
'''simple docstring'''

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 				A       (		unittest.TestCase      ):





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							"<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(" "							)
				lowerCAmelCase__									=							dict(zip(lowerCamelCase__ ,							range(len(lowerCamelCase__							)							)							)							)

				lowerCAmelCase__									=							{
				    "pad_token": "<pad>",
				    "unk_token": "<unk>",
				    "bos_token": "<s>",
				    "eos_token": "</s>",
				}
				lowerCAmelCase__									=							{
				    "feature_size": 1,
				    "padding_value": 0.0,
				    "sampling_rate": 16000,
				    "return_attention_mask": False,
				    "do_normalize": True,
				}

				lowerCAmelCase__									=							tempfile.mkdtemp()
				lowerCAmelCase__									=							os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]							)
				lowerCAmelCase__									=							os.path.join(self.tmpdirname ,							lowerCamelCase__							)
				with open(self.vocab_file ,							"w" ,							encoding="utf-8"							) as fp:
							fp.write(json.dumps(lowerCamelCase__							) + "\n"							)

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							**__magic_name__     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.add_kwargs_tokens_map.copy()
				kwargs.update(lowerCamelCase__							)
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,							**lowerCamelCase__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							**__magic_name__     :    str							):


				"""simple docstring"""

				return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,							**lowerCamelCase__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				shutil.rmtree(self.tmpdirname							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_tokenizer()
				lowerCAmelCase__									=							self.get_feature_extractor()

				lowerCAmelCase__									=							WavaVecaProcessor(tokenizer=lowerCamelCase__ ,							feature_extractor=lowerCamelCase__							)

				processor.save_pretrained(self.tmpdirname							)
				lowerCAmelCase__									=							WavaVecaProcessor.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 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							WavaVecaProcessor(tokenizer=self.get_tokenizer() ,							feature_extractor=self.get_feature_extractor()							)
				processor.save_pretrained(self.tmpdirname							)

				lowerCAmelCase__									=							self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"							)
				lowerCAmelCase__									=							self.get_feature_extractor(do_normalize=lowerCamelCase__ ,							padding_value=1.0							)

				lowerCAmelCase__									=							WavaVecaProcessor.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 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_feature_extractor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							WavaVecaProcessor(tokenizer=lowerCamelCase__ ,							feature_extractor=lowerCamelCase__							)

				lowerCAmelCase__									=							floats_list((3, 1000)							)

				lowerCAmelCase__									=							feature_extractor(lowerCamelCase__ ,							return_tensors="np"							)
				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_feature_extractor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							WavaVecaProcessor(tokenizer=lowerCamelCase__ ,							feature_extractor=lowerCamelCase__							)

				lowerCAmelCase__									=							"This is a test string"

				lowerCAmelCase__									=							processor(text=lowerCamelCase__							)

				lowerCAmelCase__									=							tokenizer(lowerCamelCase__							)

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_feature_extractor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							WavaVecaProcessor(tokenizer=lowerCamelCase__ ,							feature_extractor=lowerCamelCase__							)

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

				lowerCAmelCase__									=							processor.batch_decode(lowerCamelCase__							)
				lowerCAmelCase__									=							tokenizer.batch_decode(lowerCamelCase__							)

				self.assertListEqual(lowerCamelCase__ ,							lowerCamelCase__							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_feature_extractor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							WavaVecaProcessor(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" ,							)







'''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     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=sys.maxsize							):


				"""simple docstring"""

				lowerCAmelCase__									=							"bilinear"
				lowerCAmelCase__									=							max_size
				lowerCAmelCase__									=							short_edge_length







	def __call__(   self     :    List[str] ,							__magic_name__     :    int							):


				"""simple docstring"""

				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(__magic_name__ ,							__magic_name__							)
							if h < w:
										lowerCAmelCase__		,lowerCAmelCase__									=							size, scale * w
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							scale * h, size
							if max(__magic_name__ ,							__magic_name__							) > self.max_size:
										lowerCAmelCase__									=							self.max_size * 1.0 / max(__magic_name__ ,							__magic_name__							)
										lowerCAmelCase__									=							newh * scale
										lowerCAmelCase__									=							neww * scale
							lowerCAmelCase__									=							int(neww + 0.5							)
							lowerCAmelCase__									=							int(newh + 0.5							)

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

				return img_augs






class 				A       :





	def __init__(   self     :    str ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				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 __magic_name__							: (x - self.pixel_mean) / self.pixel_std





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							tuple(max(__magic_name__							) for s in zip(*[img.shape for img in images]							)							)
				lowerCAmelCase__									=							[im.shape[-2:] for im in images]
				lowerCAmelCase__									=							[
				    nn.functional.pad(
				        __magic_name__ ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,							value=self.pad_value ,							)
				    for size, im in zip(__magic_name__ ,							__magic_name__							)
				]

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







	def __call__(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    Optional[int]=False							):


				"""simple docstring"""

				with torch.no_grad():
							if not isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							[images]
							if single_image:
										assert len(__magic_name__							) == 1
							for i in range(len(__magic_name__							)							):
										if isinstance(images[i] ,							torch.Tensor							):
													images.insert(__magic_name__ ,							images.pop(__magic_name__							).to(self.device							).float()							)
										elif not isinstance(images[i] ,							torch.Tensor							):
													images.insert(
													    __magic_name__ ,							torch.as_tensor(img_tensorize(images.pop(__magic_name__							) ,							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(__magic_name__							)
							# 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(__magic_name__							) for x in images]
							# now pad them to do the following operations
							lowerCAmelCase__		,lowerCAmelCase__									=							self.pad(__magic_name__							)
							# Normalize

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







def 					A       ( UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Optional[Any] )   ->    List[str]:







			'''simple docstring'''



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







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Tuple[int, int] )   ->    Tuple:







			'''simple docstring'''



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





