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
a  :      Optional[int]            =    pytest.mark.integration


a  :      Tuple            =    {"""comet"""}
a  :      Optional[int]            =    importlib.util.find_spec("""fairseq""") is not None

a  :      int            =    {"""code_eval"""}
a  :      Tuple            =    os.name == """nt"""

a  :      str            =    {"""bertscore""", """frugalscore""", """perplexity"""}
a  :      Any            =    importlib.util.find_spec("""transformers""") is not None
def 		snake_case__	(		lowercase					):
   @wraps(lowercase					)
   def wrapper(self							,				lowercase					):
      if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
         self.skipTest("\"test requires Fairseq\""					)
      else:
         test_case(self							,				lowercase					)

   return wrapper
def 		snake_case__	(		lowercase					):
   @wraps(lowercase					)
   def wrapper(self							,				lowercase					):
      if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
         self.skipTest("\"test requires transformers\""					)
      else:
         test_case(self							,				lowercase					)

   return wrapper
def 		snake_case__	(		lowercase					):
   @wraps(lowercase					)
   def wrapper(self							,				lowercase					):
      if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
         self.skipTest("\"test not supported on Windows\""					)
      else:
         test_case(self							,				lowercase					)

   return wrapper
def 		snake_case__	(		):
   lowerCAmelCase_:							Union[str, Any]       =	[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(
    UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__			)
@local
class 					_lowercase    (     parameterized.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Tuple          =    {}
    SCREAMING_SNAKE_CASE:				Tuple          =    None




    @pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
    @pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							List[Any]       =	"[...]"
       lowerCAmelCase_:							List[str]       =	importlib.import_module(
           datasets.load.metric_module_factory(os.path.join("metrics"       ,						lowerCamelCase__ ) ).module_path )
       lowerCAmelCase_:							int       =	datasets.load.import_main_class(metric_module.__name__       ,						dataset=lowerCamelCase__ )
       # check parameters
       lowerCAmelCase_:							str       =	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:
                lowerCAmelCase_:							Union[str, Any]       =	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      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[int]       =	"[...]"
       lowerCAmelCase_:							int       =	importlib.import_module(
           datasets.load.metric_module_factory(os.path.join("metrics"       ,						lowerCamelCase__ ) ).module_path )
       # run doctest
       with self.use_local_metrics():
          lowerCAmelCase_:							int       =	doctest.testmod(lowerCamelCase__       ,						verbose=lowerCamelCase__       ,						raise_on_error=lowerCamelCase__ )
       self.assertEqual(results.failed       ,						0 )
       self.assertGreater(results.attempted       ,						1 )




    @contextmanager
    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       if metric_name in self.INTENSIVE_CALLS_PATCHER:
          with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase__ ):
             yield
       else:
          yield




    @contextmanager
    def      _a    (    self ):
       def load_local_metric(lowerCamelCase__       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
          return load_metric(os.path.join("metrics"       ,						lowerCamelCase__ )       ,						*lowerCamelCase__       ,						**lowerCamelCase__ )

       with patch("datasets.load_metric" ) as mock_load_metric:
          lowerCAmelCase_:							Optional[int]       =	load_local_metric
          yield




    @classmethod
    def      _a    (    cls       ,						lowerCamelCase__ ):
       def wrapper(lowerCamelCase__ ):
          lowerCAmelCase_:							List[str]       =	contextmanager(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	patcher
          return patcher

       return wrapper





@LocalMetricTest.register_intensive_calls_patcher("bleurt"					)
def 		snake_case__	(		lowercase					):
   import tensorflow.compat.va as tf
   from bleurt.score import Predictor

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

   class 					_lowercase    (     UpperCAmelCase__			):






       '''simple docstring'''




       def      _a    (    self       ,						lowerCamelCase__ ):
          assert len(input_dict["input_ids"] ) == 2
          return np.array([1.0_3, 1.0_4] )

    # 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:
      lowerCAmelCase_:							Dict       =	MockedPredictor()
      yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore"					)
def 		snake_case__	(		lowercase					):
   import torch

   def bert_cos_score_idf(lowercase							,				lowercase							,				*lowercase							,				**lowercase					):
      return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase					)					)

   # 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:
      lowerCAmelCase_:							Any       =	bert_cos_score_idf
      yield
@LocalMetricTest.register_intensive_calls_patcher("comet"					)
def 		snake_case__	(		lowercase					):
   def load_from_checkpoint(lowercase					):

      class 					_lowercase    :






          '''simple docstring'''




          def      _a    (    self       ,						lowerCamelCase__       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
             assert len(lowerCamelCase__ ) == 2
             lowerCAmelCase_:							Any       =	[0.1_9, 0.9_2]
             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:
      lowerCAmelCase_:							str       =	None
      with patch("comet.load_from_checkpoint"					) as mock_load_from_checkpoint:
         lowerCAmelCase_:							str       =	load_from_checkpoint
         yield







def 		snake_case__	(		):
   lowerCAmelCase_:							Optional[int]       =	load_metric(os.path.join("metrics"							,				"seqeval"					)					)
   lowerCAmelCase_:							Optional[Any]       =	"ERROR"
   lowerCAmelCase_:							Tuple       =	F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
   with pytest.raises(lowercase							,				match=re.escape(lowercase					)					):
      metric.compute(predictions=[]							,				references=[]							,				scheme=lowercase					)
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


a  :      List[Any]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

a  :      str            =    {
    """artists_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
    },
    """genres_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
    },
    """lyrics_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
    },
}

a  :      Any            =    {
    """jukebox""": 5_1_2,
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[int]          =    VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:				List[Any]          =    PRETRAINED_LYRIC_TOKENS_SIZES
    SCREAMING_SNAKE_CASE:				Dict          =    ['input_ids', 'attention_mask']




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=["v3", "v2", "v2"]       ,						lowerCamelCase__=512       ,						lowerCamelCase__=5       ,						lowerCamelCase__="<|endoftext|>"       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							List[str]       =	AddedToken(lowerCamelCase__       ,						lstrip=lowerCamelCase__       ,						rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__       ,						lowerCamelCase__ ) else unk_token
       super().__init__(
           unk_token=lowerCamelCase__       ,						n_genres=lowerCamelCase__       ,						version=lowerCamelCase__       ,						max_n_lyric_tokens=lowerCamelCase__       ,						**lowerCamelCase__       ,						)
       lowerCAmelCase_:							Any       =	version
       lowerCAmelCase_:							str       =	max_n_lyric_tokens
       lowerCAmelCase_:							Optional[Any]       =	n_genres

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							List[Any]       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	R"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
       # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
       if len(self.lyrics_encoder ) == 79:
          lowerCAmelCase_:							List[Any]       =	oov.replace(R"\-'"       ,						R"\-+'" )

       lowerCAmelCase_:							List[Any]       =	regex.compile(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	{v: k for k, v in self.artists_encoder.items()}
       lowerCAmelCase_:							List[str]       =	{v: k for k, v in self.genres_encoder.items()}
       lowerCAmelCase_:							int       =	{v: k for k, v in self.lyrics_encoder.items()}




    @property
    def      _a    (    self ):
       return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )




    def      _a    (    self ):
       return dict(self.artists_encoder       ,						self.genres_encoder       ,						self.lyrics_encoder )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	[self.artists_encoder.get(lowerCamelCase__       ,						0 ) for artist in list_artists]
       for genres in range(len(lowerCamelCase__ ) ):
          lowerCAmelCase_:							Tuple       =	[self.genres_encoder.get(lowerCamelCase__       ,						0 ) for genre in list_genres[genres]]
          lowerCAmelCase_:							Any       =	list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))

       lowerCAmelCase_:							Optional[Any]       =	[[self.lyrics_encoder.get(lowerCamelCase__       ,						0 ) for character in list_lyrics[0]], [], []]
       return artists_id, list_genres, lyric_ids




    def      _a    (    self       ,						lowerCamelCase__ ):
       return list(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.prepare_for_tokenization(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	self._tokenize(lowerCamelCase__ )
       return artist, genre, lyrics




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = False ):
       for idx in range(len(self.version ) ):
          if self.version[idx] == "v3":
             lowerCAmelCase_:							List[Any]       =	artists[idx].lower()
             lowerCAmelCase_:							Tuple       =	[genres[idx].lower()]
          else:
             lowerCAmelCase_:							Optional[Any]       =	self._normalize(artists[idx] ) + ".v2"
             lowerCAmelCase_:							int       =	[
                 self._normalize(lowerCamelCase__ ) + ".v2" for genre in genres[idx].split("_" )
             ]  # split is for the full dictionary with combined genres

       if self.version[0] == "v2":
          lowerCAmelCase_:							List[Any]       =	regex.compile(R"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+" )
          lowerCAmelCase_:							Optional[int]       =	"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
          lowerCAmelCase_:							Union[str, Any]       =	{vocab[index]: index + 1 for index in range(len(lowerCamelCase__ ) )}
          lowerCAmelCase_:							List[Any]       =	0
          lowerCAmelCase_:							str       =	len(lowerCamelCase__ ) + 1
          lowerCAmelCase_:							Union[str, Any]       =	self.vocab
          lowerCAmelCase_:							List[str]       =	{v: k for k, v in self.vocab.items()}
          lowerCAmelCase_:							Optional[Any]       =	""
       else:
          lowerCAmelCase_:							Optional[int]       =	regex.compile(R"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+" )

       lowerCAmelCase_:							int       =	self._run_strip_accents(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	lyrics.replace("\\"       ,						"\n" )
       lowerCAmelCase_:							List[str]       =	self.out_of_vocab.sub(""       ,						lowerCamelCase__ ), [], []
       return artists, genres, lyrics




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	unicodedata.normalize("NFD"       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	[]
       for char in text:
          lowerCAmelCase_:							Optional[Any]       =	unicodedata.category(lowerCamelCase__ )
          if cat == "Mn":
             continue
          output.append(lowerCamelCase__ )
       return "".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	(
           [chr(lowerCamelCase__ ) for i in range(ord("a" )       ,						ord("z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("A" )       ,						ord("Z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("0" )       ,						ord("9" ) + 1 )]
           + ["."]
       )
       lowerCAmelCase_:							List[str]       =	frozenset(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	re.compile(R"_+" )
       lowerCAmelCase_:							List[Any]       =	"".join([c if c in accepted else "_" for c in text.lower()] )
       lowerCAmelCase_:							Union[str, Any]       =	pattern.sub("_"       ,						lowerCamelCase__ ).strip("_" )
       return text




    def      _a    (    self       ,						lowerCamelCase__ ):
       return " ".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = False ):
       # Convert to TensorType
       if not isinstance(lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							str       =	TensorType(lowerCamelCase__ )

       # Get a function reference for the correct framework
       if tensor_type == TensorType.TENSORFLOW:
          if not is_tf_available():
             raise ImportError(
                 "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed." )
          import tensorflow as tf

          lowerCAmelCase_:							Optional[int]       =	tf.constant
          lowerCAmelCase_:							str       =	tf.is_tensor
       elif tensor_type == TensorType.PYTORCH:
          if not is_torch_available():
             raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed." )
          import torch

          lowerCAmelCase_:							Optional[Any]       =	torch.tensor
          lowerCAmelCase_:							Optional[Any]       =	torch.is_tensor
       elif tensor_type == TensorType.JAX:
          if not is_flax_available():
             raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed." )
          import jax.numpy as jnp  # noqa: F811

          lowerCAmelCase_:							Optional[Any]       =	jnp.array
          lowerCAmelCase_:							List[str]       =	_is_jax
       else:
          lowerCAmelCase_:							Optional[int]       =	np.asarray
          lowerCAmelCase_:							int       =	_is_numpy

       # Do the tensor conversion in batch

       try:
          if prepend_batch_axis:
             lowerCAmelCase_:							Any       =	[inputs]

          if not is_tensor(lowerCamelCase__ ):
             lowerCAmelCase_:							Optional[int]       =	as_tensor(lowerCamelCase__ )
       except:  # noqa E722
          raise ValueError(
              "Unable to create tensor, you should probably activate truncation and/or padding "
              "with 'padding=True' 'truncation=True' to have batched tensors with the same length." )

       return inputs




    def __call__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=""       ,						lowerCamelCase__="pt" ):
       lowerCAmelCase_:							Any       =	[0, 0, 0]
       lowerCAmelCase_:							Dict       =	[artist] * len(self.version )
       lowerCAmelCase_:							int       =	[genres] * len(self.version )

       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.tokenize(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self._convert_token_to_id(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							List[Any]       =	[-INFINITY] * len(full_tokens[-1] )
       lowerCAmelCase_:							Optional[Any]       =	[
           self.convert_to_tensors(
               [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]       ,						tensor_type=lowerCamelCase__ )
           for i in range(len(self.version ) )
       ]
       return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks} )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       if not os.path.isdir(lowerCamelCase__ ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
          return

       lowerCAmelCase_:							Dict       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"] )
       with open(lowerCamelCase__       ,						"w"       ,						encoding="utf-8" ) as f:
          f.write(json.dumps(self.artists_encoder       ,						ensure_ascii=lowerCamelCase__ ) )

       lowerCAmelCase_:							Optional[Any]       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"] )
       with open(lowerCamelCase__       ,						"w"       ,						encoding="utf-8" ) as f:
          f.write(json.dumps(self.genres_encoder       ,						ensure_ascii=lowerCamelCase__ ) )

       lowerCAmelCase_:							Dict       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"] )
       with open(lowerCamelCase__       ,						"w"       ,						encoding="utf-8" ) as f:
          f.write(json.dumps(self.lyrics_encoder       ,						ensure_ascii=lowerCamelCase__ ) )

       return (artists_file, genres_file, lyrics_file)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	self.artists_decoder.get(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	[self.genres_decoder.get(lowerCamelCase__ ) for genre in genres_index]
       lowerCAmelCase_:							Optional[int]       =	[self.lyrics_decoder.get(lowerCamelCase__ ) for character in lyric_index]
       return artist, genres, lyrics