
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 						__lowercase:


       """simple docstring"""







       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Optional[int]=sys.maxsize       )					->   Tuple:
         _lowerCAmelCase						=		'bilinear'
         _lowerCAmelCase						=		max_size
         _lowerCAmelCase						=		short_edge_length






       def __call__(     self					:			int  ,      _lowerCAmelCase					:			Tuple       )					->   int:
         _lowerCAmelCase						=		[]
         for img in imgs:
           _lowerCAmelCase	,       _lowerCAmelCase						=		img.shape[:2]
           # later: provide list and randomly choose index for resize
           _lowerCAmelCase						=		np.random.randint(self.short_edge_length[0]  ,      self.short_edge_length[1] + 1       )
           if size == 0:
             return img
           _lowerCAmelCase						=		size * 1.0 / min(lowerCAmelCase_  ,      lowerCAmelCase_       )
           if h < w:
             _lowerCAmelCase	,       _lowerCAmelCase						=		size, scale * w
           else:
             _lowerCAmelCase	,       _lowerCAmelCase						=		scale * h, size
           if max(lowerCAmelCase_  ,      lowerCAmelCase_       ) > self.max_size:
             _lowerCAmelCase						=		self.max_size * 1.0 / max(lowerCAmelCase_  ,      lowerCAmelCase_       )
             _lowerCAmelCase						=		newh * scale
             _lowerCAmelCase						=		neww * scale
           _lowerCAmelCase						=		int(neww + 0.5       )
           _lowerCAmelCase						=		int(newh + 0.5       )

           if img.dtype == np.uinta:
             _lowerCAmelCase						=		Image.fromarray(lowerCAmelCase_       )
             _lowerCAmelCase						=		pil_image.resize((neww, newh)  ,      PILImageResampling.BILINEAR       )
             _lowerCAmelCase						=		np.asarray(lowerCAmelCase_       )
           else:
             _lowerCAmelCase						=		img.permute(2  ,      0  ,      1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
             _lowerCAmelCase						=		nn.functional.interpolate(
                 lowerCAmelCase_  ,      (newh, neww)  ,      mode=self.interp_method  ,      align_corners=lowerCAmelCase_       ).squeeze(0       )
           img_augs.append(lowerCAmelCase_       )

         return img_augs





class 						__lowercase:


       """simple docstring"""







       def __init__(     self					:			Tuple  ,      _lowerCAmelCase					:			Optional[Any]       )					->   int:
         _lowerCAmelCase						=		ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,      cfg.INPUT.MAX_SIZE_TEST       )
         _lowerCAmelCase						=		cfg.INPUT.FORMAT
         _lowerCAmelCase						=		cfg.SIZE_DIVISIBILITY
         _lowerCAmelCase						=		cfg.PAD_VALUE
         _lowerCAmelCase						=		cfg.INPUT.MAX_SIZE_TEST
         _lowerCAmelCase						=		cfg.MODEL.DEVICE
         _lowerCAmelCase						=		torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )  ,      1  ,      1       )
         _lowerCAmelCase						=		torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )  ,      1  ,      1       )
         _lowerCAmelCase						=		lambda _lowerCAmelCase       : (x - self.pixel_mean) / self.pixel_std



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Tuple       )					->   Any:
         _lowerCAmelCase						=		tuple(max(lowerCAmelCase_       ) for s in zip(*[img.shape for img in images]       )       )
         _lowerCAmelCase						=		[im.shape[-2:] for im in images]
         _lowerCAmelCase						=		[
             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					:			Tuple  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Optional[Any]=False       )					->   int:
         with torch.no_grad():
           if not isinstance(lowerCAmelCase_  ,      lowerCAmelCase_       ):
             _lowerCAmelCase						=		[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
           _lowerCAmelCase						=		torch.tensor([im.shape[:2] for im in images]       )
           _lowerCAmelCase						=		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
           _lowerCAmelCase						=		[self.normalizer(lowerCAmelCase_       ) for x in images]
           # now pad them to do the following operations
           _lowerCAmelCase	,       _lowerCAmelCase						=		self.pad(lowerCAmelCase_       )
           # Normalize

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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes




def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Tuple[int, int]    ):






  """simple docstring"""




  assert torch.isfinite(_a    ).all(), "Box tensor contains infinite or NaN!"
  _lowerCAmelCase	,       _lowerCAmelCase						=		box_size
  tensor[:, 0].clamp_(min=0		,				max=_a    )
  tensor[:, 1].clamp_(min=0		,				max=_a    )
  tensor[:, 2].clamp_(min=0		,				max=_a    )
  tensor[:, 3].clamp_(min=0		,				max=_a    )


'''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


A							     = get_tests_dir("fixtures/test_sentencepiece.model")



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



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			List[str]     ) ->							List[str]:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
        snake_case_    							=				dict(zip(lowerCAmelCase_						,			range(len(lowerCAmelCase_     )     )     )     )
        snake_case_    							=				Path(self.tmpdirname     )
        save_json(lowerCAmelCase_						,			save_dir / VOCAB_FILES_NAMES["vocab_file"]     )
        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
              copyfile(lowerCAmelCase_						,			save_dir / VOCAB_FILES_NAMES["spm_file"]     )

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

        snake_case_    							=				{
            "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(lowerCAmelCase_						,			save_dir / FEATURE_EXTRACTOR_NAME     )

  def 		_lowerCamelCase						(self	:			Dict						,			**lowerCAmelCase_	:			Any     ) ->							Union[str, Any]:
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			List[str]     ) ->							Any:
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int     ) ->							str:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Optional[Any]:
        snake_case_    							=				self.get_tokenizer()
        snake_case_    							=				self.get_feature_extractor()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )
        snake_case_    							=				SpeechaTextProcessor.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     ) ->							int:
        snake_case_    							=				SpeechaTextProcessor(
            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=lowerCAmelCase_						,			padding_value=1.0     )

        snake_case_    							=				SpeechaTextProcessor.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[Any]:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        snake_case_    							=				floats_list((3, 10_00)     )

        snake_case_    							=				feature_extractor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				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	:			Union[str, Any]     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        snake_case_    							=				"This is a test string"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

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

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			int     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(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"						,			)

