





"""simple docstring"""


import shutil
import tempfile
import unittest

import pytest

from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio

from .test_feature_extraction_whisper import floats_list


if is_speech_available():
   from transformers import WhisperFeatureExtractor, WhisperProcessor


a__       :  Any       		=					5_0_3_5_8
a__       :  List[str]       		=					5_0_3_6_2



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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					"openai/whisper-small.en"
        snake_case__     				=					tempfile.mkdtemp()

       def 		_SCREAMING_SNAKE_CASE(      self ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        return WhisperTokenizer.from_pretrained(self.checkpoint ,       **__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        return WhisperFeatureExtractor.from_pretrained(self.checkpoint ,       **__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.get_tokenizer()
        snake_case__     				=					self.get_feature_extractor()

        snake_case__     				=					WhisperProcessor(tokenizer=__SCREAMING_SNAKE_CASE ,       feature_extractor=__SCREAMING_SNAKE_CASE  )

        processor.save_pretrained(self.tmpdirname  )
        snake_case__     				=					WhisperProcessor.from_pretrained(self.tmpdirname  )

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

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					WhisperProcessor(tokenizer=self.get_tokenizer() ,       feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

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

        snake_case__     				=					WhisperProcessor.from_pretrained(
            self.tmpdirname ,       bos_token="(BOS)" ,       eos_token="(EOS)" ,       do_normalize=__SCREAMING_SNAKE_CASE ,       padding_value=1.0  )

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

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.get_feature_extractor()
        snake_case__     				=					self.get_tokenizer()

        snake_case__     				=					WhisperProcessor(tokenizer=__SCREAMING_SNAKE_CASE ,       feature_extractor=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					floats_list((3, 1000)  )

        snake_case__     				=					feature_extractor(__SCREAMING_SNAKE_CASE ,       return_tensors="np"  )
        snake_case__     				=					processor(__SCREAMING_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 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.get_feature_extractor()
        snake_case__     				=					self.get_tokenizer()

        snake_case__     				=					WhisperProcessor(tokenizer=__SCREAMING_SNAKE_CASE ,       feature_extractor=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					"This is a test string"

        snake_case__     				=					processor(text=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					tokenizer(__SCREAMING_SNAKE_CASE  )

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.get_feature_extractor()
        snake_case__     				=					self.get_tokenizer()

        snake_case__     				=					WhisperProcessor(tokenizer=__SCREAMING_SNAKE_CASE ,       feature_extractor=__SCREAMING_SNAKE_CASE  )

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

        snake_case__     				=					processor.batch_decode(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  )

        self.assertListEqual(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.get_feature_extractor()
        snake_case__     				=					self.get_tokenizer()

        snake_case__     				=					WhisperProcessor(tokenizer=__SCREAMING_SNAKE_CASE ,       feature_extractor=__SCREAMING_SNAKE_CASE  )

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.get_feature_extractor()
        snake_case__     				=					self.get_tokenizer()

        snake_case__     				=					WhisperProcessor(tokenizer=__SCREAMING_SNAKE_CASE ,       feature_extractor=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					processor.get_decoder_prompt_ids(task="transcribe" ,       no_timestamps=__SCREAMING_SNAKE_CASE  )

        self.assertIsInstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )
        for ids in forced_decoder_ids:
         self.assertIsInstance(__SCREAMING_SNAKE_CASE ,       (list, tuple)  )

        snake_case__     				=					[TRANSCRIBE, NOTIMESTAMPS]
        self.assertListEqual([ids[-1] for ids in forced_decoder_ids] ,       __SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					WhisperProcessor(tokenizer=self.get_tokenizer() ,       feature_extractor=self.get_feature_extractor()  )
        snake_case__     				=					processor.get_prompt_ids("Mr. Quilter"  )
        snake_case__     				=					processor.tokenizer.decode(__SCREAMING_SNAKE_CASE  )

        self.assertListEqual(prompt_ids.tolist() ,       [5_0360, 1770, 13, 2264, 346, 353]  )
        self.assertEqual(__SCREAMING_SNAKE_CASE ,       "<|startofprev|> Mr. Quilter"  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					WhisperProcessor(tokenizer=self.get_tokenizer() ,       feature_extractor=self.get_feature_extractor()  )
        snake_case__     				=					processor.get_prompt_ids(""  )
        snake_case__     				=					processor.tokenizer.decode(__SCREAMING_SNAKE_CASE  )

        self.assertListEqual(prompt_ids.tolist() ,       [5_0360, 220]  )
        self.assertEqual(__SCREAMING_SNAKE_CASE ,       "<|startofprev|> "  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					WhisperProcessor(tokenizer=self.get_tokenizer() ,       feature_extractor=self.get_feature_extractor()  )

        def _test_prompt_error_raised_helper(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         with pytest.raises(__SCREAMING_SNAKE_CASE  ) as excinfo:
          processor.get_prompt_ids(__SCREAMING_SNAKE_CASE  )
         snake_case__     				=					F"Encountered text in the prompt corresponding to disallowed special token: {special_token}."
         self.assertEqual(__SCREAMING_SNAKE_CASE ,       str(excinfo.value  )  )

        _test_prompt_error_raised_helper("<|startofprev|> test" ,       "<|startofprev|>"  )
        _test_prompt_error_raised_helper("test <|notimestamps|>" ,       "<|notimestamps|>"  )
        _test_prompt_error_raised_helper("test <|zh|> test <|transcribe|>" ,       "<|zh|>"  )








"""simple docstring"""


import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


a__       :  Any       		=					logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
a__       :  List[Any]       		=					direct_transformers_import(Path(__file__).parent)


a__       :  Optional[Any]       		=					{
    """AutoTokenizer""": """PreTrainedTokenizerBase""",
    """AutoFeatureExtractor""": """FeatureExtractionMixin""",
    """AutoImageProcessor""": """ImageProcessingMixin""",
}



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				Tuple									=  ["""feature_extractor""", """tokenizer"""]
       # Names need to be attr_class for attr in attributes
       snake_case__					:				Tuple									=  None
       snake_case__					:				Dict									=  None
       snake_case__					:				int									=  None

       def __init__(      self ,       *__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        for key in kwargs:
         if key not in self.attributes:
          raise TypeError(F"Unexpected keyword argument {key}."  )
        for arg, attribute_name in zip(__SCREAMING_SNAKE_CASE ,       self.attributes  ):
         if attribute_name in kwargs:
          raise TypeError(F"Got multiple values for argument {attribute_name}."  )
         else:
          snake_case__     				=					arg

        if len(__SCREAMING_SNAKE_CASE  ) != len(self.attributes  ):
         raise ValueError(
             F"This processor requires {len(self.attributes  )} arguments: {', '.join(self.attributes  )}. Got "
             F"{len(__SCREAMING_SNAKE_CASE  )} arguments instead."  )

        # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
        for attribute_name, arg in kwargs.items():
         snake_case__     				=					getattr(self ,       F"{attribute_name}_class"  )
         # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
         snake_case__     				=					AUTO_TO_BASE_CLASS_MAPPING.get(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )
         if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
          snake_case__     				=					tuple(getattr(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) for n in class_name if n is not None  )
         else:
          snake_case__     				=					getattr(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

         if not isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
          raise ValueError(
              F"Received a {type(__SCREAMING_SNAKE_CASE  ).__name__} for argument {attribute_name}, but a {class_name} was expected."  )

         setattr(self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

       def __repr__(      self  ):






        '''simple docstring'''

        snake_case__     				=					[F"- {name}: {repr(getattr(self ,       __SCREAMING_SNAKE_CASE  )  )}" for name in self.attributes]
        snake_case__     				=					"\n".join(__SCREAMING_SNAKE_CASE  )
        return F"{self.__class__.__name__}:\n{attributes_repr}"

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        os.makedirs(__SCREAMING_SNAKE_CASE ,       exist_ok=__SCREAMING_SNAKE_CASE  )

        if push_to_hub:
         snake_case__     				=					kwargs.pop("commit_message" ,       __SCREAMING_SNAKE_CASE  )
         snake_case__     				=					kwargs.pop("repo_id" ,       save_directory.split(os.path.sep  )[-1]  )
         snake_case__     				=					self._create_repo(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )
         snake_case__     				=					self._get_files_timestamps(__SCREAMING_SNAKE_CASE  )
        # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
        # loaded from the Hub.
        if self._auto_class is not None:
         snake_case__     				=					[getattr(self ,       __SCREAMING_SNAKE_CASE  ) for attribute_name in self.attributes]
         snake_case__     				=					[(a.init_kwargs if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) else a) for a in attrs]
         custom_object_save(self ,       __SCREAMING_SNAKE_CASE ,       config=__SCREAMING_SNAKE_CASE  )

        for attribute_name in self.attributes:
         snake_case__     				=					getattr(self ,       __SCREAMING_SNAKE_CASE  )
         # Include the processor class in the attribute config so this processor can then be reloaded with the
         # `AutoProcessor` API.
         if hasattr(__SCREAMING_SNAKE_CASE ,       "_set_processor_class"  ):
          attribute._set_processor_class(self.__class__.__name__  )
         attribute.save_pretrained(__SCREAMING_SNAKE_CASE  )

        if self._auto_class is not None:
         # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
         for attribute_name in self.attributes:
          snake_case__     				=					getattr(self ,       __SCREAMING_SNAKE_CASE  )
          if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
           del attribute.init_kwargs["auto_map"]

        if push_to_hub:
         self._upload_modified_files(
             __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       commit_message=__SCREAMING_SNAKE_CASE ,       token=kwargs.get("use_auth_token"  ) ,       )

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = False ,       __SCREAMING_SNAKE_CASE = False ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "main" ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					cache_dir
        snake_case__     				=					force_download
        snake_case__     				=					local_files_only
        snake_case__     				=					revision

        snake_case__     				=					kwargs.pop("use_auth_token" ,       __SCREAMING_SNAKE_CASE  )
        if use_auth_token is not None:
         warnings.warn(
             "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers." ,       __SCREAMING_SNAKE_CASE  )
         if token is not None:
          raise ValueError(
              "`token` and `use_auth_token` are both specified. Please set only the argument `token`."  )
         snake_case__     				=					use_auth_token

        if token is not None:
         # change to `token` in a follow-up PR
         snake_case__     				=					token

        snake_case__     				=					cls._get_arguments_from_pretrained(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )
        return cls(*__SCREAMING_SNAKE_CASE  )

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE="AutoProcessor"  ):






        '''simple docstring'''

        if not isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         snake_case__     				=					auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         raise ValueError(F"{auto_class} is not a valid auto class."  )

        snake_case__     				=					auto_class

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					[]
        for attribute_name in cls.attributes:
         snake_case__     				=					getattr(cls ,       F"{attribute_name}_class"  )
         if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
          snake_case__     				=					tuple(getattr(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) if n is not None else None for n in class_name  )
          snake_case__     				=					kwargs.get("use_fast" ,       __SCREAMING_SNAKE_CASE  )
          if use_fast and classes[1] is not None:
           snake_case__     				=					classes[1]
          else:
           snake_case__     				=					classes[0]
         else:
          snake_case__     				=					getattr(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

         args.append(attribute_class.from_pretrained(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )  )
        return args

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					getattr(self ,       self.attributes[0]  )
        return getattr(__SCREAMING_SNAKE_CASE ,       "model_input_names" ,       __SCREAMING_SNAKE_CASE  )
a__       :  Dict       		=					copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
   a__       :  Any       		=					ProcessorMixin.push_to_hub.__doc__.format(
       object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
   )

