'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
 from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_A		:			Tuple	      =get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_torch
@require_torchaudio
@require_sentencepiece
class _lowerCAmelCase (  unittest.TestCase				):







      def     lowerCamelCase__			(     self    ):
        lowercase							:       List[Any]											=      tempfile.mkdtemp()

        lowercase							:       Optional[Any]											=      ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
        lowercase							:       Optional[Any]											=      dict(zip(lowercase     ,						range(len(lowercase    )    )    )    )
        lowercase							:       List[str]											=      Path(self.tmpdirname    )
        save_json(lowercase     ,						save_dir / VOCAB_FILES_NAMES['''vocab_file''']    )
        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
          copyfile(lowercase     ,						save_dir / VOCAB_FILES_NAMES['''spm_file''']    )

        lowercase							:       Optional[int]											=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname    )
        tokenizer.save_pretrained(self.tmpdirname    )

        lowercase							:       List[Any]											=      {
            'feature_size': 24,
            'num_mel_bins': 24,
            'padding_value': 0.0,
            'sampling_rate': 1_6000,
            'return_attention_mask': False,
            'do_normalize': True,
        }
        save_json(lowercase     ,						save_dir / FEATURE_EXTRACTOR_NAME    )







      def     lowerCamelCase__			(     self     ,						**lowercase    ):
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname     ,						**lowercase    )







      def     lowerCamelCase__			(     self     ,						**lowercase    ):
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname     ,						**lowercase    )







      def     lowerCamelCase__			(     self    ):
        shutil.rmtree(self.tmpdirname    )







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Dict											=      self.get_tokenizer()
        lowercase							:       Optional[Any]											=      self.get_feature_extractor()

        lowercase							:       List[Any]											=      SpeechaTextProcessor(tokenizer=lowercase     ,						feature_extractor=lowercase    )

        processor.save_pretrained(self.tmpdirname    )
        lowercase							:       Optional[int]											=      SpeechaTextProcessor.from_pretrained(self.tmpdirname    )

        self.assertEqual(processor.tokenizer.get_vocab()     ,						tokenizer.get_vocab()    )
        self.assertIsInstance(processor.tokenizer     ,						lowercase    )

        self.assertEqual(processor.feature_extractor.to_json_string()     ,						feature_extractor.to_json_string()    )
        self.assertIsInstance(processor.feature_extractor     ,						lowercase    )







      def     lowerCamelCase__			(     self    ):
        lowercase							:       List[str]											=      SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()     ,						feature_extractor=self.get_feature_extractor()    )
        processor.save_pretrained(self.tmpdirname    )

        lowercase							:       int											=      self.get_tokenizer(bos_token='''(BOS)'''     ,						eos_token='''(EOS)'''    )
        lowercase							:       List[Any]											=      self.get_feature_extractor(do_normalize=lowercase     ,						padding_value=1.0    )

        lowercase							:       List[str]											=      SpeechaTextProcessor.from_pretrained(
            self.tmpdirname     ,						bos_token='''(BOS)'''     ,						eos_token='''(EOS)'''     ,						do_normalize=lowercase     ,						padding_value=1.0    )

        self.assertEqual(processor.tokenizer.get_vocab()     ,						tokenizer_add_kwargs.get_vocab()    )
        self.assertIsInstance(processor.tokenizer     ,						lowercase    )

        self.assertEqual(processor.feature_extractor.to_json_string()     ,						feature_extractor_add_kwargs.to_json_string()    )
        self.assertIsInstance(processor.feature_extractor     ,						lowercase    )







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Tuple											=      self.get_feature_extractor()
        lowercase							:       Optional[int]											=      self.get_tokenizer()

        lowercase							:       Optional[int]											=      SpeechaTextProcessor(tokenizer=lowercase     ,						feature_extractor=lowercase    )

        lowercase							:       Any											=      floats_list((3, 1000)    )

        lowercase							:       Optional[int]											=      feature_extractor(lowercase     ,						return_tensors='''np'''    )
        lowercase							:       Union[str, Any]											=      processor(lowercase     ,						return_tensors='''np'''    )

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()     ,						input_processor[key].sum()     ,						delta=1e-2    )







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Optional[int]											=      self.get_feature_extractor()
        lowercase							:       Optional[int]											=      self.get_tokenizer()

        lowercase							:       List[str]											=      SpeechaTextProcessor(tokenizer=lowercase     ,						feature_extractor=lowercase    )

        lowercase							:       List[str]											=      'This is a test string'

        lowercase							:       List[Any]											=      processor(text=lowercase    )

        lowercase							:       Union[str, Any]											=      tokenizer(lowercase    )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]     ,						encoded_processor[key]    )







      def     lowerCamelCase__			(     self    ):
        lowercase							:       int											=      self.get_feature_extractor()
        lowercase							:       str											=      self.get_tokenizer()

        lowercase							:       int											=      SpeechaTextProcessor(tokenizer=lowercase     ,						feature_extractor=lowercase    )

        lowercase							:       int											=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase							:       int											=      processor.batch_decode(lowercase    )
        lowercase							:       Union[str, Any]											=      tokenizer.batch_decode(lowercase    )

        self.assertListEqual(lowercase     ,						lowercase    )







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Optional[Any]											=      self.get_feature_extractor()
        lowercase							:       int											=      self.get_tokenizer()

        lowercase							:       int											=      SpeechaTextProcessor(tokenizer=lowercase     ,						feature_extractor=lowercase    )

        self.assertListEqual(
            processor.model_input_names     ,						feature_extractor.model_input_names     ,						msg='''`processor` and `feature_extractor` model input names do not match'''     ,						)


'''simple docstring'''
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_A		:			Any	      =logging.get_logger(__name__)




@dataclass
class _lowerCAmelCase :
      UpperCAmelCase_  :	str
      UpperCAmelCase_  :	str
      UpperCAmelCase_  :	Optional[str]     =					None
      UpperCAmelCase_  :	Optional[str]     =					None







      def     lowerCamelCase__			(     self    ):
        return json.dumps(dataclasses.asdict(self    )     ,						indent=2    ) + "\n"





@dataclass(frozen=snake_case				)
class _lowerCAmelCase :
      UpperCAmelCase_  :	List[int]
      UpperCAmelCase_  :	Optional[List[int]]     =					None
      UpperCAmelCase_  :	Optional[List[int]]     =					None
      UpperCAmelCase_  :	Optional[Union[int, float]]     =					None







      def     lowerCamelCase__			(     self    ):
        return json.dumps(dataclasses.asdict(self    )    ) + "\n"





class _lowerCAmelCase :







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        if len(self.get_labels()    ) > 1:
          lowercase							:       Tuple											=      self.get_labels()[int(example.label    )]
        return example







      @classmethod
      def     lowerCamelCase__			(     cls     ,						lowercase     ,						lowercase=None    ):
        with open(lowercase     ,						'''r'''     ,						encoding='''utf-8-sig'''    ) as f:
          return list(csv.reader(lowercase     ,						delimiter='''\t'''     ,						quotechar=lowercase    )    )





class _lowerCAmelCase (  snake_case				):







      def __init__(     self     ,						lowercase=None     ,						lowercase=None     ,						lowercase="classification"     ,						lowercase=False    ):
        lowercase							:       Tuple											=      [] if labels is None else labels
        lowercase							:       Optional[int]											=      [] if examples is None else examples
        lowercase							:       Tuple											=      mode
        lowercase							:       Any											=      verbose







      def __len__(     self    ):
        return len(self.examples    )







      def __getitem__(     self     ,						lowercase    ):
        if isinstance(lowercase     ,						lowercase    ):
          return SingleSentenceClassificationProcessor(labels=self.labels     ,						examples=self.examples[idx]    )
        return self.examples[idx]







      @classmethod
      def     lowerCamelCase__			(     cls     ,						lowercase     ,						lowercase=""     ,						lowercase=0     ,						lowercase=1     ,						lowercase=None     ,						lowercase=False     ,						**lowercase    ):
        lowercase							:       int											=      cls(**lowercase    )
        processor.add_examples_from_csv(
            lowercase     ,						split_name=lowercase     ,						column_label=lowercase     ,						column_text=lowercase     ,						column_id=lowercase     ,						skip_first_row=lowercase     ,						overwrite_labels=lowercase     ,						overwrite_examples=lowercase     ,						)
        return processor







      @classmethod
      def     lowerCamelCase__			(     cls     ,						lowercase     ,						lowercase=None     ,						**lowercase    ):
        lowercase							:       Dict											=      cls(**lowercase    )
        processor.add_examples(lowercase     ,						labels=lowercase    )
        return processor







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase=""     ,						lowercase=0     ,						lowercase=1     ,						lowercase=None     ,						lowercase=False     ,						lowercase=False     ,						lowercase=False     ,						):
        lowercase							:       Union[str, Any]											=      self._read_tsv(lowercase    )
        if skip_first_row:
          lowercase							:       Tuple											=      lines[1:]
        lowercase							:       Dict											=      []
        lowercase							:       Tuple											=      []
        lowercase							:       List[str]											=      []
        for i, line in enumerate(lowercase    ):
          texts.append(line[column_text]    )
          labels.append(line[column_label]    )
          if column_id is not None:
            ids.append(line[column_id]    )
          else:
            lowercase							:       Dict											=      F"""{split_name}-{i}""" if split_name else str(lowercase    )
            ids.append(lowercase    )

        return self.add_examples(
            lowercase     ,						lowercase     ,						lowercase     ,						overwrite_labels=lowercase     ,						overwrite_examples=lowercase    )







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase=None     ,						lowercase=None     ,						lowercase=False     ,						lowercase=False    ):
        if labels is not None and len(lowercase    ) != len(lowercase    ):
          raise ValueError(
              F"""Text and labels have mismatched lengths {len(lowercase    )} and {len(lowercase    )}"""    )
        if ids is not None and len(lowercase    ) != len(lowercase    ):
          raise ValueError(F"""Text and ids have mismatched lengths {len(lowercase    )} and {len(lowercase    )}"""    )
        if ids is None:
          lowercase							:       Optional[Any]											=      [None] * len(lowercase    )
        if labels is None:
          lowercase							:       Dict											=      [None] * len(lowercase    )
        lowercase							:       int											=      []
        lowercase							:       Optional[Any]											=      set()
        for text_or_text_and_label, label, guid in zip(lowercase     ,						lowercase     ,						lowercase    ):
          if isinstance(lowercase     ,						(tuple, list)    ) and label is None:
            lowercase							,			lowercase							:       Optional[Any]											=      text_or_text_and_label
          else:
            lowercase							:       Tuple											=      text_or_text_and_label
          added_labels.add(lowercase    )
          examples.append(InputExample(guid=lowercase     ,						text_a=lowercase     ,						text_b=lowercase     ,						label=lowercase    )    )

        # Update examples
        if overwrite_examples:
          lowercase							:       List[str]											=      examples
        else:
          self.examples.extend(lowercase    )

        # Update labels
        if overwrite_labels:
          lowercase							:       Dict											=      list(lowercase    )
        else:
          lowercase							:       Tuple											=      list(set(self.labels    ).union(lowercase    )    )

        return self.examples







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase=None     ,						lowercase=False     ,						lowercase=0     ,						lowercase=True     ,						lowercase=None     ,						):
        if max_length is None:
          lowercase							:       Optional[Any]											=      tokenizer.max_len

        lowercase							:       int											=      {label: i for i, label in enumerate(self.labels    )}

        lowercase							:       str											=      []
        for ex_index, example in enumerate(self.examples    ):
          if ex_index % 1_0000 == 0:
            logger.info(F"""Tokenizing example {ex_index}"""    )

          lowercase							:       Optional[int]											=      tokenizer.encode(
              example.text_a     ,						add_special_tokens=lowercase     ,						max_length=min(lowercase     ,						tokenizer.max_len    )     ,						)
          all_input_ids.append(lowercase    )

        lowercase							:       int											=      max(len(lowercase    ) for input_ids in all_input_ids    )

        lowercase							:       Dict											=      []
        for ex_index, (input_ids, example) in enumerate(zip(lowercase     ,						self.examples    )    ):
          if ex_index % 1_0000 == 0:
            logger.info(F"""Writing example {ex_index}/{len(self.examples    )}"""    )
          # The mask has 1 for real tokens and 0 for padding tokens. Only real
          # tokens are attended to.
          lowercase							:       List[Any]											=      [1 if mask_padding_with_zero else 0] * len(lowercase    )

          # Zero-pad up to the sequence length.
          lowercase							:       List[Any]											=      batch_length - len(lowercase    )
          if pad_on_left:
            lowercase							:       Optional[int]											=      ([pad_token] * padding_length) + input_ids
            lowercase							:       Any											=      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
          else:
            lowercase							:       int											=      input_ids + ([pad_token] * padding_length)
            lowercase							:       List[str]											=      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

          if len(lowercase    ) != batch_length:
            raise ValueError(F"""Error with input length {len(lowercase    )} vs {batch_length}"""    )
          if len(lowercase    ) != batch_length:
            raise ValueError(F"""Error with input length {len(lowercase    )} vs {batch_length}"""    )

          if self.mode == "classification":
            lowercase							:       Dict											=      label_map[example.label]
          elif self.mode == "regression":
            lowercase							:       Optional[int]											=      float(example.label    )
          else:
            raise ValueError(self.mode    )

          if ex_index < 5 and self.verbose:
            logger.info('''*** Example ***'''    )
            logger.info(F"""guid: {example.guid}"""    )
            logger.info(F"""input_ids: {" ".join([str(lowercase    ) for x in input_ids]    )}"""    )
            logger.info(F"""attention_mask: {" ".join([str(lowercase    ) for x in attention_mask]    )}"""    )
            logger.info(F"""label: {example.label} (id = {label})"""    )

          features.append(InputFeatures(input_ids=lowercase     ,						attention_mask=lowercase     ,						label=lowercase    )    )

        if return_tensors is None:
          return features
        elif return_tensors == "tf":
          if not is_tf_available():
            raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''    )
          import tensorflow as tf

          def gen():
            for ex in features:
              yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

          lowercase							:       Any											=      tf.data.Dataset.from_generator(
              lowercase     ,						({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)     ,						({'''input_ids''': tf.TensorShape([None]    ), '''attention_mask''': tf.TensorShape([None]    )}, tf.TensorShape([]    ))     ,						)
          return dataset
        elif return_tensors == "pt":
          if not is_torch_available():
            raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''    )
          import torch
          from torch.utils.data import TensorDataset

          lowercase							:       Any											=      torch.tensor([f.input_ids for f in features]     ,						dtype=torch.long    )
          lowercase							:       List[str]											=      torch.tensor([f.attention_mask for f in features]     ,						dtype=torch.long    )
          if self.mode == "classification":
            lowercase							:       int											=      torch.tensor([f.label for f in features]     ,						dtype=torch.long    )
          elif self.mode == "regression":
            lowercase							:       Tuple											=      torch.tensor([f.label for f in features]     ,						dtype=torch.float    )

          lowercase							:       int											=      TensorDataset(lowercase     ,						lowercase     ,						lowercase    )
          return dataset
        else:
          raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''    )