




import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)







def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    List[str]					=					XCLIPTextConfig()

				# derive patch size from model name
				_lowerCamelCase  :    List[str]					=					model_name.find('patch'	)
				_lowerCamelCase  :    Any					=					int(model_name[start_idx + len('patch'	) : start_idx + len('patch'	) + 2]	)
				_lowerCamelCase  :    Dict					=					XCLIPVisionConfig(patch_size=__A							,					num_frames=__A	)

				if "large" in model_name:
								_lowerCamelCase  :    Any					=					768
								_lowerCamelCase  :    Optional[Any]					=					3072
								_lowerCamelCase  :    Union[str, Any]					=					12

								_lowerCamelCase  :    str					=					1024
								_lowerCamelCase  :    Any					=					4096
								_lowerCamelCase  :    List[Any]					=					16
								_lowerCamelCase  :    Optional[int]					=					24
								_lowerCamelCase  :    Optional[Any]					=					768
								_lowerCamelCase  :    str					=					3072

				if model_name == "xclip-large-patch14-16-frames":
								_lowerCamelCase  :    Tuple					=					336

				_lowerCamelCase  :    Tuple					=					XCLIPConfig.from_text_vision_configs(__A							,					__A	)

				if "large" in model_name:
								_lowerCamelCase  :    Dict					=					768

				return config







def   _snake_case    (       lowercase__	):




				if name == "token_embedding.weight":
								_lowerCamelCase  :    List[Any]					=					name.replace('token_embedding.weight'							,					'text_model.embeddings.token_embedding.weight'	)
				if name == "positional_embedding":
								_lowerCamelCase  :    Tuple					=					name.replace('positional_embedding'							,					'text_model.embeddings.position_embedding.weight'	)
				if "ln_1" in name:
								_lowerCamelCase  :    Optional[int]					=					name.replace('ln_1'							,					'layer_norm1'	)
				if "ln_2" in name:
								_lowerCamelCase  :    int					=					name.replace('ln_2'							,					'layer_norm2'	)
				if "c_fc" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('c_fc'							,					'fc1'	)
				if "c_proj" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('c_proj'							,					'fc2'	)
				if name.startswith('transformer.resblocks'	):
								_lowerCamelCase  :    Tuple					=					name.replace('transformer.resblocks'							,					'text_model.encoder.layers'	)
				if "attn.out_proj" in name and "message" not in name:
								_lowerCamelCase  :    Optional[Any]					=					name.replace('attn.out_proj'							,					'self_attn.out_proj'	)
				if "ln_final" in name:
								_lowerCamelCase  :    str					=					name.replace('ln_final'							,					'text_model.final_layer_norm'	)
				# visual encoder
				if name == "visual.class_embedding":
								_lowerCamelCase  :    str					=					name.replace('visual.class_embedding'							,					'vision_model.embeddings.class_embedding'	)
				if name == "visual.positional_embedding":
								_lowerCamelCase  :    Tuple					=					name.replace('visual.positional_embedding'							,					'vision_model.embeddings.position_embedding.weight'	)
				if name.startswith('visual.transformer.resblocks'	):
								_lowerCamelCase  :    Tuple					=					name.replace('visual.transformer.resblocks'							,					'vision_model.encoder.layers'	)
				if "visual.conv1" in name:
								_lowerCamelCase  :    Optional[int]					=					name.replace('visual.conv1'							,					'vision_model.embeddings.patch_embedding'	)
				if "visual.ln_pre" in name:
								_lowerCamelCase  :    int					=					name.replace('visual.ln_pre'							,					'vision_model.pre_layernorm'	)
				if "visual.ln_post" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('visual.ln_post'							,					'vision_model.post_layernorm'	)
				if "visual.proj" in name:
								_lowerCamelCase  :    str					=					name.replace('visual.proj'							,					'visual_projection.weight'	)
				if "text_projection" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('text_projection'							,					'text_projection.weight'	)
				# things on top
				if "prompts_visual_proj" in name:
								_lowerCamelCase  :    List[Any]					=					name.replace('prompts_visual_proj'							,					'prompts_visual_projection'	)
				if "prompts_visual_ln" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('prompts_visual_ln'							,					'prompts_visual_layernorm'	)
				# mit
				if name == "mit.positional_embedding":
								_lowerCamelCase  :    Optional[int]					=					name.replace('positional'							,					'position'	)
				if name.startswith('mit.resblocks'	):
								_lowerCamelCase  :    int					=					name.replace('mit.resblocks'							,					'mit.encoder.layers'	)
				# prompts generator
				if name.startswith('prompts_generator.norm'	):
								_lowerCamelCase  :    Union[str, Any]					=					name.replace('prompts_generator.norm'							,					'prompts_generator.layernorm'	)

				return name







def   _snake_case    (       lowercase__							,					lowercase__	):




				for key in orig_state_dict.copy().keys():
								_lowerCamelCase  :    Union[str, Any]					=					orig_state_dict.pop(__A	)

								if "attn.in_proj" in key:
												_lowerCamelCase  :    int					=					key.split('.'	)
												if key.startswith('visual'	):
																_lowerCamelCase  :    Dict					=					key_split[3]
																_lowerCamelCase  :    List[str]					=					config.vision_config.hidden_size
																if "message_attn" in key:
																				if "weight" in key:
																								_lowerCamelCase  :    List[str]					=					val[
																								    :dim, :
																								]
																								_lowerCamelCase  :    List[str]					=					val[
																								    dim : dim * 2, :
																								]
																								_lowerCamelCase  :    Optional[Any]					=					val[
																								    -dim:, :
																								]
																				else:
																								_lowerCamelCase  :    List[str]					=					val[
																								    :dim
																								]
																								_lowerCamelCase  :    Dict					=					val[
																								    dim : dim * 2
																								]
																								_lowerCamelCase  :    Any					=					val[
																								    -dim:
																								]
																else:
																				if "weight" in key:
																								_lowerCamelCase  :    Optional[Any]					=					val[
																								    :dim, :
																								]
																								_lowerCamelCase  :    Union[str, Any]					=					val[
																								    dim : dim * 2, :
																								]
																								_lowerCamelCase  :    int					=					val[
																								    -dim:, :
																								]
																				else:
																								_lowerCamelCase  :    Optional[Any]					=					val[:dim]
																								_lowerCamelCase  :    Tuple					=					val[
																								    dim : dim * 2
																								]
																								_lowerCamelCase  :    List[Any]					=					val[-dim:]
												elif key.startswith('mit'	):
																_lowerCamelCase  :    str					=					key_split[2]
																_lowerCamelCase  :    Dict					=					config.vision_config.mit_hidden_size
																if "weight" in key:
																				_lowerCamelCase  :    Any					=					val[:dim, :]
																				_lowerCamelCase  :    Optional[int]					=					val[dim : dim * 2, :]
																				_lowerCamelCase  :    Tuple					=					val[-dim:, :]
																else:
																				_lowerCamelCase  :    List[Any]					=					val[:dim]
																				_lowerCamelCase  :    int					=					val[dim : dim * 2]
																				_lowerCamelCase  :    Union[str, Any]					=					val[-dim:]
												else:
																_lowerCamelCase  :    Dict					=					key_split[2]
																_lowerCamelCase  :    str					=					config.text_config.hidden_size
																if "weight" in key:
																				_lowerCamelCase  :    Union[str, Any]					=					val[:dim, :]
																				_lowerCamelCase  :    Optional[Any]					=					val[
																				    dim : dim * 2, :
																				]
																				_lowerCamelCase  :    List[Any]					=					val[-dim:, :]
																else:
																				_lowerCamelCase  :    Dict					=					val[:dim]
																				_lowerCamelCase  :    List[Any]					=					val[
																				    dim : dim * 2
																				]
																				_lowerCamelCase  :    List[Any]					=					val[-dim:]
								else:
												_lowerCamelCase  :    Optional[int]					=					rename_key(__A	)
												if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																_lowerCamelCase  :    List[str]					=					val.T
												_lowerCamelCase  :    Optional[int]					=					val

				return orig_state_dict







def   _snake_case    (       lowercase__	):




				if num_frames == 8:
								_lowerCamelCase  :    Union[str, Any]					=					'''eating_spaghetti_8_frames.npy'''
				elif num_frames == 16:
								_lowerCamelCase  :    Union[str, Any]					=					'''eating_spaghetti.npy'''
				elif num_frames == 32:
								_lowerCamelCase  :    Any					=					'''eating_spaghetti_32_frames.npy'''
				_lowerCamelCase  :    List[Any]					=					hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'							,					filename=__A							,					repo_type='dataset'							,					)
				_lowerCamelCase  :    int					=					np.load(__A	)
				return list(__A	)







def   _snake_case    (       lowercase__							,					lowercase__=None							,					lowercase__=False	):
				_lowerCamelCase  :    Dict					=					{
				    # fully supervised kinetics-400 checkpoints
				    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
				    '''xclip-base-patch32-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
				    ),
				    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
				    '''xclip-base-patch16-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
				    ),
				    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
				    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
				    # fully supervised kinetics-600 checkpoints
				    '''xclip-base-patch16-kinetics-600''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
				    ),
				    '''xclip-base-patch16-kinetics-600-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
				    ),
				    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
				    # few shot
				    '''xclip-base-patch16-hmdb-2-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-4-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-8-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-16-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
				    ),
				    '''xclip-base-patch16-ucf-2-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
				    ),
				    '''xclip-base-patch16-ucf-4-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
				    ),
				    '''xclip-base-patch16-ucf-8-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
				    ),
				    '''xclip-base-patch16-ucf-16-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
				    ),
				    # zero shot
				    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
				}

				_lowerCamelCase  :    int					=					model_to_url[model_name]
				_lowerCamelCase  :    int					=					8
				if "16-frames" in model_name:
								_lowerCamelCase  :    Optional[Any]					=					16
				elif "shot" in model_name:
								_lowerCamelCase  :    int					=					32

				_lowerCamelCase  :    List[str]					=					get_xclip_config(__A							,					__A	)
				_lowerCamelCase  :    Union[str, Any]					=					XCLIPModel(__A	)
				model.eval()

				if "drive" in checkpoint_url:
								_lowerCamelCase  :    Optional[int]					=					'''pytorch_model.bin'''
								gdown.cached_download(__A							,					__A							,					quiet=__A	)
								_lowerCamelCase  :    Optional[int]					=					torch.load(__A							,					map_location='cpu'	)['''model''']
				else:
								_lowerCamelCase  :    Any					=					torch.hub.load_state_dict_from_url(__A	)['''model''']

				_lowerCamelCase  :    str					=					convert_state_dict(__A							,					__A	)

				_lowerCamelCase  :    Optional[int]					=					XCLIPModel(__A	)
				_lowerCamelCase  :    List[Any]					=					model.load_state_dict(__A							,					strict=__A	)
				assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
				model.eval()

				_lowerCamelCase  :    Any					=					336 if model_name == '''xclip-large-patch14-16-frames''' else 224
				_lowerCamelCase  :    str					=					VideoMAEImageProcessor(size=__A	)
				_lowerCamelCase  :    Dict					=					CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'	)
				_lowerCamelCase  :    int					=					CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'	)
				_lowerCamelCase  :    str					=					XCLIPProcessor(image_processor=__A							,					tokenizer=__A	)

				_lowerCamelCase  :    str					=					prepare_video(__A	)
				_lowerCamelCase  :    Optional[Any]					=					processor(
				    text=['playing sports', 'eating spaghetti', 'go shopping']							,					videos=__A							,					return_tensors='pt'							,					padding=__A	)

				print('Shape of pixel values:'							,					inputs.pixel_values.shape	)

				with torch.no_grad():
								_lowerCamelCase  :    Tuple					=					model(**__A	)

				# Verify outputs
				_lowerCamelCase  :    str					=					outputs.logits_per_video
				_lowerCamelCase  :    Optional[Any]					=					logits_per_video.softmax(dim=1	)
				print('Probs:'							,					__A	)
				# kinetics-400
				if model_name == "xclip-base-patch32":
								_lowerCamelCase  :    Any					=					torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]	)
				elif model_name == "xclip-base-patch32-16-frames":
								_lowerCamelCase  :    Union[str, Any]					=					torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]	)
				elif model_name == "xclip-base-patch16":
								_lowerCamelCase  :    Optional[Any]					=					torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]	)
				elif model_name == "xclip-base-patch16-16-frames":
								_lowerCamelCase  :    Optional[int]					=					torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]	)
				elif model_name == "xclip-large-patch14":
								_lowerCamelCase  :    Tuple					=					torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]	)
				elif model_name == "xclip-large-patch14-16-frames":
								_lowerCamelCase  :    List[str]					=					torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]	)
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								_lowerCamelCase  :    int					=					torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]	)
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								_lowerCamelCase  :    Tuple					=					torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]	)
				elif model_name == "xclip-large-patch14-kinetics-600":
								_lowerCamelCase  :    Tuple					=					torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]	)
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								_lowerCamelCase  :    int					=					torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]	)
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								_lowerCamelCase  :    Union[str, Any]					=					torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]	)
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								_lowerCamelCase  :    str					=					torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]	)
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								_lowerCamelCase  :    Optional[int]					=					torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]	)
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								_lowerCamelCase  :    Optional[Any]					=					torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]	)
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								_lowerCamelCase  :    str					=					torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]	)
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								_lowerCamelCase  :    List[Any]					=					torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]	)
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								_lowerCamelCase  :    str					=					torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]	)
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								_lowerCamelCase  :    Dict					=					torch.tensor([[3.5_082E-04, 9.9_785E-01, 1.7_966E-03]]	)
				else:
								raise ValueError(f'''Model name {model_name} not supported'''	)
				assert torch.allclose(__A							,					__A							,					atol=1E-3	)
				print('Looks ok!'	)

				if pytorch_dump_folder_path is not None:
								print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
								model.save_pretrained(__A	)

				if push_to_hub:
								print('Pushing model, processor and slow tokenizer files to the hub...'	)
								model.push_to_hub(__A							,					organization='nielsr'	)
								processor.push_to_hub(__A							,					organization='nielsr'	)
								slow_tokenizer.push_to_hub(__A							,					organization='nielsr'	)


if __name__ == "__main__":
			lowercase__  		=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default="""xclip-base-patch32""",
			    type=str,
			    help="""Name of the model.""",
			)
			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."""
			)

			lowercase__  		=		parser.parse_args()
			convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






import comet  # From: unbabel-comet
import torch

import datasets


lowercase__  		=		datasets.logging.get_logger(__name__)

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

lowercase__  		=		"""\
Crosslingual 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).
With 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.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""

lowercase__  		=		"""
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric('comet')
    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use
    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [0.19, 0.92]
"""






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






			'''simple docstring'''



			def       A_					(						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       A_					(						self						,   lowercase						):
							if self.config_name == "default":
											_lowerCamelCase  :    List[Any]					=					comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'						)						)
							else:
											_lowerCamelCase  :    Tuple					=					comet.load_from_checkpoint(comet.download_model(self.config_name						)						)






			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=False						):
							if gpus is None:
											_lowerCamelCase  :    Tuple					=					1 if torch.cuda.is_available() else 0
							_lowerCamelCase  :    List[Any]					=					{'src': sources, 'mt': predictions, 'ref': references}
							_lowerCamelCase  :    int					=					[dict(zip(lowercase						,   lowercase						)						) for t in zip(*data.values()						)]
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self.scorer.predict(lowercase						,   gpus=lowercase						,   progress_bar=lowercase						)
							return {"mean_score": mean_score, "scores": scores}