"""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        __UpperCAmelCase						:

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase=sys.maxsize				):
										lowerCamelCase__ ="bilinear"
										lowerCamelCase__ =max_size
										lowerCamelCase__ =short_edge_length






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

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

										return img_augs






class        __UpperCAmelCase						:

				def __init__(				self     ,			_lowerCamelCase				):
										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 _lowerCamelCase				: (x - self.pixel_mean) / self.pixel_std

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =tuple(max(_lowerCamelCase				) for s in zip(*[img.shape for img in images]				)				)
										lowerCamelCase__ =[im.shape[-2:] for im in images]
										lowerCamelCase__ =[
										    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				):
										with torch.no_grad():
																if not isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																						lowerCamelCase__ =[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
																lowerCamelCase__ =torch.tensor([im.shape[:2] for im in images]				)
																lowerCamelCase__ =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
																lowerCamelCase__ =[self.normalizer(_lowerCamelCase				) for x in images]
																# now pad them to do the following operations
																lowerCamelCase__ ,				lowerCamelCase__ =self.pad(_lowerCamelCase				)
																# Normalize

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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> str:




						'''simple docstring'''



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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> int:




						'''simple docstring'''



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



"""simple docstring"""


from typing import TYPE_CHECKING

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


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['OpenAIGPTTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OpenAIGPTDoubleHeadsModel',
	    'OpenAIGPTForSequenceClassification',
	    'OpenAIGPTLMHeadModel',
	    'OpenAIGPTModel',
	    'OpenAIGPTPreTrainedModel',
	    'load_tf_weights_in_openai_gpt',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFOpenAIGPTDoubleHeadsModel',
	    'TFOpenAIGPTForSequenceClassification',
	    'TFOpenAIGPTLMHeadModel',
	    'TFOpenAIGPTMainLayer',
	    'TFOpenAIGPTModel',
	    'TFOpenAIGPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

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


