





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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase :  Optional[Any]										=  logging.get_logger(__name__)

__lowerCamelCase :  Optional[Any]										=  {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

__lowerCamelCase :  str										=  {
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

__lowerCamelCase :  Dict										=  {
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}



def 	A_       (  _lowerCAmelCase  )		->				str:
 UpperCamelCase			:      str 			=     set()
 UpperCamelCase			:      List[str] 			=     word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)  )
  UpperCamelCase			:      int 			=     char

 UpperCamelCase			:      List[str] 			=     set(_lowerCAmelCase  )
 return pairs



class       A__    ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:str							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:int							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





      def __init__(				self ,							A_ ,							A_ ,							A_="<s>" ,							A_="</s>" ,							A_="</s>" ,							A_="<s>" ,							A_="<unk>" ,							A_="<pad>" ,							A_="<mask>" ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							sep_token=A_ ,							cls_token=A_ ,							pad_token=A_ ,							mask_token=A_ ,							**A_ ,							)

       UpperCamelCase			:      Any 			=     vocab_file
       UpperCamelCase			:      Tuple 			=     merges_file

       UpperCamelCase			:      str 			=     {}
       UpperCamelCase			:      Union[str, Any] 			=     0
       UpperCamelCase			:      Dict 			=     1
       UpperCamelCase			:      Dict 			=     2
       UpperCamelCase			:      Optional[Any] 			=     3

       self.add_from_file(A_     )

       UpperCamelCase			:      str 			=     {v: k for k, v in self.encoder.items()}

       with open(A_ ,							encoding="utf-8"     ) as merges_handle:
        UpperCamelCase			:      Optional[Any] 			=     merges_handle.read().split("\n"     )[:-1]
       UpperCamelCase			:      Union[str, Any] 			=     [tuple(merge.split()[:-1]     ) for merge in merges]
       UpperCamelCase			:      List[Any] 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )
       UpperCamelCase			:      Union[str, Any] 			=     {}





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase			:      Optional[int] 			=     [self.cls_token_id]
       UpperCamelCase			:      str 			=     [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							A_ = False     ):
       '''simple docstring'''




       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=A_ ,							token_ids_a=A_ ,							already_has_special_tokens=A_     )

       if token_ids_a is None:
        return [1] + ([0] * len(A_     )) + [1]
       return [1] + ([0] * len(A_     )) + [1, 1] + ([0] * len(A_     )) + [1]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      Any 			=     [self.cls_token_id]

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





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return len(self.encoder     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return dict(self.encoder ,							**self.added_tokens_encoder     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if token in self.cache:
        return self.cache[token]
       UpperCamelCase			:      Tuple 			=     tuple(A_     )
       UpperCamelCase			:      List[Any] 			=     tuple(list(word[:-1]     ) + [word[-1] + "</w>"]     )
       UpperCamelCase			:      str 			=     get_pairs(A_     )

       if not pairs:
        return token

       while True:
        UpperCamelCase			:      Any 			=     min(A_ ,							key=lambda A_     : self.bpe_ranks.get(A_ ,							float("inf"     )     )     )
        if bigram not in self.bpe_ranks:
         break
        UpperCamelCase			:      Any 			=     bigram
        UpperCamelCase			:      Optional[Any] 			=     []
        UpperCamelCase			:      List[Any] 			=     0
        while i < len(A_     ):
         try:
          UpperCamelCase			:      Tuple 			=     word.index(A_ ,							A_     )
         except ValueError:
          new_word.extend(word[i:]     )
          break
         else:
          new_word.extend(word[i:j]     )
          UpperCamelCase			:      int 			=     j

         if word[i] == first and i < len(A_     ) - 1 and word[i + 1] == second:
          new_word.append(first + second     )
          i += 2
         else:
          new_word.append(word[i]     )
          i += 1
        UpperCamelCase			:      int 			=     tuple(A_     )
        UpperCamelCase			:      Optional[int] 			=     new_word
        if len(A_     ) == 1:
         break
        else:
         UpperCamelCase			:      Optional[Any] 			=     get_pairs(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     "@@ ".join(A_     )
       UpperCamelCase			:      str 			=     word[:-4]
       UpperCamelCase			:      str 			=     word
       return word





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     []

       UpperCamelCase			:      Optional[Any] 			=     re.findall(R"\S+\n?" ,							A_     )

       for token in words:
        split_tokens.extend(list(self.bpe(A_     ).split(" "     )     )     )
       return split_tokens





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return self.encoder.get(A_ ,							self.encoder.get(self.unk_token     )     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return self.decoder.get(A_ ,							self.unk_token     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     " ".join(A_     ).replace("@@ " ,							""     ).strip()
       return out_string





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       if not os.path.isdir(A_     ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
        return
       UpperCamelCase			:      Dict 			=     os.path.join(
           A_ ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      Union[str, Any] 			=     os.path.join(
           A_ ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

       if os.path.abspath(self.vocab_file     ) != os.path.abspath(A_     ):
        copyfile(self.vocab_file ,							A_     )

       if os.path.abspath(self.merges_file     ) != os.path.abspath(A_     ):
        copyfile(self.merges_file ,							A_     )

       return out_vocab_file, out_merge_file





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if isinstance(A_ ,							A_     ):
        try:
         with open(A_ ,							"r" ,							encoding="utf-8"     ) as fd:
          self.add_from_file(A_     )
        except FileNotFoundError as fnfe:
         raise fnfe
        except UnicodeError:
         raise Exception(F"""Incorrect encoding detected in {f}, please rebuild the dataset"""     )
        return

       UpperCamelCase			:      Optional[int] 			=     f.readlines()
       for lineTmp in lines:
        UpperCamelCase			:      str 			=     lineTmp.strip()
        UpperCamelCase			:      Optional[Any] 			=     line.rfind(" "     )
        if idx == -1:
         raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"     )
        UpperCamelCase			:      List[Any] 			=     line[:idx]
        UpperCamelCase			:      int 			=     len(self.encoder     )







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor



class       A__     ( unittest.TestCase    ):





      def __init__(				self ,							A_ ,							A_=7 ,							A_=3 ,							A_=10 ,							A_=18 ,							A_=30 ,							A_=400 ,							A_=True ,							A_=None ,							A_=True ,							A_=[0.5, 0.5, 0.5] ,							A_=[0.5, 0.5, 0.5] ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     size if size is not None else {"shortest_edge": 18}
       UpperCamelCase			:      Tuple 			=     crop_size if crop_size is not None else {"height": 18, "width": 18}

       UpperCamelCase			:      Optional[Any] 			=     parent
       UpperCamelCase			:      Optional[int] 			=     batch_size
       UpperCamelCase			:      List[Any] 			=     num_channels
       UpperCamelCase			:      Union[str, Any] 			=     num_frames
       UpperCamelCase			:      Any 			=     image_size
       UpperCamelCase			:      Tuple 			=     min_resolution
       UpperCamelCase			:      Optional[Any] 			=     max_resolution
       UpperCamelCase			:      Any 			=     do_resize
       UpperCamelCase			:      Tuple 			=     size
       UpperCamelCase			:      List[Any] 			=     do_normalize
       UpperCamelCase			:      Optional[int] 			=     image_mean
       UpperCamelCase			:      Any 			=     image_std
       UpperCamelCase			:      str 			=     crop_size





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }



@require_torch
@require_vision
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:List[str]							=     VivitImageProcessor if is_vision_available() else None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     VivitImageProcessingTester(self     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       self.assertTrue(hasattr(A_ ,							"image_mean"     )     )
       self.assertTrue(hasattr(A_ ,							"image_std"     )     )
       self.assertTrue(hasattr(A_ ,							"do_normalize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_resize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_center_crop"     )     )
       self.assertTrue(hasattr(A_ ,							"size"     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 18}     )
       self.assertEqual(image_processor.crop_size ,							{"height": 18, "width": 18}     )

       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict ,							size=42 ,							crop_size=84     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 42}     )
       self.assertEqual(image_processor.crop_size ,							{"height": 84, "width": 84}     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PIL videos
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							Image.Image     )

       # Test not batched input
       UpperCamelCase			:      Any 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      str 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       UpperCamelCase			:      str 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							numpify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							np.ndarray     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      Any 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							torchify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							torch.Tensor     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      List[Any] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)
