





"""simple docstring"""





from typing import TYPE_CHECKING

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


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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''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:
						lowerCAmelCase__        =      [
						    '''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

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













"""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 							_lowerCamelCase   :


						def __init__(self ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




						def __call__(self ,				__a		)			-> Dict:
								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 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Any:
								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 ,				__a		)			-> List[Any]:
								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 ,				__a ,				__a=False		)			-> Optional[int]:
								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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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






