

'''simple docstring'''

import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate





class 							_snake_case	(    _a  ):
   _A    :       Dict      				=   CONFIG_NAME
   @register_to_config
   def __init__(	self     : str       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : int = 8       ,SCREAMING_SNAKE_CASE__     : str = "lanczos"       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : bool = False       ,):
     super().__init__()
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : np.ndarray							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Optional[int]   =   images[None, ...]
     SCREAMING_SNAKE_CASE:Optional[Any]   =   (images * 255).round().astype("uint8"							)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       SCREAMING_SNAKE_CASE:Dict   =   [Image.fromarray(image.squeeze()       ,mode="L"							) for image in images]
     else:
       SCREAMING_SNAKE_CASE:Any   =   [Image.fromarray(SCREAMING_SNAKE_CASE__							) for image in images]

     return pil_images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : Union[List[PIL.Image.Image], PIL.Image.Image]							):
     if not isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any   =   [images]
     SCREAMING_SNAKE_CASE:str   =   [np.array(SCREAMING_SNAKE_CASE__							).astype(np.floataa							) / 255.0 for image in images]
     SCREAMING_SNAKE_CASE:List[Any]   =   np.stack(SCREAMING_SNAKE_CASE__       ,axis=0							)

     return images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : np.ndarray							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Optional[int]   =   images[..., None]

     SCREAMING_SNAKE_CASE:Any   =   torch.from_numpy(images.transpose(0       ,3       ,1       ,2							)							)
     return images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : torch.FloatTensor							):
     SCREAMING_SNAKE_CASE:int   =   images.cpu().permute(0       ,2       ,3       ,1							).float().numpy()
     return images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : List[str]							):
     return 2.0 * images - 1.0
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : Dict							):
     return (images / 2 + 0.5).clamp(0       ,1							)
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : PIL.Image.Image							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   image.convert("RGB"							)
     return image
   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : PIL.Image.Image       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,):
     if height is None:
       SCREAMING_SNAKE_CASE:Any   =   image.height
     if width is None:
       SCREAMING_SNAKE_CASE:Optional[Any]   =   image.width

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   (
         x - x % self.config.vae_scale_factor for x in (width, height)
     )  # resize to integer multiple of vae_scale_factor
     SCREAMING_SNAKE_CASE:str   =   image.resize((width, height)       ,resample=PIL_INTERPOLATION[self.config.resample]							)
     return image
   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   (PIL.Image.Image, np.ndarray, torch.Tensor)
     if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any   =   [image]
     elif not (isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) and all(isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for i in image							)):
       raise ValueError(
           F'''Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE__							) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE__							)}'''							)

     if isinstance(image[0]       ,PIL.Image.Image							):
       if self.config.do_convert_rgb:
         SCREAMING_SNAKE_CASE:str   =   [self.convert_to_rgb(SCREAMING_SNAKE_CASE__							) for i in image]
       if self.config.do_resize:
         SCREAMING_SNAKE_CASE:Union[str, Any]   =   [self.resize(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for i in image]
       SCREAMING_SNAKE_CASE:List[Any]   =   self.pil_to_numpy(SCREAMING_SNAKE_CASE__							)  # to np
       SCREAMING_SNAKE_CASE:Optional[Any]   =   self.numpy_to_pt(SCREAMING_SNAKE_CASE__							)  # to pt

     elif isinstance(image[0]       ,np.ndarray							):
       SCREAMING_SNAKE_CASE:Any   =   np.concatenate(SCREAMING_SNAKE_CASE__       ,axis=0							) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE__       ,axis=0							)
       SCREAMING_SNAKE_CASE:Optional[Any]   =   self.numpy_to_pt(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Optional[Any]   =   image.shape
       if self.config.do_resize and (
           height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
       ):
         raise ValueError(
             F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
             F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''							)

     elif isinstance(image[0]       ,torch.Tensor							):
       SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.cat(SCREAMING_SNAKE_CASE__       ,axis=0							) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__       ,axis=0							)
       SCREAMING_SNAKE_CASE:str   =   image.shape

       # don't need any preprocess if the image is latents
       if channel == 4:
         return image

       if self.config.do_resize and (
           height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
       ):
         raise ValueError(
             F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
             F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''							)

        # expected range [0,1], normalize to [-1,1]
     SCREAMING_SNAKE_CASE:int   =   self.config.do_normalize
     if image.min() < 0:
       warnings.warn(
           "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
           F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''       ,SCREAMING_SNAKE_CASE__       ,)
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   False

     if do_normalize:
       SCREAMING_SNAKE_CASE:List[str]   =   self.normalize(SCREAMING_SNAKE_CASE__							)

     return image







   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : torch.FloatTensor       ,SCREAMING_SNAKE_CASE__     : str = "pil"       ,SCREAMING_SNAKE_CASE__     : Optional[List[bool]] = None       ,):
     if not isinstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor							):
       raise ValueError(
           F'''Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE__							)}. We only support pytorch tensor'''							)
     if output_type not in ["latent", "pt", "np", "pil"]:
       SCREAMING_SNAKE_CASE:List[Any]   =   (
           F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
           "`pil`, `np`, `pt`, `latent`"
       )
       deprecate("Unsupported output_type"       ,"1.0.0"       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:List[str]   =   "np"

     if output_type == "latent":
       return image

     if do_denormalize is None:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   [self.config.do_normalize] * image.shape[0]

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   torch.stack(
         [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

     if output_type == "pt":
       return image

     SCREAMING_SNAKE_CASE:Tuple   =   self.pt_to_numpy(SCREAMING_SNAKE_CASE__							)

     if output_type == "np":
       return image

     if output_type == "pil":
       return self.numpy_to_pil(SCREAMING_SNAKE_CASE__							)





class 							_snake_case	(    _a  ):
   _A    :       Any      				=   CONFIG_NAME
   @register_to_config
   def __init__(	self     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : int = 8       ,SCREAMING_SNAKE_CASE__     : str = "lanczos"       ,SCREAMING_SNAKE_CASE__     : bool = True       ,):
     super().__init__()
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : Optional[int]							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Tuple   =   images[None, ...]
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   (images * 255).round().astype("uint8"							)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       SCREAMING_SNAKE_CASE:str   =   [Image.fromarray(image.squeeze()       ,mode="L"							) for image in images]
     else:
       SCREAMING_SNAKE_CASE:Tuple   =   [Image.fromarray(image[:, :, :3]							) for image in images]

     return pil_images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : int							):
     return image[:, :, 1] * 2**8 + image[:, :, 2]
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : Optional[Any]							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   images[None, ...]
     SCREAMING_SNAKE_CASE:Optional[Any]   =   images[:, :, :, 3:]
     if images.shape[-1] == 6:
       SCREAMING_SNAKE_CASE:Optional[int]   =   (images_depth * 255).round().astype("uint8"							)
       SCREAMING_SNAKE_CASE:Tuple   =   [
           Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE__							)       ,mode="I;16"							) for image_depth in images_depth
       ]
     elif images.shape[-1] == 4:
       SCREAMING_SNAKE_CASE:List[str]   =   (images_depth * 65_535.0).astype(np.uintaa							)
       SCREAMING_SNAKE_CASE:Dict   =   [Image.fromarray(SCREAMING_SNAKE_CASE__       ,mode="I;16"							) for image_depth in images_depth]
     else:
       raise Exception("Not supported"							)

     return pil_images







   def       __UpperCamelCase  (	self     : Any       ,SCREAMING_SNAKE_CASE__     : torch.FloatTensor       ,SCREAMING_SNAKE_CASE__     : str = "pil"       ,SCREAMING_SNAKE_CASE__     : Optional[List[bool]] = None       ,):
     if not isinstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor							):
       raise ValueError(
           F'''Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE__							)}. We only support pytorch tensor'''							)
     if output_type not in ["latent", "pt", "np", "pil"]:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   (
           F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
           "`pil`, `np`, `pt`, `latent`"
       )
       deprecate("Unsupported output_type"       ,"1.0.0"       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:List[str]   =   "np"

     if do_denormalize is None:
       SCREAMING_SNAKE_CASE:List[Any]   =   [self.config.do_normalize] * image.shape[0]

     SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.stack(
         [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   self.pt_to_numpy(SCREAMING_SNAKE_CASE__							)

     if output_type == "np":
       if image.shape[-1] == 6:
         SCREAMING_SNAKE_CASE:int   =   np.stack([self.rgblike_to_depthmap(im[:, :, 3:]							) for im in image]       ,axis=0							)
       else:
         SCREAMING_SNAKE_CASE:Tuple   =   image[:, :, :, 3:]
       return image[:, :, :, :3], image_depth

     if output_type == "pil":
       return self.numpy_to_pil(SCREAMING_SNAKE_CASE__							), self.numpy_to_depth(SCREAMING_SNAKE_CASE__							)
     else:
       raise Exception(F'''This type {output_type} is not supported'''							)









'''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_      =							5_03_58
A_      =							5_03_62





@require_torch
@require_torchaudio
@require_sentencepiece
class 							_snake_case	(    unittest.TestCase  ):
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:int   =   "openai/whisper-small.en"
     SCREAMING_SNAKE_CASE:Optional[Any]   =   tempfile.mkdtemp()
   def       __UpperCamelCase  (	self     : Any       ,**SCREAMING_SNAKE_CASE__     : List[str]							):
     return WhisperTokenizer.from_pretrained(self.checkpoint       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : List[str]       ,**SCREAMING_SNAKE_CASE__     : Dict							):
     return WhisperFeatureExtractor.from_pretrained(self.checkpoint       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : str							):
     shutil.rmtree(self.tmpdirname							)
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:Dict   =   self.get_tokenizer()
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.get_feature_extractor()

     SCREAMING_SNAKE_CASE:Dict   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     processor.save_pretrained(self.tmpdirname							)
     SCREAMING_SNAKE_CASE:List[Any]   =   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       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:int   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)
     processor.save_pretrained(self.tmpdirname							)

     SCREAMING_SNAKE_CASE:List[str]   =   self.get_tokenizer(bos_token="(BOS)"       ,eos_token="(EOS)"							)
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE__       ,padding_value=1.0							)

     SCREAMING_SNAKE_CASE:Any   =   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       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:Any   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:List[Any]   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Optional[Any]   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Any   =   floats_list((3, 1_000)							)

     SCREAMING_SNAKE_CASE:Any   =   feature_extractor(SCREAMING_SNAKE_CASE__       ,return_tensors="np"							)
     SCREAMING_SNAKE_CASE:List[str]   =   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       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:int   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str   =   "This is a test string"

     SCREAMING_SNAKE_CASE:int   =   processor(text=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str   =   tokenizer(SCREAMING_SNAKE_CASE__							)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]							)
   def       __UpperCamelCase  (	self     : str							):
     SCREAMING_SNAKE_CASE:List[Any]   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:List[str]   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

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

     SCREAMING_SNAKE_CASE:Any   =   processor.batch_decode(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : int							):
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   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       __UpperCamelCase  (	self     : int							):
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:Any   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Any   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   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)							)

     SCREAMING_SNAKE_CASE:Any   =   [TRANSCRIBE, NOTIMESTAMPS]
     self.assertListEqual([ids[-1] for ids in forced_decoder_ids]       ,SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Dict							):
     SCREAMING_SNAKE_CASE:Tuple   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)
     SCREAMING_SNAKE_CASE:Dict   =   processor.get_prompt_ids("Mr. Quilter"							)
     SCREAMING_SNAKE_CASE:int   =   processor.tokenizer.decode(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(prompt_ids.tolist()       ,[50_360, 1_770, 13, 2_264, 346, 353]							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,"<|startofprev|> Mr. Quilter"							)
   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Dict   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)
     SCREAMING_SNAKE_CASE:List[Any]   =   processor.get_prompt_ids(""							)
     SCREAMING_SNAKE_CASE:Tuple   =   processor.tokenizer.decode(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(prompt_ids.tolist()       ,[50_360, 220]							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,"<|startofprev|> "							)







   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Tuple   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)

     def _test_prompt_error_raised_helper(SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : List[str]							):
       with pytest.raises(SCREAMING_SNAKE_CASE__							) as excinfo:
         processor.get_prompt_ids(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:List[Any]   =   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|>"							)






