(
						    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]       )
						lowerCamelCase       =      torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]       )
			elif model_name == "deta-swin-large-o365":
						lowerCamelCase       =      torch.tensor(
						    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]       )
						lowerCamelCase       =      torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]       )
			assert torch.allclose(outputs.logits[0, :3, :3] ,				expected_logits.to(snake_case__       ) ,				atol=1E-4       )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,				expected_boxes.to(snake_case__       ) ,				atol=1E-4       )
			print("""Everything ok!"""       )

			if pytorch_dump_folder_path:
						# Save model and processor
						logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'       )
						Path(snake_case__       ).mkdir(exist_ok=snake_case__       )
						model.save_pretrained(snake_case__       )
						processor.save_pretrained(snake_case__       )

			# Push to hub
			if push_to_hub:
						print("""Pushing model and processor to hub..."""       )
						model.push_to_hub(F'jozhang97/{model_name}'       )
						processor.push_to_hub(F'jozhang97/{model_name}'       )


if __name__ == "__main__":
					lowerCAmelCase							:    Any  							=							argparse.ArgumentParser()

					parser.add_argument(
					    """--model_name""",
					    type=str,
					    default="""deta-swin-large""",
					    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
					    help="""Name of the model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    help="""Path to the folder to output PyTorch model.""",
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)
					lowerCAmelCase							:    List[str]  							=							parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase							:    List[Any]  							=							"""\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@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\",
}
"""

lowerCAmelCase							:    List[str]  							=							"""\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

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#chrf--chrf for more information.
"""

lowerCAmelCase							:    str  							=							"""
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""






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






				'''simple docstring'''





				def      _lowerCAmelCase       ( 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="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""	,				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#chrf--chrf"""]	,				reference_urls=[
							        """https://github.com/m-popovic/chrF""",
							    ]	,				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a = CHRF.CHAR_ORDER	,				_a = CHRF.WORD_ORDER	,				_a = CHRF.BETA	,				_a = False	,				_a = False	,				_a = False	,				):





							"""simple docstring"""
							lowerCamelCase       =      len(references[0]				)
							if any(len(_a				) != 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(_a				)]

							lowerCamelCase       =      CHRF(_a	,				_a	,				_a	,				_a	,				_a	,				_a				)
							lowerCamelCase       =      sb_chrf.corpus_score(_a	,				_a				)

							return {
							    "score": output.score,
							    "char_order": output.char_order,
							    "word_order": output.word_order,
							    "beta": output.beta,
							}



