


"""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	,					lowerCamelCase__	,					lowerCamelCase__=sys.maxsize)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      "bilinear"
								snake_case__  :						Optional[int]		=      max_size
								snake_case__  :						Optional[int]		=      short_edge_length







							def __call__(						self	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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

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

								return img_augs


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




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

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







							def __call__(						self	,					lowerCamelCase__	,					lowerCamelCase__=False)   ->	Optional[Any]:







								'''simple docstring'''




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

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



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      int	)							->	Union[str, Any]:

	'''simple docstring'''


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



def 			A__		(					_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      Tuple[int, int]	)							->	Any:

	'''simple docstring'''


	assert torch.isfinite(_UpperCAmelCase	).all(), "Box tensor contains infinite or NaN!"
	snake_case__,					snake_case__  :						List[str]		=      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	)









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			snake_case__  				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


