






import comet  # From: unbabel-comet
import torch

import datasets


snake_case     					=		datasets.logging.get_logger(__name__)

snake_case     					=		"""\
@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\",
}
"""

snake_case     					=		"""\
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.
"""

snake_case     					=		"""
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 			SCREAMING_SNAKE_CASE      (      datasets.Metric       ):
     '''simple docstring'''


     def 					_A							(					self							:						Optional[int]   ):
       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							:						List[Any]       ,			UpperCAmelCase_							:						List[Any]   ):
       if self.config_name == "default":
         SCREAMING_SNAKE_CASE							:   int						    =     comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"   )   )
       else:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     comet.load_from_checkpoint(comet.download_model(self.config_name   )   )






     def 					_A							(					self							:						Dict       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Dict=None       ,			UpperCAmelCase_							:						Optional[int]=False   ):
       if gpus is None:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     1 if torch.cuda.is_available() else 0
       SCREAMING_SNAKE_CASE							:   int						    =     {"src": sources, "mt": predictions, "ref": references}
       SCREAMING_SNAKE_CASE							:   str						    =     [dict(zip(UpperCAmelCase_       ,			UpperCAmelCase_   )   ) for t in zip(*data.values()   )]
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     self.scorer.predict(UpperCAmelCase_       ,			gpus=UpperCAmelCase_       ,			progress_bar=UpperCAmelCase_   )
       return {"mean_score": mean_score, "scores": scores}













"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_					=     logging.get_logger(__name__)

a_					=     {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""poolformer"""

 def __init__(       self   ,		__lowerCamelCase=3   ,		__lowerCamelCase=16   ,		__lowerCamelCase=16   ,		__lowerCamelCase=3   ,		__lowerCamelCase=4.0   ,		__lowerCamelCase=[2, 2, 6, 2]   ,		__lowerCamelCase=[64, 128, 320, 512]   ,		__lowerCamelCase=[7, 3, 3, 3]   ,		__lowerCamelCase=[4, 2, 2, 2]   ,		__lowerCamelCase=[2, 1, 1, 1]   ,		__lowerCamelCase=4   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=True   ,		__lowerCamelCase=1e-5   ,		__lowerCamelCase=0.0_2   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		str							=	num_channels
     __A       :		List[str]							=	patch_size
     __A       :		str							=	stride
     __A       :		Any							=	padding
     __A       :		Any							=	pool_size
     __A       :		Dict							=	hidden_sizes
     __A       :		Optional[Any]							=	mlp_ratio
     __A       :		Any							=	depths
     __A       :		List[str]							=	patch_sizes
     __A       :		Union[str, Any]							=	strides
     __A       :		List[str]							=	num_encoder_blocks
     __A       :		Optional[int]							=	drop_path_rate
     __A       :		Union[str, Any]							=	hidden_act
     __A       :		Optional[Any]							=	use_layer_scale
     __A       :		List[Any]							=	layer_scale_init_value
     __A       :		List[Any]							=	initializer_range
     super().__init__(**__lowerCamelCase    )






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	version.parse("""1.11"""	)

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ]    )







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 2e-3




