



"""simple docstring"""
import sys
from typing import Tuple

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

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class    a		:



  def __init__(  self	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict=sys.maxsize						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            ="""bilinear"""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =short_edge_length





  def __call__(  self	:   Tuple    ,						lowerCAmelCase	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[]
     for img in imgs:
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[str]            =img.shape[:2]
        # later: provide list and randomly choose index for resize
        SCREAMING_SNAKE_CASE_:	Dict            =np.random.randint(self.short_edge_length[0]    ,						self.short_edge_length[1] + 1						)
        if size == 0:
           return img
        SCREAMING_SNAKE_CASE_:	Tuple            =size * 1.0 / min(lowerCAmelCase    ,						lowerCAmelCase						)
        if h < w:
           SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Tuple            =size, scale * w
        else:
           SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[Any]            =scale * h, size
        if max(lowerCAmelCase    ,						lowerCAmelCase						) > self.max_size:
           SCREAMING_SNAKE_CASE_:	Tuple            =self.max_size * 1.0 / max(lowerCAmelCase    ,						lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	Union[str, Any]            =newh * scale
           SCREAMING_SNAKE_CASE_:	str            =neww * scale
        SCREAMING_SNAKE_CASE_:	Dict            =int(neww + 0.5						)
        SCREAMING_SNAKE_CASE_:	List[Any]            =int(newh + 0.5						)

        if img.dtype == np.uinta:
           SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	List[str]            =pil_image.resize((neww, newh)    ,						PILImageResampling.BILINEAR						)
           SCREAMING_SNAKE_CASE_:	Dict            =np.asarray(lowerCAmelCase						)
        else:
           SCREAMING_SNAKE_CASE_:	str            =img.permute(2    ,						0    ,						1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
           SCREAMING_SNAKE_CASE_:	Optional[Any]            =nn.functional.interpolate(
               lowerCAmelCase    ,						(newh, neww)    ,						mode=self.interp_method    ,						align_corners=lowerCAmelCase						).squeeze(0						)
        img_augs.append(lowerCAmelCase						)

     return img_augs







class    a		:



  def __init__(  self	:   Any    ,						lowerCAmelCase	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,						cfg.INPUT.MAX_SIZE_TEST						)
     SCREAMING_SNAKE_CASE_:	int            =cfg.INPUT.FORMAT
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =cfg.SIZE_DIVISIBILITY
     SCREAMING_SNAKE_CASE_:	Dict            =cfg.PAD_VALUE
     SCREAMING_SNAKE_CASE_:	List[str]            =cfg.INPUT.MAX_SIZE_TEST
     SCREAMING_SNAKE_CASE_:	Optional[int]            =cfg.MODEL.DEVICE
     SCREAMING_SNAKE_CASE_:	Any            =torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =lambda lowerCAmelCase						: (x - self.pixel_mean) / self.pixel_std



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =tuple(max(lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[im.shape[-2:] for im in images]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[
         nn.functional.pad(
             lowerCAmelCase    ,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,						value=self.pad_value    ,						)
         for size, im in zip(lowerCAmelCase    ,						lowerCAmelCase						)
     ]

     return torch.stack(lowerCAmelCase						), torch.tensor(lowerCAmelCase						)





  def __call__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Union[str, Any]=False						)       ->   Optional[Any]:


     '''simple docstring'''




     with torch.no_grad():
        if not isinstance(lowerCAmelCase    ,						lowerCAmelCase						):
           SCREAMING_SNAKE_CASE_:	Optional[int]            =[images]
        if single_image:
           assert len(lowerCAmelCase						) == 1
        for i in range(len(lowerCAmelCase						)						):
           if isinstance(images[i]    ,						torch.Tensor						):
              images.insert(lowerCAmelCase    ,						images.pop(lowerCAmelCase						).to(self.device						).float()						)
           elif not isinstance(images[i]    ,						torch.Tensor						):
              images.insert(
                  lowerCAmelCase    ,						torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase						)    ,						input_format=self.input_format						)						)
                  .to(self.device						)
                  .float()    ,						)
            # resize smallest edge
        SCREAMING_SNAKE_CASE_:	int            =torch.tensor([im.shape[:2] for im in images]						)
        SCREAMING_SNAKE_CASE_:	Dict            =self.aug(lowerCAmelCase						)
        # transpose images and convert to torch tensors
        # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
        # now normalize before pad to avoid useless arithmetic
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[self.normalizer(lowerCAmelCase						) for x in images]
        # now pad them to do the following operations
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.pad(lowerCAmelCase						)
        # Normalize

        if self.size_divisibility > 0:
           raise NotImplementedError()
        # pad
        SCREAMING_SNAKE_CASE_:	List[Any]            =torch.true_divide(lowerCAmelCase    ,						lowerCAmelCase						)
        if single_image:
           return images[0], sizes[0], scales_yx[0]
        else:
           return images, sizes, scales_yx







def        __magic_name__			(							lowercase							,     lowercase	):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes





def        __magic_name__			(							lowercase							,     lowercase	):
   assert torch.isfinite(lowercase	).all(), "Box tensor contains infinite or NaN!"
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =box_size
   tensor[:, 0].clamp_(min=0							,     max=lowercase	)
   tensor[:, 1].clamp_(min=0							,     max=lowercase	)
   tensor[:, 2].clamp_(min=0							,     max=lowercase	)
   tensor[:, 3].clamp_(min=0							,     max=lowercase	)








"""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


_UpperCAmelCase       =     5_0_3_5_8
_UpperCAmelCase       =     5_0_3_6_2
@require_torch
@require_torchaudio
@require_sentencepiece
class    a		(					unittest.TestCase    ):



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[Any]            ="""openai/whisper-small.en"""
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tempfile.mkdtemp()



  def 		lowerCamelCase__    (  self	:   Any    ,						**lowerCAmelCase	:   str						)       ->   str:


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   str    ,						**lowerCAmelCase	:   Optional[Any]						)       ->   Optional[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Optional[Any]:


     '''simple docstring'''




     shutil.rmtree(self.tmpdirname						)



  def 		lowerCamelCase__    (  self	:   int						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_tokenizer()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_feature_extractor()

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =WhisperProcessor(tokenizer=lowerCAmelCase    ,						feature_extractor=lowerCAmelCase						)

     processor.save_pretrained(self.tmpdirname						)
     SCREAMING_SNAKE_CASE_:	Dict            =WhisperProcessor.from_pretrained(self.tmpdirname						)

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

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



  def 		lowerCamelCase__    (  self	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	int            =self.get_tokenizer(bos_token="""(BOS)"""    ,						eos_token="""(EOS)"""						)
     SCREAMING_SNAKE_CASE_:	Dict            =self.get_feature_extractor(do_normalize=lowerCAmelCase    ,						padding_value=1.0						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =WhisperProcessor.from_pretrained(
         self.tmpdirname    ,						bos_token="""(BOS)"""    ,						eos_token="""(EOS)"""    ,						do_normalize=lowerCAmelCase    ,						padding_value=1.0						)

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

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



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_feature_extractor()
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:	Dict            =WhisperProcessor(tokenizer=lowerCAmelCase    ,						feature_extractor=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Dict            =floats_list((3, 1000)						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =feature_extractor(lowerCAmelCase    ,						return_tensors="""np"""						)
     SCREAMING_SNAKE_CASE_:	str            =processor(lowerCAmelCase    ,						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 		lowerCamelCase__    (  self	:   Tuple						)       ->   Any:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	List[Any]            =WhisperProcessor(tokenizer=lowerCAmelCase    ,						feature_extractor=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	List[str]            ="""This is a test string"""

     SCREAMING_SNAKE_CASE_:	Tuple            =processor(text=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =tokenizer(lowerCAmelCase						)

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



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_feature_extractor()
     SCREAMING_SNAKE_CASE_:	str            =self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =WhisperProcessor(tokenizer=lowerCAmelCase    ,						feature_extractor=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	List[str]            =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     SCREAMING_SNAKE_CASE_:	Optional[int]            =processor.batch_decode(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            =tokenizer.batch_decode(lowerCAmelCase						)

     self.assertListEqual(lowerCAmelCase    ,						lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Dict:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =WhisperProcessor(tokenizer=lowerCAmelCase    ,						feature_extractor=lowerCAmelCase						)

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



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.get_feature_extractor()
     SCREAMING_SNAKE_CASE_:	Any            =self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:	List[Any]            =WhisperProcessor(tokenizer=lowerCAmelCase    ,						feature_extractor=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Any            =processor.get_decoder_prompt_ids(task="""transcribe"""    ,						no_timestamps=lowerCAmelCase						)

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

     SCREAMING_SNAKE_CASE_:	Tuple            =[TRANSCRIBE, NOTIMESTAMPS]
     self.assertListEqual([ids[-1] for ids in forced_decoder_ids]    ,						lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =WhisperProcessor(tokenizer=self.get_tokenizer()    ,						feature_extractor=self.get_feature_extractor()						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =processor.get_prompt_ids("""Mr. Quilter"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =processor.tokenizer.decode(lowerCAmelCase						)

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



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =WhisperProcessor(tokenizer=self.get_tokenizer()    ,						feature_extractor=self.get_feature_extractor()						)
     SCREAMING_SNAKE_CASE_:	int            =processor.get_prompt_ids(""""""						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =processor.tokenizer.decode(lowerCAmelCase						)

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





  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =WhisperProcessor(tokenizer=self.get_tokenizer()    ,						feature_extractor=self.get_feature_extractor()						)

     def _test_prompt_error_raised_helper(lowerCAmelCase	:   List[str]    ,						lowerCAmelCase	:   int						):
        with pytest.raises(lowerCAmelCase						) as excinfo:
           processor.get_prompt_ids(lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	List[Any]            =f'''Encountered text in the prompt corresponding to disallowed special token: {special_token}.'''
        self.assertEqual(lowerCAmelCase    ,						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|>"""						)



