



"""simple docstring"""





import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  lowerCamelCase  			=		None


lowerCamelCase  			=		logging.get_logger(__name__)

lowerCamelCase  			=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase  			=		{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase  			=		{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	VOCAB_FILES_NAMES
       UpperCamelCase		=	PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase		=	['''input_ids''', '''attention_mask''']
       UpperCamelCase		=	TaTokenizer

       UpperCamelCase		=	[]



       def __init__(				self     :    int						,							_UpperCAmelCase     :    List[str]=None						,							_UpperCAmelCase     :    str=None						,							_UpperCAmelCase     :    Tuple="</s>"						,							_UpperCAmelCase     :    str="<unk>"						,							_UpperCAmelCase     :    List[Any]="<pad>"						,							_UpperCAmelCase     :    List[str]=100						,							_UpperCAmelCase     :    str=None						,							**_UpperCAmelCase     :    List[str]						,							)					->		List[str]:


             '''simple docstring'''




             if extra_ids > 0 and additional_special_tokens is None:
                   UpperCAmelCase_     					=				[F"""<extra_id_{i}>""" for i in range(_UpperCAmelCase       )]
             elif extra_ids > 0 and additional_special_tokens is not None:
                   # Check that we have the right number of extra special tokens
                   UpperCAmelCase_     					=				len(set(filter(lambda _UpperCAmelCase       : bool("extra_id_" in str(_UpperCAmelCase       )       )						,							_UpperCAmelCase       )       )       )
                   if extra_tokens != extra_ids:
                         raise ValueError(
                             F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                             " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                             " tokens"       )

             super().__init__(
                 _UpperCAmelCase						,							tokenizer_file=_UpperCAmelCase						,							eos_token=_UpperCAmelCase						,							unk_token=_UpperCAmelCase						,							pad_token=_UpperCAmelCase						,							extra_ids=_UpperCAmelCase						,							additional_special_tokens=_UpperCAmelCase						,							**_UpperCAmelCase						,							)

             UpperCAmelCase_     					=				vocab_file
             UpperCAmelCase_     					=				False if not self.vocab_file else True
             UpperCAmelCase_     					=				extra_ids



       @staticmethod
       def 	lowercase__							(				_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    Any       )					->		List[Any]:


             '''simple docstring'''




             if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                   UpperCAmelCase_     					=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                   if init_max_model_length is not None and init_max_model_length != max_model_length:
                         return init_max_model_length
                   elif init_max_model_length is None:
                         warnings.warn(
                             "This tokenizer was incorrectly instantiated with a model max length of"
                             F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                             " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                             " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                             F""" {pretrained_model_name_or_path} automatically truncating your input to"""
                             F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                             F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                             " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                             " instantiate this tokenizer with `model_max_length` set to your preferred value."						,							_UpperCAmelCase						,							)

             return max_model_length



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Optional[str] = None       )					->		Tuple[str]:


             '''simple docstring'''




             if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                       "tokenizer."       )

             if not os.path.isdir(_UpperCAmelCase       ):
                   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                   return
             UpperCAmelCase_     					=				os.path.join(
                 _UpperCAmelCase						,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCAmelCase       ):
                   copyfile(self.vocab_file						,							_UpperCAmelCase       )
                   logger.info(F"""Copy vocab file to {out_vocab_file}"""       )

             return (out_vocab_file,)



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    List[int]						,							_UpperCAmelCase     :    Optional[List[int]] = None       )					->		List[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a
             else:
                   UpperCAmelCase_     					=				token_ids_a + [self.eos_token_id]
                   return self.prefix_tokens + token_ids_a + token_ids_a



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    List[int]						,							_UpperCAmelCase     :    Optional[List[int]] = None       )					->		List[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				[self.eos_token_id]

             if token_ids_a is None:
                   return len(token_ids_a + eos       ) * [0]
             return len(token_ids_a + eos + token_ids_a + eos       ) * [0]



       def 	lowercase__							(				self     :    Optional[Any]       )					->		List[str]:


             '''simple docstring'''




             return list(
                 set(filter(lambda _UpperCAmelCase       : bool(re.search(r"<extra_id_\d+>"						,							_UpperCAmelCase       )       ) is not None						,							self.additional_special_tokens       )       )       )







       def 	lowercase__							(				self     :    int       )					->		Any:


             '''simple docstring'''




             return [self.convert_tokens_to_ids(_UpperCAmelCase       ) for token in self.get_sentinel_tokens()]







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     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=sys.maxsize   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							"""bilinear"""
              __snake_case       	=							max_size
              __snake_case       	=							short_edge_length






       def __call__(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


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

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

              return img_augs






class     __snake_case    :
       """simple docstring"""







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






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              __snake_case       	=							tuple(max(_UpperCamelCase   ) for s in zip(*[img.shape for img in images]   )   )
              __snake_case       	=							[im.shape[-2:] for im in images]
              __snake_case       	=							[
                  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      , _UpperCamelCase      , _UpperCamelCase=False   )      ->							Union[str, Any]:






              """simple docstring"""


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

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







def 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :List[Any]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Tuple	,__A    :Tuple[int, int]		):


       """simple docstring"""




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

