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


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







@require_torch
@require_torchaudio
@require_sentencepiece
class       __snake_case (  unittest.TestCase ):
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=tempfile.mkdtemp()

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

       UpperCAmelCase     :			List[Any]     	=SpeechaTextTokenizer.from_pretrained(self.tmpdirname    )
       tokenizer.save_pretrained(self.tmpdirname    )

       UpperCAmelCase     :			Dict     	={
           '''feature_size''': 24,
           '''num_mel_bins''': 24,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_6000,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(snake_case__		, save_dir / FEATURE_EXTRACTOR_NAME    )
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname		, **snake_case__    )
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname		, **snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       shutil.rmtree(self.tmpdirname    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.get_tokenizer()
       UpperCAmelCase     :			int     	=self.get_feature_extractor()

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

       processor.save_pretrained(self.tmpdirname    )
       UpperCAmelCase     :			List[Any]     	=SpeechaTextProcessor.from_pretrained(self.tmpdirname    )

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

       self.assertEqual(processor.feature_extractor.to_json_string()		, feature_extractor.to_json_string()    )
       self.assertIsInstance(processor.feature_extractor		, snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





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

       UpperCAmelCase     :			Dict     	=self.get_tokenizer(bos_token='''(BOS)'''		, eos_token='''(EOS)'''    )
       UpperCAmelCase     :			Union[str, Any]     	=self.get_feature_extractor(do_normalize=snake_case__		, padding_value=1.0    )

       UpperCAmelCase     :			Optional[Any]     	=SpeechaTextProcessor.from_pretrained(
           self.tmpdirname		, bos_token='''(BOS)'''		, eos_token='''(EOS)'''		, do_normalize=snake_case__		, padding_value=1.0    )

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

       self.assertEqual(processor.feature_extractor.to_json_string()		, feature_extractor_add_kwargs.to_json_string()    )
       self.assertIsInstance(processor.feature_extractor		, snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.get_feature_extractor()
       UpperCAmelCase     :			List[str]     	=self.get_tokenizer()

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

       UpperCAmelCase     :			List[str]     	=floats_list((3, 1000)    )

       UpperCAmelCase     :			Tuple     	=feature_extractor(snake_case__		, return_tensors='''np'''    )
       UpperCAmelCase     :			str     	=processor(snake_case__		, 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 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.get_feature_extractor()
       UpperCAmelCase     :			Optional[Any]     	=self.get_tokenizer()

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

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

       UpperCAmelCase     :			int     	=processor(text=snake_case__    )

       UpperCAmelCase     :			int     	=tokenizer(snake_case__    )

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]		, encoded_processor[key]    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=self.get_feature_extractor()
       UpperCAmelCase     :			List[Any]     	=self.get_tokenizer()

       UpperCAmelCase     :			int     	=SpeechaTextProcessor(tokenizer=snake_case__		, feature_extractor=snake_case__    )

       UpperCAmelCase     :			Any     	=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCAmelCase     :			Union[str, Any]     	=processor.batch_decode(snake_case__    )
       UpperCAmelCase     :			List[str]     	=tokenizer.batch_decode(snake_case__    )

       self.assertListEqual(snake_case__		, snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.get_feature_extractor()
       UpperCAmelCase     :			Dict     	=self.get_tokenizer()

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

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

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
