




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
   from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

   from .test_feature_extraction_speechta import floats_list


lowercase__   :    List[str] 					=      get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")




@require_torch
class     a__ (      unittest.TestCase   ):







     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          a											=   tempfile.mkdtemp()

          a											=   SpeechTaTokenizer(A				)
          tokenizer.save_pretrained(self.tmpdirname				)

          a											=   {
              "feature_size": 1,
              "padding_value": 0.0,
              "sampling_rate": 16000,
              "do_normalize": False,
              "num_mel_bins": 80,
              "hop_length": 16,
              "win_length": 64,
              "win_function": "hann_window",
              "fmin": 80,
              "fmax": 7600,
              "mel_floor": 1e-10,
              "reduction_factor": 2,
              "return_attention_mask": True,
          }

          a											=   os.path.join(self.tmpdirname						,	A				)
          with open(self.feature_extraction_file						,	"w"						,	encoding="utf-8"				) as fp:
               fp.write(json.dumps(A				) + "\n"				)







     def     lowerCAmelCase_       (			self						,	**A				)						->					int:



          '''simple docstring'''



          return SpeechTaTokenizer.from_pretrained(self.tmpdirname						,	**A				)







     def     lowerCAmelCase_       (			self						,	**A				)						->					str:



          '''simple docstring'''



          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname						,	**A				)







     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          shutil.rmtree(self.tmpdirname				)







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          a											=   self.get_tokenizer()
          a											=   self.get_feature_extractor()

          a											=   SpeechTaProcessor(tokenizer=A						,	feature_extractor=A				)

          processor.save_pretrained(self.tmpdirname				)
          a											=   SpeechTaProcessor.from_pretrained(self.tmpdirname				)

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

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







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   SpeechTaProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()				)
          processor.save_pretrained(self.tmpdirname				)

          a											=   self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"				)
          a											=   self.get_feature_extractor(do_normalize=A						,	padding_value=1.0				)

          a											=   SpeechTaProcessor.from_pretrained(
              self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=A						,	padding_value=1.0				)

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

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







     def     lowerCAmelCase_       (			self				)						->					List[Any]:



          '''simple docstring'''



          a											=   self.get_feature_extractor()
          a											=   self.get_tokenizer()

          a											=   SpeechTaProcessor(tokenizer=A						,	feature_extractor=A				)

          a											=   floats_list((3, 1000)				)

          a											=   feature_extractor(audio=A						,	return_tensors="np"				)
          a											=   processor(audio=A						,	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				)						->					List[Any]:



          '''simple docstring'''



          a											=   self.get_feature_extractor()
          a											=   self.get_tokenizer()

          a											=   SpeechTaProcessor(tokenizer=A						,	feature_extractor=A				)

          a											=   floats_list((3, 1000)				)

          a											=   feature_extractor(audio_target=A						,	return_tensors="np"				)
          a											=   processor(audio_target=A						,	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				)						->					str:



          '''simple docstring'''



          a											=   self.get_feature_extractor()
          a											=   self.get_tokenizer()

          a											=   SpeechTaProcessor(tokenizer=A						,	feature_extractor=A				)

          a											=   "This is a test string"

          a											=   processor(text=A				)
          a											=   tokenizer(A				)

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







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   self.get_feature_extractor()
          a											=   self.get_tokenizer()

          a											=   SpeechTaProcessor(tokenizer=A						,	feature_extractor=A				)

          a											=   "This is a test string"

          a											=   processor(text_target=A				)
          a											=   tokenizer(A				)

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







     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



          a											=   self.get_feature_extractor()
          a											=   self.get_tokenizer()

          a											=   SpeechTaProcessor(tokenizer=A						,	feature_extractor=A				)

          a											=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          a											=   processor.batch_decode(A				)
          a											=   tokenizer.batch_decode(A				)

          self.assertListEqual(A						,	A				)







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          a											=   self.get_feature_extractor()
          a											=   self.get_tokenizer()

          a											=   SpeechTaProcessor(tokenizer=A						,	feature_extractor=A				)

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







from collections import OrderedDict
from typing import Mapping

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


lowercase__   :    str 					=      logging.get_logger(__name__)

lowercase__   :    List[str] 					=      {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}




class     a__ (      UpperCamelCase__   ):
     a	:    Union[str, Any]	      =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
     a	:    Optional[int]	      =			"""mobilebert"""







     def __init__(			self						,	A=30522						,	A=512						,	A=24						,	A=4						,	A=512						,	A="relu"						,	A=0.0						,	A=0.1						,	A=512						,	A=2						,	A=0.0_2						,	A=1e-12						,	A=0						,	A=128						,	A=True						,	A=True						,	A=128						,	A=False						,	A=True						,	A=4						,	A="no_norm"						,	A=True						,	A=None						,	**A						,	)						->					str:



          '''simple docstring'''



          super().__init__(pad_token_id=A						,	**A				)

          a											=   vocab_size
          a											=   hidden_size
          a											=   num_hidden_layers
          a											=   num_attention_heads
          a											=   hidden_act
          a											=   intermediate_size
          a											=   hidden_dropout_prob
          a											=   attention_probs_dropout_prob
          a											=   max_position_embeddings
          a											=   type_vocab_size
          a											=   initializer_range
          a											=   layer_norm_eps
          a											=   embedding_size
          a											=   trigram_input
          a											=   use_bottleneck
          a											=   intra_bottleneck_size
          a											=   use_bottleneck_attention
          a											=   key_query_shared_bottleneck
          a											=   num_feedforward_networks
          a											=   normalization_type
          a											=   classifier_activation

          if self.use_bottleneck:
               a											=   intra_bottleneck_size
          else:
               a											=   hidden_size

          a											=   classifier_dropout







class     a__ (      UpperCamelCase__   ):







     @property
     def     lowerCAmelCase_       (			self				)						->					Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          if self.task == "multiple-choice":
               a											=   {0: "batch", 1: "choice", 2: "sequence"}
          else:
               a											=   {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]				)

