



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  snake_case__						:






     """simple docstring"""


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






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

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

            return img_augs




class  snake_case__						:






     """simple docstring"""


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






     def        lowercase_       (	self  :  List[str],			_snake_case  :  Any )       ->str:
            snake_case__							:     str            =     tuple(max(_snake_case ) for s in zip(*[img.shape for img in images] ) )
            snake_case__							:     int            =     [im.shape[-2:] for im in images]
            snake_case__							:     List[Any]            =     [
                nn.functional.pad(
                    _snake_case,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],			value=self.pad_value,			)
                for size, im in zip(_snake_case,			_snake_case )
            ]

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






     def __call__(	self  :  List[str],			_snake_case  :  Optional[int],			_snake_case  :  List[Any]=False )       ->Optional[Any]:
            with torch.no_grad():
                   if not isinstance(_snake_case,			_snake_case ):
                          snake_case__							:     Any            =     [images]
                   if single_image:
                          assert len(_snake_case ) == 1
                   for i in range(len(_snake_case ) ):
                          if isinstance(images[i],			torch.Tensor ):
                                 images.insert(_snake_case,			images.pop(_snake_case ).to(self.device ).float() )
                          elif not isinstance(images[i],			torch.Tensor ):
                                 images.insert(
                                     _snake_case,			torch.as_tensor(img_tensorize(images.pop(_snake_case ),			input_format=self.input_format ) )
                                     .to(self.device )
                                     .float(),			)
            # resize smallest edge
                   snake_case__							:     Dict            =     torch.tensor([im.shape[:2] for im in images] )
                   snake_case__							:     int            =     self.aug(_snake_case )
                   # 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
                   snake_case__							:     Union[str, Any]            =     [self.normalizer(_snake_case ) for x in images]
                   # now pad them to do the following operations
                   snake_case__     ,    snake_case__							:     Union[str, Any]            =     self.pad(_snake_case )
                   # Normalize

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


def 	lowercase_							(A			:			List[Any]		, A			:			Optional[Any]				):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes





def 	lowercase_							(A			:			Tuple		, A			:			Tuple[int, int]				):
       assert torch.isfinite(A				).all(), "Box tensor contains infinite or NaN!"
       snake_case__     ,    snake_case__							:     str            =     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				)











import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


     def        lowercase_       (	self  :  Optional[int] )       ->Optional[Any]:
            snake_case__							:     Tuple            =     '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
            snake_case__							:     Any            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )

            snake_case__							:     List[str]            =     {
                'pad_token': '<pad>',
                'unk_token': '<unk>',
                'bos_token': '<s>',
                'eos_token': '</s>',
            }
            snake_case__							:     Optional[int]            =     {
                'feature_size': 1,
                'padding_value': 0.0,
                'sampling_rate': 1_6_0_0_0,
                'return_attention_mask': False,
                'do_normalize': True,
            }

            snake_case__							:     Union[str, Any]            =     tempfile.mkdtemp()
            snake_case__							:     Optional[int]            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     int            =     os.path.join(self.tmpdirname,			_snake_case )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )

            with open(self.feature_extraction_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )






     def        lowercase_       (	self  :  Tuple,			**_snake_case  :  Dict )       ->Union[str, Any]:
            snake_case__							:     Tuple            =     self.add_kwargs_tokens_map.copy()
            kwargs.update(_snake_case )
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			**_snake_case  :  Dict )       ->Union[str, Any]:
            return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  List[Any] )       ->str:
            shutil.rmtree(self.tmpdirname )






     def        lowercase_       (	self  :  Optional[int] )       ->Any:
            snake_case__							:     Any            =     self.get_tokenizer()
            snake_case__							:     Any            =     self.get_feature_extractor()

            snake_case__							:     Tuple            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            processor.save_pretrained(self.tmpdirname )
            snake_case__							:     List[Any]            =     WavaVecaProcessor.from_pretrained(self.tmpdirname )

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

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






     def        lowercase_       (	self  :  Optional[Any] )       ->Any:
            snake_case__							:     str            =     WavaVecaProcessor(tokenizer=self.get_tokenizer(),			feature_extractor=self.get_feature_extractor() )
            processor.save_pretrained(self.tmpdirname )

            snake_case__							:     Optional[int]            =     self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)' )
            snake_case__							:     List[str]            =     self.get_feature_extractor(do_normalize=_snake_case,			padding_value=1.0 )

            snake_case__							:     List[str]            =     WavaVecaProcessor.from_pretrained(
                self.tmpdirname,			bos_token='(BOS)',			eos_token='(EOS)',			do_normalize=_snake_case,			padding_value=1.0 )

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

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






     def        lowercase_       (	self  :  str )       ->Optional[Any]:
            snake_case__							:     Optional[Any]            =     self.get_feature_extractor()
            snake_case__							:     Tuple            =     self.get_tokenizer()

            snake_case__							:     List[Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Any            =     floats_list((3, 1_0_0_0) )

            snake_case__							:     int            =     feature_extractor(_snake_case,			return_tensors='np' )
            snake_case__							:     Union[str, Any]            =     processor(_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        lowercase_       (	self  :  Optional[int] )       ->str:
            snake_case__							:     int            =     self.get_feature_extractor()
            snake_case__							:     Any            =     self.get_tokenizer()

            snake_case__							:     List[str]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     'This is a test string'

            snake_case__							:     Optional[Any]            =     processor(text=_snake_case )

            snake_case__							:     Any            =     tokenizer(_snake_case )

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






     def        lowercase_       (	self  :  Union[str, Any] )       ->List[str]:
            snake_case__							:     Union[str, Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

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

            snake_case__							:     Dict            =     processor.batch_decode(_snake_case )
            snake_case__							:     Optional[int]            =     tokenizer.batch_decode(_snake_case )

            self.assertListEqual(_snake_case,			_snake_case )






     def        lowercase_       (	self  :  List[str] )       ->Any:
            snake_case__							:     List[Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

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






