





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "Wav2Vec2FeatureExtractor"
							UpperCAmelCase_      						=   "AutoTokenizer"






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





														"""simple docstring"""
														super().__init__(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.feature_extractor
														SCREAMING_SNAKE_CASE__       :      Tuple     =						False






							@classmethod
							def  A_     ( cls     :					List[str],			_UpperCAmelCase     :					int,			**_UpperCAmelCase     :					int						)				->			Dict:





														"""simple docstring"""
														try:
																					return super().from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)
														except OSError:
																					warnings.warn(
																					    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
																					    " include a `tokenizer_class` attribute is deprecated and will be "
																					    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
																					    " attribute to either your `config.json` or `tokenizer_config.json` "
																					    "file to suppress this warning: ",			_UpperCAmelCase,			)

																					SCREAMING_SNAKE_CASE__       :      str     =						WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						WavaVecaCTCTokenizer.from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)

																					return cls(feature_extractor=_UpperCAmelCase,			tokenizer=_UpperCAmelCase						)






							def __call__( self     :					List[Any],			*_UpperCAmelCase     :					Tuple,			**_UpperCAmelCase     :					Dict						)				->			List[Any]:





														"""simple docstring"""
														# For backward compatibility
														if self._in_target_context_manager:
																					return self.current_processor(*_UpperCAmelCase,			**_UpperCAmelCase						)

														if "raw_speech" in kwargs:
																					warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."						)
																					SCREAMING_SNAKE_CASE__       :      Any     =						kwargs.pop("raw_speech"						)
														else:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						kwargs.pop("audio",			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						kwargs.pop("sampling_rate",			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						kwargs.pop("text",			_UpperCAmelCase						)
														if len(_UpperCAmelCase						) > 0:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						args[0]
																					SCREAMING_SNAKE_CASE__       :      int     =						args[1:]

														if audio is None and text is None:
																					raise ValueError("You need to specify either an `audio` or `text` input to process."						)

														if audio is not None:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self.feature_extractor(_UpperCAmelCase,			*_UpperCAmelCase,			sampling_rate=_UpperCAmelCase,			**_UpperCAmelCase						)
														if text is not None:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.tokenizer(_UpperCAmelCase,			**_UpperCAmelCase						)

														if text is None:
																					return inputs
														elif audio is None:
																					return encodings
														else:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						encodings["input_ids"]
																					return inputs






							def  A_     ( self     :					List[Any],			*_UpperCAmelCase     :					Tuple,			**_UpperCAmelCase     :					List[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														# For backward compatibility
														if self._in_target_context_manager:
																					return self.current_processor.pad(*_UpperCAmelCase,			**_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						kwargs.pop("input_features",			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						kwargs.pop("labels",			_UpperCAmelCase						)
														if len(_UpperCAmelCase						) > 0:
																					SCREAMING_SNAKE_CASE__       :      str     =						args[0]
																					SCREAMING_SNAKE_CASE__       :      int     =						args[1:]

														if input_features is not None:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.feature_extractor.pad(_UpperCAmelCase,			*_UpperCAmelCase,			**_UpperCAmelCase						)
														if labels is not None:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.tokenizer.pad(_UpperCAmelCase,			**_UpperCAmelCase						)

														if labels is None:
																					return input_features
														elif input_features is None:
																					return labels
														else:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						labels["input_ids"]
																					return input_features






							def  A_     ( self     :					Any,			*_UpperCAmelCase     :					Tuple,			**_UpperCAmelCase     :					Tuple						)				->			Any:





														"""simple docstring"""
														return self.tokenizer.batch_decode(*_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			*_UpperCAmelCase     :					Optional[Any],			**_UpperCAmelCase     :					str						)				->			Dict:





														"""simple docstring"""
														return self.tokenizer.decode(*_UpperCAmelCase,			**_UpperCAmelCase						)






							@contextmanager
							def  A_     ( self     :					Tuple						)				->			int:





														"""simple docstring"""
														warnings.warn(
														    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
														    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
														    "your audio inputs, or in a separate call."						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						True
														SCREAMING_SNAKE_CASE__       :      int     =						self.tokenizer
														yield
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.feature_extractor
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						False













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 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[Any]=sys.maxsize						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bilinear"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						short_edge_length






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





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

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

														return img_augs


class 						lowerCamelCase       :







							"""simple docstring"""




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





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






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tuple(max(_UpperCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[im.shape[-2:] for im in images]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    nn.functional.pad(
														        _UpperCAmelCase,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],			value=self.pad_value,			)
														    for size, im in zip(_UpperCAmelCase,			_UpperCAmelCase						)
														]

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






							def __call__( self     :					Any,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[str]=False						)				->			Optional[Any]:





														"""simple docstring"""
														with torch.no_grad():
																					if not isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      str     =						[images]
																					if single_image:
																												assert len(_UpperCAmelCase						) == 1
																					for i in range(len(_UpperCAmelCase						)						):
																												if isinstance(images[i],			torch.Tensor						):
																																			images.insert(_UpperCAmelCase,			images.pop(_UpperCAmelCase						).to(self.device						).float()						)
																												elif not isinstance(images[i],			torch.Tensor						):
																																			images.insert(
																																			    _UpperCAmelCase,			torch.as_tensor(img_tensorize(images.pop(_UpperCAmelCase						),			input_format=self.input_format						)						)
																																			    .to(self.device						)
																																			    .float(),			)
            # resize smallest edge
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor([im.shape[:2] for im in images]						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.aug(_UpperCAmelCase						)
																					# 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__       :      List[Any]     =						[self.normalizer(_UpperCAmelCase						) for x in images]
																					# now pad them to do the following operations
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						self.pad(_UpperCAmelCase						)
																					# Normalize

																					if self.size_divisibility > 0:
																												raise NotImplementedError()
																					# pad
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.true_divide(_UpperCAmelCase,			_UpperCAmelCase						)
																					if single_image:
																												return images[0], sizes[0], scales_yx[0]
																					else:
																												return images, sizes, scales_yx




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Union[str, Any]:

							'''simple docstring'''






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







def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple[int, int]       )	->    List[Any]:

							'''simple docstring'''






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






