


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]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Any=sys.maxsize							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				'''bilinear'''
									lowercase__						=				max_size
									lowercase__						=				short_edge_length


							def __call__(     self:     List[Any]   ,						UpperCamelCase_:     Optional[int]							)      ->       Any:





									"""simple docstring"""







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

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

									return img_augs


class 	_a							:

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





									"""simple docstring"""







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

							def 				lowerCamelCase_			(     self:     str   ,						UpperCamelCase_:     Optional[int]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				tuple(max(UpperCamelCase_							) for s in zip(*[img.shape for img in images]							)							)
									lowercase__						=				[im.shape[-2:] for im in images]
									lowercase__						=				[
									    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:     Any   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[int]=False							)      ->       List[Any]:





									"""simple docstring"""







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

											if self.size_divisibility > 0:
													raise NotImplementedError()
											# pad
											lowercase__						=				torch.true_divide(UpperCamelCase_   ,						UpperCamelCase_							)
											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!"
		lowercase__					,		lowercase__						=				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					)






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      int  				=			'''git_vision_model'''

							def __init__(							self:     Tuple   ,						UpperCamelCase_:     Optional[int]=768   ,						UpperCamelCase_:     Optional[int]=3_072   ,						UpperCamelCase_:     Optional[int]=12   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3   ,						UpperCamelCase_:     str=224   ,						UpperCamelCase_:     int=16   ,						UpperCamelCase_:     Any="quick_gelu"   ,						UpperCamelCase_:     Union[str, Any]=1E-5   ,						UpperCamelCase_:     Tuple=0.0   ,						UpperCamelCase_:     Tuple=0.02   ,						**UpperCamelCase_:     str   ,						)      ->       Optional[int]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

									lowercase__						=				hidden_size
									lowercase__						=				intermediate_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				num_channels
									lowercase__						=				patch_size
									lowercase__						=				image_size
									lowercase__						=				initializer_range
									lowercase__						=				attention_dropout
									lowercase__						=				layer_norm_eps
									lowercase__						=				hidden_act


							@classmethod
							def 				lowerCamelCase_			(							cls:     Optional[Any]   ,						UpperCamelCase_:     Union[str, os.PathLike]   ,						**UpperCamelCase_:     List[Any]							)      ->       "PretrainedConfig":





									"""simple docstring"""







									cls._set_token_in_kwargs(UpperCamelCase_							)

									lowercase__					,		lowercase__						=				cls.get_config_dict(UpperCamelCase_   ,						**UpperCamelCase_							)

									# get the vision config dict if we are loading from GITConfig
									if config_dict.get('''model_type'''							) == "git":
											lowercase__						=				config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls   ,						'''model_type'''							) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'							)

									return cls.from_dict(UpperCamelCase_   ,						**UpperCamelCase_							)


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Optional[int]  				=			'''git'''

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     Optional[Any]=None   ,						UpperCamelCase_:     List[str]=30_522   ,						UpperCamelCase_:     List[Any]=768   ,						UpperCamelCase_:     List[Any]=6   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3_072   ,						UpperCamelCase_:     Any="gelu"   ,						UpperCamelCase_:     List[Any]=0.1   ,						UpperCamelCase_:     Dict=0.1   ,						UpperCamelCase_:     str=1_024   ,						UpperCamelCase_:     Any=0.02   ,						UpperCamelCase_:     str=1E-1_2   ,						UpperCamelCase_:     Optional[int]=0   ,						UpperCamelCase_:     Optional[Any]="absolute"   ,						UpperCamelCase_:     Optional[int]=True   ,						UpperCamelCase_:     List[Any]=False   ,						UpperCamelCase_:     List[Any]=101   ,						UpperCamelCase_:     int=102   ,						UpperCamelCase_:     List[str]=None   ,						**UpperCamelCase_:     int   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									super().__init__(bos_token_id=UpperCamelCase_   ,						eos_token_id=UpperCamelCase_   ,						pad_token_id=UpperCamelCase_   ,						**UpperCamelCase_							)

									if vision_config is None:
											lowercase__						=				{}
											logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''							)

									lowercase__						=				GitVisionConfig(**UpperCamelCase_							)
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				position_embedding_type
									lowercase__						=				use_cache
									lowercase__						=				tie_word_embeddings
									lowercase__						=				num_image_with_embedding

									lowercase__						=				bos_token_id
									lowercase__						=				eos_token_id


							def 				lowerCamelCase_			(							self:     Dict							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				copy.deepcopy(self.__dict__							)
									lowercase__						=				self.vision_config.to_dict()
									lowercase__						=				self.__class__.model_type
									return output


