





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowerCAmelCase_        =							logging.get_logger(__name__)

lowerCAmelCase_        =							OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

lowerCAmelCase_        =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Any:






				"""simple docstring"""


				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												snake_case_		:       Union[str, Any]           =    model_type_to_module_name(_UpperCamelCase					)

												snake_case_		:       Dict           =    importlib.import_module(f'''.{module_name}'''					,							'''transformers.models'''					)
												try:
																return getattr(_UpperCamelCase					,							_UpperCamelCase					)
												except AttributeError:
																continue

				for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
								if getattr(_UpperCamelCase					,							'''__name__'''					,							_UpperCamelCase					) == class_name:
												return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
				snake_case_		:       Union[str, Any]           =    importlib.import_module('''transformers'''					)
				if hasattr(_UpperCamelCase					,							_UpperCamelCase					):
								return getattr(_UpperCamelCase					,							_UpperCamelCase					)

				return None






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase = None					,							_UpperCamelCase = False					,							_UpperCamelCase = False					,							_UpperCamelCase = None					,							_UpperCamelCase = None					,							_UpperCamelCase = None					,							_UpperCamelCase = False					,							**_UpperCamelCase					,							)						->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:       Optional[Any]           =    get_file_from_repo(
				    _UpperCamelCase					,							_UpperCamelCase					,							cache_dir=_UpperCamelCase					,							force_download=_UpperCamelCase					,							resume_download=_UpperCamelCase					,							proxies=_UpperCamelCase					,							use_auth_token=_UpperCamelCase					,							revision=_UpperCamelCase					,							local_files_only=_UpperCamelCase					,							)
				if resolved_config_file is None:
								logger.info(
								    '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''					)
								return {}

				with open(_UpperCamelCase					,							encoding='''utf-8'''					) as reader:
								return json.load(_UpperCamelCase					)





class    __lowerCAmelCase			:






			def __init__(self       )	->  Optional[Any]:


							'''simple docstring'''



							raise EnvironmentError(
							    '''AutoFeatureExtractor is designed to be instantiated '''
							    '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'''       )






			@classmethod
			@replace_list_option_in_docstrings(__magic_name__       )
			def      lowerCamelCase    (cls   ,				__magic_name__   ,				**__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Dict           =    kwargs.pop('''config'''   ,				__magic_name__       )
							snake_case_		:       Union[str, Any]           =    kwargs.pop('''trust_remote_code'''   ,				__magic_name__       )
							snake_case_		:       Tuple           =    True

							snake_case_     ,				snake_case_		:       Tuple           =    FeatureExtractionMixin.get_feature_extractor_dict(__magic_name__   ,				**__magic_name__       )
							snake_case_		:       int           =    config_dict.get('''feature_extractor_type'''   ,				__magic_name__       )
							snake_case_		:       str           =    None
							if "AutoFeatureExtractor" in config_dict.get('''auto_map'''   ,				{}       ):
											snake_case_		:       Any           =    config_dict['''auto_map''']['''AutoFeatureExtractor''']

							# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
							if feature_extractor_class is None and feature_extractor_auto_map is None:
											if not isinstance(__magic_name__   ,				__magic_name__       ):
															snake_case_		:       Any           =    AutoConfig.from_pretrained(__magic_name__   ,				**__magic_name__       )
											# It could be in `config.feature_extractor_type``
											snake_case_		:       List[Any]           =    getattr(__magic_name__   ,				'''feature_extractor_type'''   ,				__magic_name__       )
											if hasattr(__magic_name__   ,				'''auto_map'''       ) and "AutoFeatureExtractor" in config.auto_map:
															snake_case_		:       List[str]           =    config.auto_map['''AutoFeatureExtractor''']

							if feature_extractor_class is not None:
											snake_case_		:       List[Any]           =    feature_extractor_class_from_name(__magic_name__       )

							snake_case_		:       Optional[int]           =    feature_extractor_auto_map is not None
							snake_case_		:       Tuple           =    feature_extractor_class is not None or type(__magic_name__       ) in FEATURE_EXTRACTOR_MAPPING
							snake_case_		:       Optional[int]           =    resolve_trust_remote_code(
							    __magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )

							if has_remote_code and trust_remote_code:
											snake_case_		:       int           =    get_class_from_dynamic_module(
											    __magic_name__   ,				__magic_name__   ,				**__magic_name__       )
											snake_case_		:       int           =    kwargs.pop('''code_revision'''   ,				__magic_name__       )
											if os.path.isdir(__magic_name__       ):
															feature_extractor_class.register_for_auto_class()
											return feature_extractor_class.from_dict(__magic_name__   ,				**__magic_name__       )
							elif feature_extractor_class is not None:
											return feature_extractor_class.from_dict(__magic_name__   ,				**__magic_name__       )
							# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
							elif type(__magic_name__       ) in FEATURE_EXTRACTOR_MAPPING:
											snake_case_		:       Optional[Any]           =    FEATURE_EXTRACTOR_MAPPING[type(__magic_name__       )]
											return feature_extractor_class.from_dict(__magic_name__   ,				**__magic_name__       )

							raise ValueError(
							    F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
							    F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
							    F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()       )}'''       )






			@staticmethod
			def      lowerCamelCase    (__magic_name__   ,				__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



							FEATURE_EXTRACTOR_MAPPING.register(__magic_name__   ,				__magic_name__       )



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      , _UpperCamelCase      , _UpperCamelCase=sys.maxsize   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							"""bilinear"""
              __snake_case       	=							max_size
              __snake_case       	=							short_edge_length






       def __call__(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


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

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

              return img_augs






class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


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






              """simple docstring"""


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

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







def 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :List[Any]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Tuple	,__A    :Tuple[int, int]		):


       """simple docstring"""




       assert torch.isfinite(__A		).all(), "Box tensor contains infinite or NaN!"
       __snake_case	,    __snake_case       	=							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		)

