



"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase       =     logging.get_logger(__name__)

_UpperCAmelCase       =     {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase	):
   for attribute in key.split("""."""	):
      SCREAMING_SNAKE_CASE_:	Tuple            =getattr(lowercase							,     lowercase	)

   if weight_type is not None:
      SCREAMING_SNAKE_CASE_:	Tuple            =getattr(lowercase							,     lowercase	).shape
   else:
      SCREAMING_SNAKE_CASE_:	str            =hf_pointer.shape

   assert hf_shape == value.shape, (
       f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
       f''' {value.shape} for {full_name}'''
   )

   if weight_type == "weight":
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =value
   elif weight_type == "weight_g":
      SCREAMING_SNAKE_CASE_:	Any            =value
   elif weight_type == "weight_v":
      SCREAMING_SNAKE_CASE_:	Dict            =value
   elif weight_type == "bias":
      SCREAMING_SNAKE_CASE_:	int            =value
   else:
      SCREAMING_SNAKE_CASE_:	Any            =value

   logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =[]
   SCREAMING_SNAKE_CASE_:	Tuple            =fairseq_model.state_dict()

   SCREAMING_SNAKE_CASE_:	List[str]            =hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      SCREAMING_SNAKE_CASE_:	int            =False
      if "conv_layers" in name:
         load_conv_layer(
             lowercase							,     lowercase							,     lowercase							,     lowercase							,     hf_model.config.feat_extract_norm == """group"""							,     )
         SCREAMING_SNAKE_CASE_:	Optional[int]            =True
      else:
         for key, mapped_key in MAPPING.items():
            SCREAMING_SNAKE_CASE_:	Any            ="""sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

            if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
               SCREAMING_SNAKE_CASE_:	Any            =True
               if "*" in mapped_key:
                  SCREAMING_SNAKE_CASE_:	Any            =name.split(lowercase	)[0].split("""."""	)[-2]
                  if not layer_index.isnumeric():
                     continue
                  SCREAMING_SNAKE_CASE_:	Optional[Any]            =mapped_key.replace("""*"""							,     lowercase	)
               if "weight_g" in name:
                  SCREAMING_SNAKE_CASE_:	Optional[Any]            ="""weight_g"""
               elif "weight_v" in name:
                  SCREAMING_SNAKE_CASE_:	Any            ="""weight_v"""
               elif "weight" in name:
                  SCREAMING_SNAKE_CASE_:	Any            ="""weight"""
               elif "bias" in name:
                  SCREAMING_SNAKE_CASE_:	Optional[int]            ="""bias"""
               else:
                  SCREAMING_SNAKE_CASE_:	Dict            =None
               set_recursively(lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase	)
            continue
      if not is_used:
         unused_weights.append(lowercase	)

   logger.warning(f'''Unused weights: {unused_weights}'''	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =full_name.split("""conv_layers."""	)[-1]
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =name.split("""."""	)
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =int(items[0]	)
   SCREAMING_SNAKE_CASE_:	List[Any]            =int(items[1]	)

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f'''{full_name} has size {value.shape}, but'''
             f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
         )
         SCREAMING_SNAKE_CASE_:	List[Any]            =value
         logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f'''{full_name} has size {value.shape}, but'''
             f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
         )
         SCREAMING_SNAKE_CASE_:	int            =value
         logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
             " found."
         )
         SCREAMING_SNAKE_CASE_:	List[str]            =value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f'''{full_name} has size {value.shape}, but'''
             f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
         )
         SCREAMING_SNAKE_CASE_:	Tuple            =value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
   else:
      unused_weights.append(lowercase	)





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	List[Any]            =SEWDConfig()
   if is_finetuned:
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =model.wav_encoder.wav_model.cfg
   else:
      SCREAMING_SNAKE_CASE_:	Dict            =model.cfg

   SCREAMING_SNAKE_CASE_:	str            =fs_config.conv_bias
   SCREAMING_SNAKE_CASE_:	int            =eval(fs_config.conv_feature_layers	)
   SCREAMING_SNAKE_CASE_:	int            =[x[0] for x in conv_layers]
   SCREAMING_SNAKE_CASE_:	Tuple            =[x[1] for x in conv_layers]
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[x[2] for x in conv_layers]
   SCREAMING_SNAKE_CASE_:	Tuple            ="""gelu"""
   SCREAMING_SNAKE_CASE_:	Dict            ="""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =0.0
   SCREAMING_SNAKE_CASE_:	List[Any]            =fs_config.activation_fn.name
   SCREAMING_SNAKE_CASE_:	Optional[int]            =fs_config.encoder_embed_dim
   SCREAMING_SNAKE_CASE_:	int            =0.02
   SCREAMING_SNAKE_CASE_:	Tuple            =fs_config.encoder_ffn_embed_dim
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =1e-5
   SCREAMING_SNAKE_CASE_:	Dict            =fs_config.encoder_layerdrop
   SCREAMING_SNAKE_CASE_:	Any            =fs_config.encoder_attention_heads
   SCREAMING_SNAKE_CASE_:	int            =fs_config.conv_pos_groups
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =fs_config.conv_pos
   SCREAMING_SNAKE_CASE_:	str            =len(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[str]            =fs_config.encoder_layers
   SCREAMING_SNAKE_CASE_:	List[Any]            =fs_config.squeeze_factor
   # DeBERTa-specific parameters:
   SCREAMING_SNAKE_CASE_:	Any            =fs_config.max_position_embeddings
   SCREAMING_SNAKE_CASE_:	List[str]            =fs_config.position_buckets
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =fs_config.share_att_key
   SCREAMING_SNAKE_CASE_:	str            =fs_config.relative_attention
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =fs_config.position_biased_input
   SCREAMING_SNAKE_CASE_:	Optional[int]            =tuple(fs_config.pos_att_type.split("""|"""	)	)
   SCREAMING_SNAKE_CASE_:	Optional[int]            =fs_config.norm_rel_ebd

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      SCREAMING_SNAKE_CASE_:	List[str]            =model.cfg
      SCREAMING_SNAKE_CASE_:	List[Any]            =fs_config.final_dropout
      SCREAMING_SNAKE_CASE_:	Any            =fs_config.layerdrop
   SCREAMING_SNAKE_CASE_:	Dict            =fs_config.activation_dropout
   SCREAMING_SNAKE_CASE_:	Any            =fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   SCREAMING_SNAKE_CASE_:	int            =fs_config.attention_dropout
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =fs_config.dropout_input
   SCREAMING_SNAKE_CASE_:	Dict            =fs_config.dropout
   SCREAMING_SNAKE_CASE_:	Dict            =fs_config.mask_channel_length
   SCREAMING_SNAKE_CASE_:	Any            =fs_config.mask_channel_prob
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =fs_config.mask_length
   SCREAMING_SNAKE_CASE_:	Optional[int]            =fs_config.mask_prob

   SCREAMING_SNAKE_CASE_:	List[Any]            ="""Wav2Vec2FeatureExtractor"""
   SCREAMING_SNAKE_CASE_:	Tuple            ="""Wav2Vec2CTCTokenizer"""

   return config





@torch.no_grad()
def        __magic_name__			(							lowercase							,     lowercase							,     lowercase=None							,     lowercase=None							,     lowercase=True	):

   if is_finetuned:
      SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Dict            =fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]							,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
   else:
      SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Any            =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

   if config_path is not None:
      SCREAMING_SNAKE_CASE_:	Dict            =SEWDConfig.from_pretrained(lowercase	)
   else:
      SCREAMING_SNAKE_CASE_:	List[str]            =convert_config(model[0]							,     lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            =model[0].eval()

   SCREAMING_SNAKE_CASE_:	Dict            =True if config.feat_extract_norm == """layer""" else False
   SCREAMING_SNAKE_CASE_:	int            =WavaVecaFeatureExtractor(
       feature_size=1							,     sampling_rate=1_6000							,     padding_value=0							,     do_normalize=lowercase							,     return_attention_mask=lowercase							,     )

   if is_finetuned:
      if dict_path:
         SCREAMING_SNAKE_CASE_:	Union[str, Any]            =Dictionary.load(lowercase	)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         SCREAMING_SNAKE_CASE_:	Dict            =target_dict.pad_index
         SCREAMING_SNAKE_CASE_:	Any            =target_dict.bos_index
         SCREAMING_SNAKE_CASE_:	Optional[int]            =target_dict.pad_index
         SCREAMING_SNAKE_CASE_:	Tuple            =target_dict.bos_index
         SCREAMING_SNAKE_CASE_:	List[Any]            =target_dict.eos_index
         SCREAMING_SNAKE_CASE_:	Union[str, Any]            =len(target_dict.symbols	)
         SCREAMING_SNAKE_CASE_:	Optional[Any]            =os.path.join(lowercase							,     """vocab.json"""	)
         if not os.path.isdir(lowercase	):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase	)	)
            return
         os.makedirs(lowercase							,     exist_ok=lowercase	)
         with open(lowercase							,     """w"""							,     encoding="""utf-8"""	) as vocab_handle:
            json.dump(target_dict.indices							,     lowercase	)
         SCREAMING_SNAKE_CASE_:	Any            =WavaVecaCTCTokenizer(
             lowercase							,     unk_token=target_dict.unk_word							,     pad_token=target_dict.pad_word							,     bos_token=target_dict.bos_word							,     eos_token=target_dict.eos_word							,     word_delimiter_token="""|"""							,     do_lower_case=lowercase							,     )
         SCREAMING_SNAKE_CASE_:	Optional[Any]            =WavaVecaProcessor(feature_extractor=lowercase							,     tokenizer=lowercase	)
         processor.save_pretrained(lowercase	)

      SCREAMING_SNAKE_CASE_:	str            =SEWDForCTC(lowercase	)
   else:
      SCREAMING_SNAKE_CASE_:	Optional[int]            =SEWDModel(lowercase	)
      feature_extractor.save_pretrained(lowercase	)

   recursively_load_weights(lowercase							,     lowercase							,     lowercase	)

   hf_model.save_pretrained(lowercase	)


if __name__ == "__main__":
   _UpperCAmelCase       =     argparse.ArgumentParser()
   parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
   parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
   )
   _UpperCAmelCase       =     parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )








"""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	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict=sys.maxsize						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            ="""bilinear"""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =short_edge_length





  def __call__(  self	:   Tuple    ,						lowerCAmelCase	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




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

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

     return img_augs







class    a		:



  def __init__(  self	:   Any    ,						lowerCAmelCase	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   Any:


     '''simple docstring'''




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


     '''simple docstring'''




     with torch.no_grad():
        if not isinstance(lowerCAmelCase    ,						lowerCAmelCase						):
           SCREAMING_SNAKE_CASE_:	Optional[int]            =[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
        SCREAMING_SNAKE_CASE_:	int            =torch.tensor([im.shape[:2] for im in images]						)
        SCREAMING_SNAKE_CASE_:	Dict            =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
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[self.normalizer(lowerCAmelCase						) for x in images]
        # now pad them to do the following operations
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.pad(lowerCAmelCase						)
        # Normalize

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







def        __magic_name__			(							lowercase							,     lowercase	):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes





def        __magic_name__			(							lowercase							,     lowercase	):
   assert torch.isfinite(lowercase	).all(), "Box tensor contains infinite or NaN!"
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =box_size
   tensor[:, 0].clamp_(min=0							,     max=lowercase	)
   tensor[:, 1].clamp_(min=0							,     max=lowercase	)
   tensor[:, 2].clamp_(min=0							,     max=lowercase	)
   tensor[:, 3].clamp_(min=0							,     max=lowercase	)



