
"""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


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





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







   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     tempfile.mkdtemp()

      a_						   =     ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
      a_						   =     dict(zip(__UpperCAmelCase       ,							range(len(__UpperCAmelCase))))
      a_						   =     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"])

      a_						   =     SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
      tokenizer.save_pretrained(self.tmpdirname)

      a_						   =     {
          "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    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->Any:
      return SpeechaTextTokenizer.from_pretrained(self.tmpdirname       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->str:
      return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Any:
      shutil.rmtree(self.tmpdirname)







   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     self.get_tokenizer()
      a_						   =     self.get_feature_extractor()

      a_						   =     SpeechaTextProcessor(tokenizer=__UpperCAmelCase       ,							feature_extractor=__UpperCAmelCase)

      processor.save_pretrained(self.tmpdirname)
      a_						   =     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    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     SpeechaTextProcessor(
          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=__UpperCAmelCase       ,							padding_value=1.0)

      a_						   =     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    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     self.get_feature_extractor()
      a_						   =     self.get_tokenizer()

      a_						   =     SpeechaTextProcessor(tokenizer=__UpperCAmelCase       ,							feature_extractor=__UpperCAmelCase)

      a_						   =     floats_list((3, 10_00))

      a_						   =     feature_extractor(__UpperCAmelCase       ,							return_tensors="np")
      a_						   =     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    UpperCAmelCase__	( self)   ->List[Any]:
      a_						   =     self.get_feature_extractor()
      a_						   =     self.get_tokenizer()

      a_						   =     SpeechaTextProcessor(tokenizer=__UpperCAmelCase       ,							feature_extractor=__UpperCAmelCase)

      a_						   =     "This is a test string"

      a_						   =     processor(text=__UpperCAmelCase)

      a_						   =     tokenizer(__UpperCAmelCase)

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







   def    UpperCAmelCase__	( self)   ->Optional[int]:
      a_						   =     self.get_feature_extractor()
      a_						   =     self.get_tokenizer()

      a_						   =     SpeechaTextProcessor(tokenizer=__UpperCAmelCase       ,							feature_extractor=__UpperCAmelCase)

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

      a_						   =     processor.batch_decode(__UpperCAmelCase)
      a_						   =     tokenizer.batch_decode(__UpperCAmelCase)

      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     self.get_feature_extractor()
      a_						   =     self.get_tokenizer()

      a_						   =     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"       ,							)

"""simple docstring"""






import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


UpperCamelCase_						   =   logging.get_logger(__name__)





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Tuple     					=     """hybrid-clip"""
   a_					:		Optional[Any]     					=     True







   def __init__( self       ,							__UpperCAmelCase=5_12       ,							**__UpperCAmelCase)   ->Dict:
      super().__init__(**__UpperCAmelCase)

      if "text_config" not in kwargs:
         raise ValueError("`text_config` can not be `None`.")

      if "vision_config" not in kwargs:
         raise ValueError("`vision_config` can not be `None`.")

      a_						   =     kwargs.pop("text_config")
      a_						   =     kwargs.pop("vision_config")

      a_						   =     text_config.pop("model_type")
      a_						   =     vision_config.pop("model_type")

      from transformers import AutoConfig

      a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      if vision_model_type == "clip":
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase).vision_config
      elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         a_						   =     CLIPVisionConfig(**__UpperCAmelCase)
      else:
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      a_						   =     projection_dim
      a_						   =     1.0







   @classmethod
   def    UpperCAmelCase__	( cls       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Optional[int]:
      return cls(text_config=text_config.to_dict()       ,							vision_config=vision_config.to_dict()       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->str:
      a_						   =     copy.deepcopy(self.__dict__)
      a_						   =     self.text_config.to_dict()
      a_						   =     self.vision_config.to_dict()
      a_						   =     self.__class__.model_type
      return output