from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A         =				logging.get_logger(__name__)

__A         =				{
    "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json",
    "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json",
    "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json",
    "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json",
}


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Tuple							=				"flaubert"
    _UpperCAmelCase			:int							=				{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }






    def __init__(     self					,    _UpperCAmelCase=False					,    _UpperCAmelCase=0.0					,    _UpperCAmelCase=30145					,    _UpperCAmelCase=2048					,    _UpperCAmelCase=12					,    _UpperCAmelCase=16					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=True					,    _UpperCAmelCase=False					,    _UpperCAmelCase=False					,    _UpperCAmelCase=False					,    _UpperCAmelCase=1					,    _UpperCAmelCase=True					,    _UpperCAmelCase=512					,    _UpperCAmelCase=2048**-0.5					,    _UpperCAmelCase=1e-1_2					,    _UpperCAmelCase=0.02					,    _UpperCAmelCase=0					,    _UpperCAmelCase=1					,    _UpperCAmelCase=2					,    _UpperCAmelCase=3					,    _UpperCAmelCase=5					,    _UpperCAmelCase=True					,    _UpperCAmelCase="first"					,    _UpperCAmelCase=True					,    _UpperCAmelCase=None					,    _UpperCAmelCase=True					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=5					,    _UpperCAmelCase=5					,    _UpperCAmelCase=0					,    _UpperCAmelCase=0					,    _UpperCAmelCase=2					,    _UpperCAmelCase=0					,    **_UpperCAmelCase					,    ):
       lowercase__:     Optional[int]      =   pre_norm
       lowercase__:     str      =   layerdrop
       lowercase__:     Dict      =   vocab_size
       lowercase__:     Tuple      =   emb_dim
       lowercase__:     Optional[Any]      =   n_layers
       lowercase__:     str      =   n_heads
       lowercase__:     Optional[int]      =   dropout
       lowercase__:     Tuple      =   attention_dropout
       lowercase__:     Union[str, Any]      =   gelu_activation
       lowercase__:     int      =   sinusoidal_embeddings
       lowercase__:     Any      =   causal
       lowercase__:     List[Any]      =   asm
       lowercase__:     Optional[Any]      =   n_langs
       lowercase__:     Any      =   use_lang_emb
       lowercase__:     str      =   layer_norm_eps
       lowercase__:     int      =   bos_index
       lowercase__:     str      =   eos_index
       lowercase__:     List[str]      =   pad_index
       lowercase__:     List[str]      =   unk_index
       lowercase__:     Union[str, Any]      =   mask_index
       lowercase__:     int      =   is_encoder
       lowercase__:     List[Any]      =   max_position_embeddings
       lowercase__:     int      =   embed_init_std
       lowercase__:     Tuple      =   init_std
       lowercase__:     Dict      =   summary_type
       lowercase__:     str      =   summary_use_proj
       lowercase__:     List[Any]      =   summary_activation
       lowercase__:     int      =   summary_proj_to_labels
       lowercase__:     int      =   summary_first_dropout
       lowercase__:     List[Any]      =   start_n_top
       lowercase__:     List[Any]      =   end_n_top
       lowercase__:     Union[str, Any]      =   mask_token_id
       lowercase__:     Any      =   lang_id

       if "n_words" in kwargs:
          lowercase__:     str      =   kwargs['''n_words''']

       super().__init__(pad_token_id=_UpperCAmelCase					,    bos_token_id=_UpperCAmelCase					,    **_UpperCAmelCase							)



class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    @property
    def _snake_case    (     self							):
       if self.task == "multiple-choice":
          lowercase__:     List[Any]      =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
          lowercase__:     str      =   {0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]							)

"""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         =				get_tests_dir("fixtures/test_sentencepiece.model")


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



    """simple docstring"""

    def _snake_case    (     self							):
       lowercase__:     Dict      =   tempfile.mkdtemp()

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

       lowercase__:     Tuple      =   SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)

       lowercase__:     Optional[int]      =   {
           '''feature_size''': 24,
           '''num_mel_bins''': 24,
           '''padding_value''': 0.0,
           '''sampling_rate''': 16000,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(_UpperCAmelCase					,    save_dir / FEATURE_EXTRACTOR_NAME							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_tokenizer()
       lowercase__:     Optional[int]      =   self.get_feature_extractor()

       lowercase__:     int      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       processor.save_pretrained(self.tmpdirname							)
       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()							)
       processor.save_pretrained(self.tmpdirname							)

       lowercase__:     Any      =   self.get_tokenizer(bos_token='''(BOS)'''					,    eos_token='''(EOS)'''							)
       lowercase__:     Tuple      =   self.get_feature_extractor(do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

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

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     List[Any]      =   self.get_tokenizer()

       lowercase__:     Any      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     int      =   floats_list((3, 1000)							)

       lowercase__:     Optional[Any]      =   feature_extractor(_UpperCAmelCase					,    return_tensors='''np'''							)
       lowercase__:     Dict      =   processor(_UpperCAmelCase					,    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 _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     Dict      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     Dict      =   '''This is a test string'''

       lowercase__:     int      =   processor(text=_UpperCAmelCase							)

       lowercase__:     Union[str, Any]      =   tokenizer(_UpperCAmelCase							)

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






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.get_feature_extractor()
       lowercase__:     Union[str, Any]      =   self.get_tokenizer()

       lowercase__:     Tuple      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     List[str]      =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase__:     List[Any]      =   processor.batch_decode(_UpperCAmelCase							)
       lowercase__:     Dict      =   tokenizer.batch_decode(_UpperCAmelCase							)

       self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   self.get_feature_extractor()
       lowercase__:     List[str]      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

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