





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


__UpperCAmelCase						=      get_tests_dir('fixtures/test_sentencepiece.model')

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



       def  A							(     self	:   Dict			):
           lowerCAmelCase_					:	Any						=   tempfile.mkdtemp()

           lowerCAmelCase_					:	Union[str, Any]						=   ["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
           lowerCAmelCase_					:	List[Any]						=   dict(zip(UpperCAmelCase					,			range(len(UpperCAmelCase			)			)			)			)
           lowerCAmelCase_					:	int						=   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"""]			)

           lowerCAmelCase_					:	Union[str, Any]						=   SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
           tokenizer.save_pretrained(self.tmpdirname			)

           lowerCAmelCase_					:	Optional[int]						=   {
               """feature_size""": 24,
               """num_mel_bins""": 24,
               """padding_value""": 0.0,
               """sampling_rate""": 1_60_00,
               """return_attention_mask""": False,
               """do_normalize""": True,
           }
           save_json(UpperCAmelCase					,			save_dir / FEATURE_EXTRACTOR_NAME			)



       def  A							(     self	:   str					,			**UpperCAmelCase	:   List[str]			):
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   str					,			**UpperCAmelCase	:   Union[str, Any]			):
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           shutil.rmtree(self.tmpdirname			)



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	int						=   self.get_tokenizer()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_feature_extractor()

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

           processor.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	Optional[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  A							(     self	:   Any			):
           lowerCAmelCase_					:	Optional[int]						=   SpeechaTextProcessor(
               tokenizer=self.get_tokenizer()					,			feature_extractor=self.get_feature_extractor()			)
           processor.save_pretrained(self.tmpdirname			)

           lowerCAmelCase_					:	List[Any]						=   self.get_tokenizer(bos_token="""(BOS)"""					,			eos_token="""(EOS)"""			)
           lowerCAmelCase_					:	Optional[Any]						=   self.get_feature_extractor(do_normalize=UpperCAmelCase					,			padding_value=1.0			)

           lowerCAmelCase_					:	Dict						=   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  A							(     self	:   Any			):
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_feature_extractor()
           lowerCAmelCase_					:	Tuple						=   self.get_tokenizer()

           lowerCAmelCase_					:	Union[str, Any]						=   SpeechaTextProcessor(tokenizer=UpperCAmelCase					,			feature_extractor=UpperCAmelCase			)

           lowerCAmelCase_					:	Any						=   floats_list((3, 10_00)			)

           lowerCAmelCase_					:	List[str]						=   feature_extractor(UpperCAmelCase					,			return_tensors="""np"""			)
           lowerCAmelCase_					:	List[Any]						=   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  A							(     self	:   Any			):
           lowerCAmelCase_					:	Dict						=   self.get_feature_extractor()
           lowerCAmelCase_					:	str						=   self.get_tokenizer()

           lowerCAmelCase_					:	Optional[Any]						=   SpeechaTextProcessor(tokenizer=UpperCAmelCase					,			feature_extractor=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   """This is a test string"""

           lowerCAmelCase_					:	List[str]						=   processor(text=UpperCAmelCase			)

           lowerCAmelCase_					:	int						=   tokenizer(UpperCAmelCase			)

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



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	str						=   self.get_feature_extractor()
           lowerCAmelCase_					:	str						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   SpeechaTextProcessor(tokenizer=UpperCAmelCase					,			feature_extractor=UpperCAmelCase			)

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

           lowerCAmelCase_					:	Any						=   processor.batch_decode(UpperCAmelCase			)
           lowerCAmelCase_					:	Dict						=   tokenizer.batch_decode(UpperCAmelCase			)

           self.assertListEqual(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	str						=   self.get_feature_extractor()
           lowerCAmelCase_					:	Dict						=   self.get_tokenizer()

           lowerCAmelCase_					:	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"""					,			)









import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.tokenizer_ab
           lowerCAmelCase_					:	Any						=   """ I am a small frog."""
           lowerCAmelCase_					:	Union[str, Any]						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self.rust_tokenizer_ab
           lowerCAmelCase_					:	str						=   """ I am a small frog."""
           lowerCAmelCase_					:	Tuple						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   str			):
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]



       def  A							(     self	:   List[str]			):
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]


