'''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				:							List[Any]     ,		__a				:							int     ,		__a				:							Optional[Any]=sys.maxsize	)							->	Union[str, Any]:
								__UpperCAmelCase							=			'''bilinear'''
								__UpperCAmelCase							=			max_size
								__UpperCAmelCase							=			short_edge_length






							def __call__(      self				:							List[Any]     ,		__a				:							Optional[Any]	)							->	int:
								__UpperCAmelCase							=			[]
								for img in imgs:
									__UpperCAmelCase				,			__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(__a     ,		__a	)
									if h < w:
										__UpperCAmelCase				,			__UpperCAmelCase							=			size, scale * w
									else:
										__UpperCAmelCase				,			__UpperCAmelCase							=			scale * h, size
									if max(__a     ,		__a	) > self.max_size:
										__UpperCAmelCase							=			self.max_size * 1.0 / max(__a     ,		__a	)
										__UpperCAmelCase							=			newh * scale
										__UpperCAmelCase							=			neww * scale
									__UpperCAmelCase							=			int(neww + 0.5	)
									__UpperCAmelCase							=			int(newh + 0.5	)

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

								return img_augs






class 						A   :





							def __init__(      self				:							Union[str, Any]     ,		__a				:							Any	)							->	Optional[int]:
								__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 __a	: (x - self.pixel_mean) / self.pixel_std





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Optional[Any]	)							->	str:
								__UpperCAmelCase							=			tuple(max(__a	) for s in zip(*[img.shape for img in images]	)	)
								__UpperCAmelCase							=			[im.shape[-2:] for im in images]
								__UpperCAmelCase							=			[
								    nn.functional.pad(
								        __a     ,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]     ,		value=self.pad_value     ,		)
								    for size, im in zip(__a     ,		__a	)
								]

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






							def __call__(      self				:							Tuple     ,		__a				:							Optional[int]     ,		__a				:							Union[str, Any]=False	)							->	Union[str, Any]:
								with torch.no_grad():
									if not isinstance(__a     ,		__a	):
										__UpperCAmelCase							=			[images]
									if single_image:
										assert len(__a	) == 1
									for i in range(len(__a	)	):
										if isinstance(images[i]     ,		torch.Tensor	):
											images.insert(__a     ,		images.pop(__a	).to(self.device	).float()	)
										elif not isinstance(images[i]     ,		torch.Tensor	):
											images.insert(
											    __a     ,		torch.as_tensor(img_tensorize(images.pop(__a	)     ,		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(__a	)
									# 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(__a	) for x in images]
									# now pad them to do the following operations
									__UpperCAmelCase				,			__UpperCAmelCase							=			self.pad(__a	)
									# Normalize

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





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Any       ):


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





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Tuple[int, int]       ):


	"""simple docstring"""
	assert torch.isfinite(UpperCamelCase__       ).all(), "Box tensor contains infinite or NaN!"
	__UpperCAmelCase				,			__UpperCAmelCase							=			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__       )

'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase	:       Optional[Any]						       =	{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	[
					    "LlamaForCausalLM",
					    "LlamaModel",
					    "LlamaPreTrainedModel",
					    "LlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					__lowerCAmelCase	:       Optional[Any]						       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
