


from collections.abc import Generator
from math import sin
def 				UpperCAmelCase_					(							__snake_case						)     ->			bytes:







 """simple docstring"""


 if len(__snake_case						) != 32:
  raise ValueError('''Input must be of length 32'''						)

 _lowercase							      =b''''''
 for i in [3, 2, 1, 0]:
  little_endian += string_aa[8 * i : 8 * i + 8]
 return little_endian
def 				UpperCAmelCase_					(							__snake_case						)     ->			bytes:







 """simple docstring"""


 if i < 0:
  raise ValueError('''Input must be non-negative'''						)

 _lowercase							      =format(__snake_case     ,						'''08x'''						)[-8:]
 _lowercase							      =b''''''
 for i in [3, 2, 1, 0]:
  little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''						)
 return little_endian_hex
def 				UpperCAmelCase_					(							__snake_case						)     ->			bytes:







 """simple docstring"""


 _lowercase							      =b''''''
 for char in message:
  bit_string += format(__snake_case     ,						'''08b'''						).encode('''utf-8'''						)
 _lowercase							      =format(len(__snake_case						)     ,						'''064b'''						).encode('''utf-8'''						)

 # Pad bit_string to a multiple of 512 chars
 bit_string += b"1"
 while len(__snake_case						) % 512 != 448:
  bit_string += b"0"
 bit_string += to_little_endian(start_len[32:]						) + to_little_endian(start_len[:32]						)

 return bit_string
def 				UpperCAmelCase_					(							__snake_case						)     ->			Generator[list[int], None, None]:







 """simple docstring"""


 if len(__snake_case						) % 512 != 0:
  raise ValueError('''Input must have length that\'s a multiple of 512'''						)

 for pos in range(0     ,						len(__snake_case						)     ,						512						):
  _lowercase							      =bit_string[pos : pos + 512]
  _lowercase							      =[]
  for i in range(0     ,						512     ,						32						):
   block_words.append(int(to_little_endian(block[i : i + 32]						)     ,						2						)						)
  yield block_words
def 				UpperCAmelCase_					(							__snake_case						)     ->			int:







 """simple docstring"""


 if i < 0:
  raise ValueError('''Input must be non-negative'''						)

 _lowercase							      =format(__snake_case     ,						'''032b'''						)
 _lowercase							      =''''''
 for c in i_str:
  new_str += "1" if c == "0" else "0"
 return int(__snake_case     ,						2						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 return (a + b) % 2**32
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 if i < 0:
  raise ValueError('''Input must be non-negative'''						)
 if shift < 0:
  raise ValueError('''Shift must be non-negative'''						)
 return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def 				UpperCAmelCase_					(							__snake_case						)     ->			bytes:







 """simple docstring"""


 _lowercase							      =preprocess(__snake_case						)

 _lowercase							      =[int(2**32 * abs(sin(i + 1						)						)						) for i in range(64						)]

 # Starting states
 _lowercase							      =0X6_7_4_5_2_3_0_1
 _lowercase							      =0XE_F_C_D_A_B_8_9
 _lowercase							      =0X9_8_B_A_D_C_F_E
 _lowercase							      =0X1_0_3_2_5_4_7_6

 _lowercase							      =[
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
 ]

 # Process bit string in chunks, each with 16 32-char words
 for block_words in get_block_words(__snake_case						):
  _lowercase							      =aa
  _lowercase							      =ba
  _lowercase							      =ca
  _lowercase							      =da

  # Hash current chunk
  for i in range(64						):
   if i <= 15:
    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
    _lowercase							      =d ^ (b & (c ^ d))
    _lowercase							      =i
   elif i <= 31:
    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
    _lowercase							      =c ^ (d & (b ^ c))
    _lowercase							      =(5 * i + 1) % 16
   elif i <= 47:
    _lowercase							      =b ^ c ^ d
    _lowercase							      =(3 * i + 5) % 16
   else:
    _lowercase							      =c ^ (b | not_aa(__snake_case						))
    _lowercase							      =(7 * i) % 16
   _lowercase							      =(f + a + added_consts[i] + block_words[g]) % 2**32
   _lowercase							      =d
   _lowercase							      =c
   _lowercase							      =b
   _lowercase							      =sum_aa(__snake_case     ,						left_rotate_aa(__snake_case     ,						shift_amounts[i]						)						)

  # Add hashed chunk to running total
  _lowercase							      =sum_aa(__snake_case     ,						__snake_case						)
  _lowercase							      =sum_aa(__snake_case     ,						__snake_case						)
  _lowercase							      =sum_aa(__snake_case     ,						__snake_case						)
  _lowercase							      =sum_aa(__snake_case     ,						__snake_case						)

 _lowercase							      =reformat_hex(__snake_case						) + reformat_hex(__snake_case						) + reformat_hex(__snake_case						) + reformat_hex(__snake_case						)
 return digest


if __name__ == "__main__":
  import doctest

  doctest.testmod()





import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase__   	=							datasets.logging.get_logger(__name__)

UpperCAmelCase__   	=							'''\
@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",
}
'''

UpperCAmelCase__   	=							'''\
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.
'''

UpperCAmelCase__   	=							'''
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):





     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		,     UpperCAmelCase       )     ->   Dict:
      if self.config_name == "default":
       _lowercase							      =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
      else:
       _lowercase							      =comet.load_from_checkpoint(comet.download_model(self.config_name       )       )





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

