

import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							str:
       SCREAMING_SNAKE_CASE__   =   tempfile.mkdtemp()
       SCREAMING_SNAKE_CASE__   =   5

       # Realm tok
       SCREAMING_SNAKE_CASE__   =   [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''test''',
           '''question''',
           '''this''',
           '''is''',
           '''the''',
           '''first''',
           '''second''',
           '''third''',
           '''fourth''',
           '''fifth''',
           '''record''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       SCREAMING_SNAKE_CASE__   =   os.path.join(self.tmpdirname     ,       '''realm_tokenizer''' )
       os.makedirs(__lowerCamelCase     ,       exist_ok=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   os.path.join(__lowerCamelCase     ,       VOCAB_FILES_NAMES['''vocab_file'''] )
       with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8''' ) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

       SCREAMING_SNAKE_CASE__   =   os.path.join(self.tmpdirname     ,       '''realm_block_records''' )
       os.makedirs(__lowerCamelCase     ,       exist_ok=__lowerCamelCase )






  def 				lowercase_		(							self						:		Dict )		->							RealmTokenizer:
       return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,       '''realm_tokenizer''' ) )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Tuple:
       shutil.rmtree(self.tmpdirname )






  def 				lowercase_		(							self						:		str )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   RealmConfig(num_block_records=self.num_block_records )
       return config






  def 				lowercase_		(							self						:		int )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   Dataset.from_dict(
           {
               '''id''': ['''0''', '''1'''],
               '''question''': ['''foo''', '''bar'''],
               '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
           } )
       return dataset






  def 				lowercase_		(							self						:		Tuple )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   np.array(
           [
               B'''This is the first record''',
               B'''This is the second record''',
               B'''This is the third record''',
               B'''This is the fourth record''',
               B'''This is the fifth record''',
               B'''This is a longer longer longer record''',
           ]     ,       dtype=__lowerCamelCase     ,       )
       return block_records






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   RealmRetriever(
           block_records=self.get_dummy_block_records()     ,       tokenizer=self.get_tokenizer()     ,       )
       return retriever






  def 				lowercase_		(							self						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.get_config()
       SCREAMING_SNAKE_CASE__   =   self.get_dummy_retriever()
       SCREAMING_SNAKE_CASE__   =   retriever.tokenizer

       SCREAMING_SNAKE_CASE__   =   np.array([0, 3]     ,       dtype='''long''' )
       SCREAMING_SNAKE_CASE__   =   tokenizer(['''Test question'''] ).input_ids
       SCREAMING_SNAKE_CASE__   =   tokenizer(
           ['''the fourth''']     ,       add_special_tokens=__lowerCamelCase     ,       return_token_type_ids=__lowerCamelCase     ,       return_attention_mask=__lowerCamelCase     ,       ).input_ids
       SCREAMING_SNAKE_CASE__   =   config.reader_seq_len

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   retriever(
           __lowerCamelCase     ,       __lowerCamelCase     ,       answer_ids=__lowerCamelCase     ,       max_length=__lowerCamelCase     ,       return_tensors='''np''' )

       self.assertEqual(len(__lowerCamelCase )     ,       2 )
       self.assertEqual(len(__lowerCamelCase )     ,       2 )
       self.assertEqual(len(__lowerCamelCase )     ,       2 )
       self.assertEqual(concat_inputs.input_ids.shape     ,       (2, 10) )
       self.assertEqual(concat_inputs.attention_mask.shape     ,       (2, 10) )
       self.assertEqual(concat_inputs.token_type_ids.shape     ,       (2, 10) )
       self.assertEqual(concat_inputs.special_tokens_mask.shape     ,       (2, 10) )
       self.assertEqual(
           tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )     ,       ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]''']     ,       )
       self.assertEqual(
           tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )     ,       ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]''']     ,       )






  def 				lowercase_		(							self						:		Optional[int] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   self.get_config()
       SCREAMING_SNAKE_CASE__   =   self.get_dummy_retriever()
       SCREAMING_SNAKE_CASE__   =   retriever.tokenizer

       SCREAMING_SNAKE_CASE__   =   np.array([0, 3, 5]     ,       dtype='''long''' )
       SCREAMING_SNAKE_CASE__   =   tokenizer(['''Test question'''] ).input_ids
       SCREAMING_SNAKE_CASE__   =   tokenizer(
           ['''the fourth''', '''longer longer''']     ,       add_special_tokens=__lowerCamelCase     ,       return_token_type_ids=__lowerCamelCase     ,       return_attention_mask=__lowerCamelCase     ,       ).input_ids
       SCREAMING_SNAKE_CASE__   =   config.reader_seq_len

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   retriever(
           __lowerCamelCase     ,       __lowerCamelCase     ,       answer_ids=__lowerCamelCase     ,       max_length=__lowerCamelCase     ,       return_tensors='''np''' )

       self.assertEqual([False, True, True]     ,       __lowerCamelCase )
       self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]     ,       __lowerCamelCase )
       self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]     ,       __lowerCamelCase )







  def 				lowercase_		(							self						:		Dict )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.get_dummy_retriever()
       retriever.save_pretrained(os.path.join(self.tmpdirname     ,       '''realm_block_records''' ) )

       # Test local path
       SCREAMING_SNAKE_CASE__   =   retriever.from_pretrained(os.path.join(self.tmpdirname     ,       '''realm_block_records''' ) )
       self.assertEqual(retriever.block_records[0]     ,       B'''This is the first record''' )

       # Test mocked remote path
       with patch('''transformers.models.realm.retrieval_realm.hf_hub_download''' ) as mock_hf_hub_download:
            SCREAMING_SNAKE_CASE__   =   os.path.join(
                os.path.join(self.tmpdirname     ,       '''realm_block_records''' )     ,       _REALM_BLOCK_RECORDS_FILENAME )
            SCREAMING_SNAKE_CASE__   =   RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )

       self.assertEqual(retriever.block_records[0]     ,       B'''This is the first record''' )





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}


