
"""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     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=sys.maxsize     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     '''bilinear'''
													__UpperCamelCase       					=     max_size
													__UpperCamelCase       					=     short_edge_length



							def __call__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






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

																			if img.dtype == np.uinta:
																									__UpperCamelCase       					=     Image.fromarray(__snake_case     )
																									__UpperCamelCase       					=     pil_image.resize((neww, newh)       ,		PILImageResampling.BILINEAR     )
																									__UpperCamelCase       					=     np.asarray(__snake_case     )
																			else:
																									__UpperCamelCase       					=     img.permute(2       ,		0       ,		1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
																									__UpperCamelCase       					=     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     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tuple(max(__snake_case     ) for s in zip(*[img.shape for img in images]     )     )
													__UpperCamelCase       					=     [im.shape[-2:] for im in images]
													__UpperCamelCase       					=     [
													    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       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False     )->				str:





													'''simple docstring'''






													with torch.no_grad():
																			if not isinstance(__snake_case       ,		__snake_case     ):
																									__UpperCamelCase       					=     [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
																			__UpperCamelCase       					=     torch.tensor([im.shape[:2] for im in images]     )
																			__UpperCamelCase       					=     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
																			__UpperCamelCase       					=     [self.normalizer(__snake_case     ) for x in images]
																			# now pad them to do the following operations
																			__UpperCamelCase       					=     self.pad(__snake_case     )
																			# Normalize

																			if self.size_divisibility > 0:
																									raise NotImplementedError()
																			# pad
																			__UpperCamelCase       					=     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 		A_	(							snake_case							:				int	, snake_case							:				Union[str, Any]	) ->      Optional[int]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Dict	, snake_case							:				Tuple[int, int]	) ->      Tuple:



						'''simple docstring'''

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






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


lowercase__							:							List[Any]													=     get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tempfile.mkdtemp()

													__UpperCamelCase       					=     SpeechTaTokenizer(SCREAMING_SNAKE_CASE_     )
													tokenizer.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     {
													    '''feature_size''': 1,
													    '''padding_value''': 0.0,
													    '''sampling_rate''': 16000,
													    '''do_normalize''': False,
													    '''num_mel_bins''': 80,
													    '''hop_length''': 16,
													    '''win_length''': 64,
													    '''win_function''': '''hann_window''',
													    '''fmin''': 80,
													    '''fmax''': 7600,
													    '''mel_floor''': 1E-10,
													    '''reduction_factor''': 2,
													    '''return_attention_mask''': True,
													}

													__UpperCamelCase       					=     os.path.join(self.tmpdirname       ,		SCREAMING_SNAKE_CASE_     )
													with open(self.feature_extraction_file       ,		'''w'''       ,		encoding='''utf-8'''     ) as fp:
																			fp.write(json.dumps(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				str:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_tokenizer()
													__UpperCamelCase       					=     self.get_feature_extractor()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													processor.save_pretrained(self.tmpdirname     )
													__UpperCamelCase       					=     SpeechTaProcessor.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       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,		feature_extractor=self.get_feature_extractor()     )
													processor.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     self.get_tokenizer(bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''     )
													__UpperCamelCase       					=     self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													__UpperCamelCase       					=     SpeechTaProcessor.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       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio=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       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio_target=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       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

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



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text_target=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

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



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

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

													__UpperCamelCase       					=     processor.batch_decode(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



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





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(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'''       ,		)




