from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


_lowerCAmelCase        =       logging.get_logger(__name__)

_lowerCAmelCase        =       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase        =       {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

_lowerCAmelCase        =       {
    'openai/clip-vit-base-patch32': 7_7,
}






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   VOCAB_FILES_NAMES
 __magic_name__          =   PRETRAINED_VOCAB_FILES_MAP
 __magic_name__          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__          =   ["""input_ids""", """attention_mask"""]
 __magic_name__          =   CLIPTokenizer
 def __init__(    self ,       __magic_name__=None ,       __magic_name__=None ,       __magic_name__=None ,       __magic_name__="<|endoftext|>" ,       __magic_name__="<|startoftext|>" ,       __magic_name__="<|endoftext|>" ,       __magic_name__="<|endoftext|>" ,       **__magic_name__ ,       ):

        """simple docstring"""

        super().__init__(
            lowerCAmelCase_ ,       lowerCAmelCase_ ,       tokenizer_file=lowerCAmelCase_ ,       unk_token=lowerCAmelCase_ ,       bos_token=lowerCAmelCase_ ,       eos_token=lowerCAmelCase_ ,       pad_token=lowerCAmelCase_ ,       **lowerCAmelCase_ ,       )

        if not isinstance(self.backend_tokenizer.pre_tokenizer ,       pre_tokenizers.Sequence      ):
               raise ValueError(
                   '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
                   ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
                   ''' to be compatible with this version.The easiest way to do so is'''
                   ''' `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
                   ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
                   ''' transformers.'''      )

        self._wrap_decode_method_backend_tokenizer()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				self.backend_tokenizer.decode

        def new_decode_method(*__magic_name__ ,       **__magic_name__      ):
               A_							:       Optional[Any]	      =				orig_decode_method(*lowerCAmelCase_ ,       **lowerCAmelCase_      )
               A_							:       List[Any]	      =				text.replace(self.backend_tokenizer.model.end_of_word_suffix ,       ''' '''      ).strip()
               return text

        A_							:       Dict	      =				new_decode_method
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ = None      ):

        """simple docstring"""

        A_							:       List[str]	      =				[self.bos_token_id]
        A_							:       int	      =				[self.eos_token_id]

        if token_ids_a is None:
               return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ = None      ):

        """simple docstring"""

        A_							:       List[Any]	      =				[self.bos_token_id]
        A_							:       Optional[int]	      =				[self.eos_token_id]

        if token_ids_a is None:
               return len(bos_token + token_ids_a + eos_token      ) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token      ) * [0]
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ = None      ):

        """simple docstring"""

        A_							:       str	      =				self._tokenizer.model.save(lowerCAmelCase_ ,       name=lowerCAmelCase_      )
        return tuple(lowerCAmelCase_      )




'''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	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]=sys.maxsize     ) ->							Tuple:
        snake_case_    							=				"bilinear"
        snake_case_    							=				max_size
        snake_case_    							=				short_edge_length


  def __call__(self	:			int						,			lowerCAmelCase_	:			Tuple     ) ->							int:
        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(lowerCAmelCase_						,			lowerCAmelCase_     )
              if h < w:
                    snake_case_							,						snake_case_    							=				size, scale * w
              else:
                    snake_case_							,						snake_case_    							=				scale * h, size
              if max(lowerCAmelCase_						,			lowerCAmelCase_     ) > self.max_size:
                    snake_case_    							=				self.max_size * 1.0 / max(lowerCAmelCase_						,			lowerCAmelCase_     )
                    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(lowerCAmelCase_     )
                    snake_case_    							=				pil_image.resize((neww, newh)						,			PILImageResampling.BILINEAR     )
                    snake_case_    							=				np.asarray(lowerCAmelCase_     )
              else:
                    snake_case_    							=				img.permute(2						,			0						,			1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
                    snake_case_    							=				nn.functional.interpolate(
                        lowerCAmelCase_						,			(newh, neww)						,			mode=self.interp_method						,			align_corners=lowerCAmelCase_     ).squeeze(0     )
              img_augs.append(lowerCAmelCase_     )

        return img_augs



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[Any]     ) ->							int:
        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 lowerCAmelCase_     : (x - self.pixel_mean) / self.pixel_std

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple     ) ->							Any:
        snake_case_    							=				tuple(max(lowerCAmelCase_     ) for s in zip(*[img.shape for img in images]     )     )
        snake_case_    							=				[im.shape[-2:] for im in images]
        snake_case_    							=				[
            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	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]=False     ) ->							int:
        with torch.no_grad():
              if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				[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
              snake_case_    							=				torch.tensor([im.shape[:2] for im in images]     )
              snake_case_    							=				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
              snake_case_    							=				[self.normalizer(lowerCAmelCase_     ) for x in images]
              # now pad them to do the following operations
              snake_case_							,						snake_case_    							=				self.pad(lowerCAmelCase_     )
              # Normalize

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

def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[Any]      ):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes




def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Tuple[int, int]      ):
      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      )

