


'''simple docstring'''







from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


A      :     List[str]										= logging.get_logger(__name__)


class  __lowerCamelCase (       nn.Module       ):
    """simple docstring"""
    a													=    42
    a													=    jnp.floataa


    def    A    (  self			:							int):
           _A     :	str      =					self.config.text_config
           _A     :	Optional[int]      =					self.config.vision_config

           _A     :	Optional[Any]      =					self.config.projection_dim
           _A     :	Union[str, Any]      =					text_config.hidden_size
           _A     :	str      =					vision_config.hidden_size

           _A     :	Dict      =					FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
           _A     :	Union[str, Any]      =					FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__      ,							SCREAMING_SNAKE_CASE).module_class

           _A     :	List[str]      =					text_module(SCREAMING_SNAKE_CASE      ,							dtype=self.dtype)
           _A     :	Optional[int]      =					vision_module(SCREAMING_SNAKE_CASE      ,							dtype=self.dtype)

           _A     :	List[Any]      =					nn.Dense(
               self.projection_dim      ,							dtype=self.dtype      ,							kernel_init=jax.nn.initializers.normal(0.02)      ,							use_bias=SCREAMING_SNAKE_CASE      ,							)
           _A     :	Union[str, Any]      =					nn.Dense(
               self.projection_dim      ,							dtype=self.dtype      ,							kernel_init=jax.nn.initializers.normal(0.02)      ,							use_bias=SCREAMING_SNAKE_CASE      ,							)
           _A     :	Any      =					self.param('logit_scale'      ,							jax.nn.initializers.ones      ,							[])


    def __call__(  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=None      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=None      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=None      ,							SCREAMING_SNAKE_CASE			:							List[str]=None      ,							SCREAMING_SNAKE_CASE			:							Tuple=None      ,							SCREAMING_SNAKE_CASE			:							bool = True      ,							SCREAMING_SNAKE_CASE			:							Optional[int]=None      ,							SCREAMING_SNAKE_CASE			:							str=None      ,							SCREAMING_SNAKE_CASE			:							str=None      ,							):
           _A     :	str      =					return_dict if return_dict is not None else self.config.return_dict

           _A     :	Optional[int]      =					self.vision_model(
               pixel_values=SCREAMING_SNAKE_CASE      ,							deterministic=SCREAMING_SNAKE_CASE      ,							output_attentions=SCREAMING_SNAKE_CASE      ,							output_hidden_states=SCREAMING_SNAKE_CASE      ,							return_dict=SCREAMING_SNAKE_CASE      ,							)

           _A     :	List[Any]      =					self.text_model(
               input_ids=SCREAMING_SNAKE_CASE      ,							attention_mask=SCREAMING_SNAKE_CASE      ,							token_type_ids=SCREAMING_SNAKE_CASE      ,							position_ids=SCREAMING_SNAKE_CASE      ,							deterministic=SCREAMING_SNAKE_CASE      ,							output_attentions=SCREAMING_SNAKE_CASE      ,							output_hidden_states=SCREAMING_SNAKE_CASE      ,							return_dict=SCREAMING_SNAKE_CASE      ,							)

           _A     :	Optional[Any]      =					vision_outputs[1]
           _A     :	Any      =					self.visual_projection(SCREAMING_SNAKE_CASE)

           _A     :	List[str]      =					text_outputs[1]
           _A     :	Dict      =					self.text_projection(SCREAMING_SNAKE_CASE)

           # normalized features
           _A     :	Tuple      =					image_embeds / jnp.linalg.norm(SCREAMING_SNAKE_CASE      ,							axis=-1      ,							keepdims=SCREAMING_SNAKE_CASE)
           _A     :	int      =					text_embeds / jnp.linalg.norm(SCREAMING_SNAKE_CASE      ,							axis=-1      ,							keepdims=SCREAMING_SNAKE_CASE)

           # cosine similarity as logits
           _A     :	Optional[int]      =					jnp.exp(self.logit_scale)
           _A     :	Tuple      =					jnp.matmul(SCREAMING_SNAKE_CASE      ,							image_embeds.T) * logit_scale
           _A     :	Dict      =					logits_per_text.T

           if not return_dict:
                  return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

           return FlaxCLIPOutput(
               logits_per_image=SCREAMING_SNAKE_CASE      ,							logits_per_text=SCREAMING_SNAKE_CASE      ,							text_embeds=SCREAMING_SNAKE_CASE      ,							image_embeds=SCREAMING_SNAKE_CASE      ,							text_model_output=SCREAMING_SNAKE_CASE      ,							vision_model_output=SCREAMING_SNAKE_CASE      ,							)




class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    HybridCLIPConfig
    a													=    FlaxHybridCLIPModule


    def __init__(  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							HybridCLIPConfig      ,							SCREAMING_SNAKE_CASE			:							Optional[Tuple] = None      ,							SCREAMING_SNAKE_CASE			:							int = 0      ,							SCREAMING_SNAKE_CASE			:							jnp.dtype = jnp.floataa      ,							**SCREAMING_SNAKE_CASE			:							str      ,							):
           if input_shape is None:
                  _A     :	Dict      =					((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

           _A     :	List[Any]      =					self.module_class(config=SCREAMING_SNAKE_CASE      ,							dtype=SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)
           super().__init__(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							input_shape=SCREAMING_SNAKE_CASE      ,							seed=SCREAMING_SNAKE_CASE      ,							dtype=SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							jax.random.PRNGKey      ,							SCREAMING_SNAKE_CASE			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							FrozenDict = None):
           # init input tensor
           _A     :	Optional[Any]      =					jnp.zeros(input_shape[0]      ,							dtype='i4')
           _A     :	Any      =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(SCREAMING_SNAKE_CASE).shape[-1])      ,							input_shape[0])
           _A     :	str      =					jnp.ones_like(SCREAMING_SNAKE_CASE)
           _A     :	List[Any]      =					jnp.ones_like(SCREAMING_SNAKE_CASE)

           _A     :	Optional[Any]      =					jax.random.normal(SCREAMING_SNAKE_CASE      ,							input_shape[1])

           _A  , _A     :	Optional[Any]      =					jax.random.split(SCREAMING_SNAKE_CASE)
           _A     :	List[str]      =					{'params': params_rng, 'dropout': dropout_rng}

           return self.module.init(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)["params"]


    def __call__(  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Tuple=None      ,							SCREAMING_SNAKE_CASE			:							Tuple=None      ,							SCREAMING_SNAKE_CASE			:							List[Any]=None      ,							SCREAMING_SNAKE_CASE			:							dict = None      ,							SCREAMING_SNAKE_CASE			:							jax.random.PRNGKey = None      ,							SCREAMING_SNAKE_CASE			:							bool = False      ,							SCREAMING_SNAKE_CASE			:							Optional[bool] = None      ,							SCREAMING_SNAKE_CASE			:							Optional[bool] = None      ,							SCREAMING_SNAKE_CASE			:							Optional[bool] = None      ,							):
           _A     :	int      =					output_attentions if output_attentions is not None else self.config.output_attentions
           _A     :	Union[str, Any]      =					(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _A     :	Optional[Any]      =					return_dict if return_dict is not None else self.config.return_dict

           if position_ids is None:
                  _A     :	str      =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(SCREAMING_SNAKE_CASE).shape[-1])      ,							input_ids.shape)

           if token_type_ids is None:
                  _A     :	Optional[Any]      =					jnp.zeros_like(SCREAMING_SNAKE_CASE)

           if attention_mask is None:
                  _A     :	Tuple      =					jnp.ones_like(SCREAMING_SNAKE_CASE)

           # Handle any PRNG if needed
           _A     :	List[Any]      =					{}
           if dropout_rng is not None:
                  _A     :	int      =					dropout_rng

           return self.module.apply(
               {'params': params or self.params}      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype=jnp.floataa)      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							not train      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							rngs=SCREAMING_SNAKE_CASE      ,							)


    def    A    (  self			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							List[Any]=None      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=None      ,							SCREAMING_SNAKE_CASE			:							Dict=None      ,							SCREAMING_SNAKE_CASE			:							dict = None      ,							SCREAMING_SNAKE_CASE			:							jax.random.PRNGKey = None      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=False      ,							):
           if position_ids is None:
                  _A     :	Any      =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(SCREAMING_SNAKE_CASE).shape[-1])      ,							input_ids.shape)

           if token_type_ids is None:
                  _A     :	Optional[Any]      =					jnp.zeros_like(SCREAMING_SNAKE_CASE)

           if attention_mask is None:
                  _A     :	Optional[int]      =					jnp.ones_like(SCREAMING_SNAKE_CASE)

           # Handle any PRNG if needed
           _A     :	Union[str, Any]      =					{}
           if dropout_rng is not None:
                  _A     :	List[str]      =					dropout_rng

           def _get_features(SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Optional[int]):
                  _A     :	str      =					module.text_model(
                      input_ids=SCREAMING_SNAKE_CASE      ,							attention_mask=SCREAMING_SNAKE_CASE      ,							position_ids=SCREAMING_SNAKE_CASE      ,							token_type_ids=SCREAMING_SNAKE_CASE      ,							deterministic=SCREAMING_SNAKE_CASE      ,							)
                  _A     :	Any      =					text_outputs[1]
                  _A     :	Optional[int]      =					module.text_projection(SCREAMING_SNAKE_CASE)
                  return text_features

           return self.module.apply(
               {'params': params or self.params}      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype='i4')      ,							not train      ,							method=_get_features      ,							rngs=SCREAMING_SNAKE_CASE      ,							)


    def    A    (  self			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							dict = None      ,							SCREAMING_SNAKE_CASE			:							jax.random.PRNGKey = None      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=False):
           _A     :	List[str]      =					{}
           if dropout_rng is not None:
                  _A     :	List[str]      =					dropout_rng

           def _get_features(SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]):
                  _A     :	int      =					module.vision_model(pixel_values=SCREAMING_SNAKE_CASE      ,							deterministic=SCREAMING_SNAKE_CASE)
                  _A     :	Union[str, Any]      =					vision_outputs[1]  # pooled_output
                  _A     :	List[str]      =					module.visual_projection(SCREAMING_SNAKE_CASE)
                  return image_features

           return self.module.apply(
               {'params': params or self.params}      ,							jnp.array(SCREAMING_SNAKE_CASE      ,							dtype=jnp.floataa)      ,							not train      ,							method=_get_features      ,							rngs=SCREAMING_SNAKE_CASE      ,							)


    @classmethod
    def    A    (  cls			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							str = None      ,							SCREAMING_SNAKE_CASE			:							str = None      ,							*SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							**SCREAMING_SNAKE_CASE			:							Optional[int]      ,							):
           _A     :	Tuple      =					{
               argument[len('text_') :]: value for argument, value in kwargs.items() if argument.startswith('text_')
           }

           _A     :	int      =					{
               argument[len('vision_') :]: value for argument, value in kwargs.items() if argument.startswith('vision_')
           }

           # remove text, vision kwargs from kwargs
           for key in kwargs_text.keys():
                  del kwargs["text_" + key]
           for key in kwargs_vision.keys():
                  del kwargs["vision_" + key]

           # Load and initialize the text and vision model
           _A     :	str      =					kwargs_text.pop('model'      ,							SCREAMING_SNAKE_CASE)
           if text_model is None:
                  assert (
                      text_model_name_or_path is not None
                  ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
                  from transformers import FlaxAutoModel

                  if "config" not in kwargs_text:
                         from transformers import AutoConfig

                         _A     :	Tuple      =					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
                         _A     :	Tuple      =					text_config

                  _A     :	Tuple      =					FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE      ,							*SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)

           _A     :	str      =					kwargs_vision.pop('model'      ,							SCREAMING_SNAKE_CASE)
           if vision_model is None:
                  assert (
                      vision_model_name_or_path is not None
                  ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
                  from transformers import FlaxAutoModel

                  if "config" not in kwargs_vision:
                         from transformers import AutoConfig

                         _A     :	List[Any]      =					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
                         _A     :	str      =					vision_config

                  _A     :	Tuple      =					FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE      ,							*SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)

           # instantiate config with corresponding kwargs
           _A     :	Dict      =					kwargs.pop('dtype'      ,							jnp.floataa)
           _A     :	List[Any]      =					HybridCLIPConfig.from_text_vision_configs(text_model.config      ,							vision_model.config      ,							**SCREAMING_SNAKE_CASE)

           # init model
           _A     :	str      =					cls(SCREAMING_SNAKE_CASE      ,							*SCREAMING_SNAKE_CASE      ,							dtype=SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE)

           if vision_config.model_type == "clip":
                  _A     :	List[Any]      =					vision_model.params['vision_model']
                  _A     :	List[Any]      =					vision_model.params['visual_projection']['kernel']
           else:
                  _A     :	List[Any]      =					vision_model.params

           _A     :	List[Any]      =					text_model.params

           return model










'''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 :
    """simple docstring"""


    def __init__(  self			:							str      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							List[Any]=sys.maxsize):
           _A     :	Tuple      =					'bilinear'
           _A     :	List[Any]      =					max_size
           _A     :	Optional[int]      =					short_edge_length


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

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

           return img_augs




class  __lowerCamelCase :
    """simple docstring"""


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


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	Any      =					tuple(max(SCREAMING_SNAKE_CASE) for s in zip(*[img.shape for img in images]))
           _A     :	str      =					[im.shape[-2:] for im in images]
           _A     :	int      =					[
               nn.functional.pad(
                   SCREAMING_SNAKE_CASE      ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,							value=self.pad_value      ,							)
               for size, im in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           ]

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


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

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






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			Union[str, Any]   ):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes






def       lowerCAmelCase__       (				lowerCamelCase     :			Any  ,lowerCamelCase     :			Tuple[int, int]   ):
       assert torch.isfinite(lowerCamelCase   ).all(), "Box tensor contains infinite or NaN!"
       _A  , _A     :	Union[str, Any]      =					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   )






