



import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
UpperCAmelCase_		 =					pytest.mark.integration


UpperCAmelCase_		 =					{"""comet"""}
UpperCAmelCase_		 =					importlib.util.find_spec("""fairseq""") is not None

UpperCAmelCase_		 =					{"""code_eval"""}
UpperCAmelCase_		 =					os.name == """nt"""

UpperCAmelCase_		 =					{"""bertscore""", """frugalscore""", """perplexity"""}
UpperCAmelCase_		 =					importlib.util.find_spec("""transformers""") is not None

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any				)  ->     List[str]:
   @wraps(_snake_case				)
   def wrapper(self	:Optional[Any]	,   _snake_case	:Dict				):
      if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
         self.skipTest('''"test requires Fairseq"'''				)
      else:
         test_case(self	,   _snake_case				)

   return wrapper

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple				)  ->     str:
   @wraps(_snake_case				)
   def wrapper(self	:Optional[int]	,   _snake_case	:List[str]				):
      if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
         self.skipTest('''"test requires transformers"'''				)
      else:
         test_case(self	,   _snake_case				)

   return wrapper

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     Union[str, Any]:
   @wraps(_snake_case				)
   def wrapper(self	:List[Any]	,   _snake_case	:str				):
      if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
         self.skipTest('''"test not supported on Windows"'''				)
      else:
         test_case(self	,   _snake_case				)

   return wrapper

def 	SCREAMING_SNAKE_CASE_							(	)  ->     str:
   _A      =       [metric_dir.split(os.sep				)[-2] for metric_dir in glob.glob('''./metrics/*/'''				)]
   return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished


@parameterized.named_parameters(get_local_metric_names())
@for_all_test_methods(
    _A							,  _A							,  _A)
@local
class 	lowerCamelCase__	(       parameterized.TestCase):



 """simple docstring"""

 a__					: List[Any] =  {}
 a__					: Tuple =  None

 @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''      )
 @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''      )
 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Any      )   ->	List[Any]:
    _A      =       '''[...]'''
    _A      =       importlib.import_module(
        datasets.load.metric_module_factory(os.path.join('''metrics'''			,						__lowerCAmelCase      )      ).module_path      )
    _A      =       datasets.load.import_main_class(metric_module.__name__			,						dataset=__lowerCAmelCase      )
    # check parameters
    _A      =       inspect.signature(metric._compute      ).parameters
    self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()      )      )  # no **kwargs
    # run doctest
    with self.patch_intensive_calls(__lowerCAmelCase			,						metric_module.__name__      ):
       with self.use_local_metrics():
          try:
             _A      =       doctest.testmod(__lowerCAmelCase			,						verbose=__lowerCAmelCase			,						raise_on_error=__lowerCAmelCase      )
          except doctest.UnexpectedException as e:
             raise e.exc_info[1]  # raise the exception that doctest caught
    self.assertEqual(results.failed			,						0      )
    self.assertGreater(results.attempted			,						1      )

 @slow
 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					int      )   ->	Optional[Any]:
    _A      =       '''[...]'''
    _A      =       importlib.import_module(
        datasets.load.metric_module_factory(os.path.join('''metrics'''			,						__lowerCAmelCase      )      ).module_path      )
    # run doctest
    with self.use_local_metrics():
       _A      =       doctest.testmod(__lowerCAmelCase			,						verbose=__lowerCAmelCase			,						raise_on_error=__lowerCAmelCase      )
    self.assertEqual(results.failed			,						0      )
    self.assertGreater(results.attempted			,						1      )

 @contextmanager
 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Tuple      )   ->	Optional[int]:
    if metric_name in self.INTENSIVE_CALLS_PATCHER:
       with self.INTENSIVE_CALLS_PATCHER[metric_name](__lowerCAmelCase      ):
          yield
    else:
       yield

 @contextmanager
 def     snake_case_      (    self					:					Optional[int]      )   ->	List[Any]:
    def load_local_metric(__lowerCAmelCase					:					List[str]			,						*__lowerCAmelCase					:					Dict			,						**__lowerCAmelCase					:					Optional[int]      ):
       return load_metric(os.path.join('''metrics'''			,						__lowerCAmelCase      )			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )

    with patch('''datasets.load_metric'''      ) as mock_load_metric:
       _A      =       load_local_metric
       yield

 @classmethod
 def     snake_case_      (    cls					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	List[str]:
    def wrapper(__lowerCAmelCase					:					Optional[int]      ):
       _A      =       contextmanager(__lowerCAmelCase      )
       _A      =       patcher
       return patcher

    return wrapper

@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''				)
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     Any:
   import tensorflow.compat.va as tf
   from bleurt.score import Predictor

   tf.flags.DEFINE_string('''sv'''	,   ''''''	,   ''''''				)  # handle pytest cli flags


   class 	lowerCamelCase__	(       _A):



    """simple docstring"""

    def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Dict      )   ->	int:
       assert len(input_dict['''input_ids''']      ) == 2
       return np.array([1.03, 1.04]      )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
   with patch('''bleurt.score._create_predictor'''				) as mock_create_predictor:
      _A      =       MockedPredictor()
      yield

@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''				)
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     Any:
   import torch

   def bert_cos_score_idf(_snake_case	:List[Any]	,   _snake_case	:Optional[int]	,   *_snake_case	:Union[str, Any]	,   **_snake_case	:Optional[int]				):
      return torch.tensor([[1.0, 1.0, 1.0]] * len(_snake_case				)				)

   # mock get_model which is supposed to do download a bert model
   # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
   with patch('''bert_score.scorer.get_model'''				), patch(
       '''bert_score.scorer.bert_cos_score_idf'''				) as mock_bert_cos_score_idf:
      _A      =       bert_cos_score_idf
      yield

@LocalMetricTest.register_intensive_calls_patcher('''comet'''				)
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     Union[str, Any]:
   def load_from_checkpoint(_snake_case	:Dict				):


      class 	lowerCamelCase__	:



       """simple docstring"""

       def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Any			,						*__lowerCAmelCase					:					Any			,						**__lowerCAmelCase					:					str      )   ->	Tuple:
          assert len(__lowerCAmelCase      ) == 2
          _A      =       [0.19, 0.92]
          return scores, sum(__lowerCAmelCase      ) / len(__lowerCAmelCase      )
      return Model()

   # mock load_from_checkpoint which is supposed to do download a bert model
   # mock load_from_checkpoint which is supposed to do download a bert model
   with patch('''comet.download_model'''				) as mock_download_model:
      _A      =       None
      with patch('''comet.load_from_checkpoint'''				) as mock_load_from_checkpoint:
         _A      =       load_from_checkpoint
         yield


def 	SCREAMING_SNAKE_CASE_							(	)  ->     List[Any]:
   _A      =       load_metric(os.path.join('''metrics'''	,   '''seqeval'''				)				)
   _A      =       '''ERROR'''
   _A      =       F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
   with pytest.raises(_snake_case	,   match=re.escape(_snake_case				)				):
      metric.compute(predictions=[]	,   references=[]	,   scheme=_snake_case				)








import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)


UpperCAmelCase_		 =					{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

UpperCAmelCase_		 =					"""</w>"""
UpperCAmelCase_		 =					"""@@ """

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     List[str]:
   _A      =       set()
   _A      =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      _A      =       char
   return pairs


# Speech2Text2 has no max input length
UpperCAmelCase_		 =					{"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Dict =  VOCAB_FILES_NAMES
 a__					: str =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[Any] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: List[Any] =  ["input_ids", "attention_mask"]

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[str]="<s>"			,						__lowerCAmelCase					:					Tuple="<pad>"			,						__lowerCAmelCase					:					Optional[Any]="</s>"			,						__lowerCAmelCase					:					Dict="<unk>"			,						__lowerCAmelCase					:					Union[str, Any]=False			,						__lowerCAmelCase					:					Optional[int]=None			,						**__lowerCAmelCase					:					str			,						)   ->	Dict:
    super().__init__(
        unk_token=__lowerCAmelCase			,						bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						do_lower_case=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

    _A      =       do_lower_case

    with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as vocab_handle:
       _A      =       json.load(__lowerCAmelCase      )
    _A      =       {v: k for k, v in self.encoder.items()}

    if merges_file is None:
       logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''      )

       _A      =       None
       _A      =       None
    else:
       with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as merges_handle:
          _A      =       merges_handle.read().split('''\n'''      )[:-1]

       _A      =       [tuple(merge.split()[:2]      ) for merge in merges]
       _A      =       dict(zip(__lowerCAmelCase			,						range(len(__lowerCAmelCase      )      )      )      )
       _A      =       {}

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	int:
    return len(self.decoder      )

 def     snake_case_      (    self					:					Dict      )   ->	Dict:
    return dict(self.encoder			,						**self.added_tokens_encoder      )

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
    if token in self.cache:
       return self.cache[token]
    _A      =       get_pairs(__lowerCAmelCase      )

    if not pairs:
       return token

    while True:
       _A      =       min(__lowerCAmelCase			,						key=lambda __lowerCAmelCase      : self.bpe_ranks.get(__lowerCAmelCase			,						float('''inf'''      )      )      )
       if bigram not in self.bpe_ranks:
          break
       _A  ,    _A      =       bigram
       _A      =       []
       _A      =       0
       while i < len(__lowerCAmelCase      ):
          try:
             _A      =       word.index(__lowerCAmelCase			,						__lowerCAmelCase      )
          except ValueError:
             new_word.extend(word[i:]      )
             break
          else:
             new_word.extend(word[i:j]      )
             _A      =       j

          if word[i] == first and i < len(__lowerCAmelCase      ) - 1 and word[i + 1] == second:
             new_word.append(first + second      )
             i += 2
          else:
             new_word.append(word[i]      )
             i += 1
       _A      =       tuple(__lowerCAmelCase      )
       _A      =       new_word
       if len(__lowerCAmelCase      ) == 1:
          break
       else:
          _A      =       get_pairs(__lowerCAmelCase      )
    _A      =       ''' '''.join(__lowerCAmelCase      )
    if word == "\n  " + BPE_TOKEN_MERGES:
       _A      =       '''\n''' + BPE_TOKEN_MERGES

    if word.endswith(__lowerCAmelCase      ):
       _A      =       word.replace(__lowerCAmelCase			,						''''''      )

    _A      =       word.replace(''' '''			,						__lowerCAmelCase      )
    _A      =       word
    return word

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Tuple      )   ->	Optional[int]:

    if self.bpe_ranks is None:
       raise ValueError(
           '''This tokenizer was instantiated without a `merges.txt` file, so'''
           ''' that it can only be used for decoding, not for encoding.'''
           '''Make sure to provide `merges.txt` file at instantiation to enable '''
           '''encoding.'''      )

    if self.do_lower_case:
       _A      =       text.lower()

    _A      =       text.split()

    _A      =       []
    for token in text:
       if token:
          split_tokens.extend(list(self.bpe(__lowerCAmelCase      ).split(''' '''      )      )      )

    return split_tokens

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str      )   ->	int:
    return self.encoder.get(__lowerCAmelCase			,						self.encoder.get(self.unk_token      )      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					int      )   ->	str:
    _A      =       self.decoder.get(__lowerCAmelCase			,						self.unk_token      )
    return result

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					List[str]      )   ->	str:
    _A      =       ''' '''.join(__lowerCAmelCase      )

    # make sure @@ tokens are concatenated
    _A      =       ''''''.join(string.split(__lowerCAmelCase      )      )

    return string

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

    with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as f:
       f.write(json.dumps(self.encoder			,						indent=2			,						sort_keys=__lowerCAmelCase			,						ensure_ascii=__lowerCAmelCase      ) + '''\n'''      )

    _A      =       0
    if self.bpe_ranks is None:
       return (vocab_file,)

    with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as writer:
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,						key=lambda __lowerCAmelCase      : kv[1]      ):
          if index != token_index:
             logger.warning(
                 f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                 ''' Please check that the tokenizer is not corrupted!'''      )
             _A      =       token_index
          writer.write(''' '''.join(__lowerCAmelCase      ) + '''\n'''      )
          index += 1

    return (vocab_file, merges_file)



