

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[int]             =      {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "efficientnet"






  def __init__(							self						:		int     ,       __lowerCamelCase						:		int = 3     ,       __lowerCamelCase						:		int = 600     ,       __lowerCamelCase						:		float = 2.0     ,       __lowerCamelCase						:		float = 3.1     ,       __lowerCamelCase						:		int = 8     ,       __lowerCamelCase						:		List[int] = [3, 3, 5, 3, 5, 5, 3]     ,       __lowerCamelCase						:		List[int] = [32, 16, 24, 40, 80, 112, 192]     ,       __lowerCamelCase						:		List[int] = [16, 24, 40, 80, 112, 192, 320]     ,       __lowerCamelCase						:		List[int] = []     ,       __lowerCamelCase						:		List[int] = [1, 2, 2, 2, 1, 2, 1]     ,       __lowerCamelCase						:		List[int] = [1, 2, 2, 3, 3, 4, 1]     ,       __lowerCamelCase						:		List[int] = [1, 6, 6, 6, 6, 6, 6]     ,       __lowerCamelCase						:		float = 0.25     ,       __lowerCamelCase						:		str = "swish"     ,       __lowerCamelCase						:		int = 2560     ,       __lowerCamelCase						:		str = "mean"     ,       __lowerCamelCase						:		float = 0.02     ,       __lowerCamelCase						:		float = 0.001     ,       __lowerCamelCase						:		float = 0.99     ,       __lowerCamelCase						:		float = 0.5     ,       __lowerCamelCase						:		float = 0.2     ,       **__lowerCamelCase						:		Any     ,       )		->							Dict:
       super().__init__(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   width_coefficient
       SCREAMING_SNAKE_CASE__   =   depth_coefficient
       SCREAMING_SNAKE_CASE__   =   depth_divisor
       SCREAMING_SNAKE_CASE__   =   kernel_sizes
       SCREAMING_SNAKE_CASE__   =   in_channels
       SCREAMING_SNAKE_CASE__   =   out_channels
       SCREAMING_SNAKE_CASE__   =   depthwise_padding
       SCREAMING_SNAKE_CASE__   =   strides
       SCREAMING_SNAKE_CASE__   =   num_block_repeats
       SCREAMING_SNAKE_CASE__   =   expand_ratios
       SCREAMING_SNAKE_CASE__   =   squeeze_expansion_ratio
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dim
       SCREAMING_SNAKE_CASE__   =   pooling_type
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   batch_norm_eps
       SCREAMING_SNAKE_CASE__   =   batch_norm_momentum
       SCREAMING_SNAKE_CASE__   =   dropout_rate
       SCREAMING_SNAKE_CASE__   =   drop_connect_rate
       SCREAMING_SNAKE_CASE__   =   sum(__lowerCamelCase ) * 4






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= version.parse("1.11"	)






  @property
  def 				lowercase_		(							self						:		List[Any] )		->							Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ] )







  @property
  def 				lowercase_		(							self						:		Dict )		->							float:
       return 1e-5





import comet  # From: unbabel-comet
import torch

import datasets


_SCREAMING_SNAKE_CASE					:  List[str]             =      datasets.logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Any             =      '''\
@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",
}
'''

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      '''\
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.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
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 	UpperCAmelCase__		(	datasets.Metric	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							List[str]:
       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 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Dict )		->							Tuple:
       if self.config_name == "default":
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
       else:
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model(self.config_name ) )







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


