



"""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  			=		logging.get_logger(__name__)
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				SwinConfig(
          embed_dim=192      ,     depths=(2, 2, 18, 2)      ,     num_heads=(6, 12, 24, 48)      ,     window_size=12      ,     out_features=["stage2", "stage3", "stage4"]      ,     )

      UpperCAmelCase_     					=				DetaConfig(
          backbone_config=lowerCAmelCase__      ,     num_queries=900      ,     encoder_ffn_dim=2048      ,     decoder_ffn_dim=2048      ,     num_feature_levels=5      ,     assign_first_stage=lowerCAmelCase__      ,     with_box_refine=lowerCAmelCase__      ,     two_stage=lowerCAmelCase__      ,     )

      # set labels
      UpperCAmelCase_     					=				"huggingface/label-files"
      if "o365" in model_name:
            UpperCAmelCase_     					=				366
            UpperCAmelCase_     					=				"object365-id2label.json"
      else:
            UpperCAmelCase_     					=				91
            UpperCAmelCase_     					=				"coco-detection-id2label.json"

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

      return config
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[]

      # 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__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				dct.pop(lowerCAmelCase__ )
      UpperCAmelCase_     					=				val
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
      for i in range(len(backbone_config.depths ) ):
            UpperCAmelCase_     					=				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)
                  UpperCAmelCase_     					=				state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight""" )
                  UpperCAmelCase_     					=				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
                  UpperCAmelCase_     					=				in_proj_weight[:dim, :]
                  UpperCAmelCase_     					=				in_proj_bias[: dim]
                  UpperCAmelCase_     					=				in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_     					=				in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_     					=				in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase_     					=				in_proj_bias[-dim :]
                  # fmt: on
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      # transformer decoder self-attention layers
      UpperCAmelCase_     					=				config.d_model
      for i in range(config.decoder_layers ):
            # read in weights + bias of input projection layer of self-attention
            UpperCAmelCase_     					=				state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				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
            UpperCAmelCase_     					=				in_proj_weight[:hidden_size, :]
            UpperCAmelCase_     					=				in_proj_bias[:hidden_size]
            UpperCAmelCase_     					=				in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            UpperCAmelCase_     					=				in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_     					=				in_proj_weight[-hidden_size:, :]
            UpperCAmelCase_     					=				in_proj_bias[-hidden_size:]
def   a__      ( ):
      UpperCAmelCase_     					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_     					=				Image.open(requests.get(lowerCAmelCase__      ,     stream=lowerCAmelCase__ ).raw )

      return im
@torch.no_grad()
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				get_deta_config(lowerCAmelCase__ )

      # load original state dict
      if model_name == "deta-swin-large":
            UpperCAmelCase_     					=				hf_hub_download(repo_id="nielsr/deta-checkpoints"      ,     filename="adet_swin_ft.pth" )
      elif model_name == "deta-swin-large-o365":
            UpperCAmelCase_     					=				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""" )

      UpperCAmelCase_     					=				torch.load(lowerCAmelCase__      ,     map_location="cpu" )["model"]

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

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

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

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

      UpperCAmelCase_     					=				"cuda" if torch.cuda.is_available() else "cpu"
      model.to(lowerCAmelCase__ )

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

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

      # verify logits
      print("Logits:"      ,     outputs.logits[0, :3, :3] )
      print("Boxes:"      ,     outputs.pred_boxes[0, :3, :3] )
      if model_name == "deta-swin-large":
            UpperCAmelCase_     					=				torch.tensor(
                [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
            UpperCAmelCase_     					=				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":
            UpperCAmelCase_     					=				torch.tensor(
                [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
            UpperCAmelCase_     					=				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(lowerCAmelCase__ )      ,     atol=1e-4 )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,     expected_boxes.to(lowerCAmelCase__ )      ,     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(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
            model.save_pretrained(lowerCAmelCase__ )
            processor.save_pretrained(lowerCAmelCase__ )

      # 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  			=		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  			=		parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import comet  # From: unbabel-comet
import torch

import datasets


SCREAMING_SNAKE_CASE							    =							datasets.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

SCREAMING_SNAKE_CASE							    =							'\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

SCREAMING_SNAKE_CASE							    =							'\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


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


     """simple docstring"""


     def 	snake_case_  (	self    ):
        return datasets.MetricInfo(
            description=_DESCRIPTION  ,       citation=_CITATION  ,       homepage="""https://unbabel.github.io/COMET/html/index.html"""  ,       inputs_description=_KWARGS_DESCRIPTION  ,       features=datasets.Features(
                {
                    """sources""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """predictions""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """references""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                }    )  ,       codebase_urls=["""https://github.com/Unbabel/COMET"""]  ,       reference_urls=[
                """https://github.com/Unbabel/COMET""",
                """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
                """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
            ]  ,       )


     def 	snake_case_  (	self  ,       __A    ):
        if self.config_name == "default":
           __a					=  comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""    )    )
        else:
           __a					=  comet.load_from_checkpoint(comet.download_model(self.config_name    )    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None  ,       __A=False    ):
        if gpus is None:
           __a					=  1 if torch.cuda.is_available() else 0
        __a					=  {"""src""": sources, """mt""": predictions, """ref""": references}
        __a					=  [dict(zip(__A  ,       __A    )    ) for t in zip(*data.values()    )]
        __a	,    __a					=  self.scorer.predict(__A  ,       gpus=__A  ,       progress_bar=__A    )
        return {"mean_score": mean_score, "scores": scores}



