





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

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


if is_sentencepiece_available():
      from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
      SCREAMING_SNAKE_CASE_		  =			None


SCREAMING_SNAKE_CASE_		  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		  =			{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_		  =			{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE_		  =			{
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      str						      =     VOCAB_FILES_NAMES
     __snake_case       :      int						      =     PRETRAINED_VOCAB_FILES_MAP
     __snake_case       :      List[str]						      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case       :      Dict						      =     ["input_ids", "attention_mask"]
     __snake_case       :      Union[str, Any]						      =     XLMRobertaTokenizer






     def __init__(				self		:       List[Any]		,lowerCamelCase__		:       Optional[int]=None		,lowerCamelCase__		:       str=None		,lowerCamelCase__		:       List[Any]="<s>"		,lowerCamelCase__		:       str="</s>"		,lowerCamelCase__		:       List[str]="</s>"		,lowerCamelCase__		:       Optional[int]="<s>"		,lowerCamelCase__		:       List[Any]="<unk>"		,lowerCamelCase__		:       Any="<pad>"		,lowerCamelCase__		:       Any="<mask>"		,**lowerCamelCase__		:       Union[str, Any]		,)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__		,lowerCamelCase__			) else mask_token

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

           SCREAMING_SNAKE_CASE						=		vocab_file
           SCREAMING_SNAKE_CASE						=		False if not self.vocab_file else True






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




           '''simple docstring'''


           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           SCREAMING_SNAKE_CASE						=		[self.cls_token_id]
           SCREAMING_SNAKE_CASE						=		[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		[self.sep_token_id]
           SCREAMING_SNAKE_CASE						=		[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]






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]		,lowerCamelCase__		:       str		,lowerCamelCase__		:       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(lowerCamelCase__			):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""			)
                 return
           SCREAMING_SNAKE_CASE						=		os.path.join(
               lowerCamelCase__		,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

           return (out_vocab_file,)









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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=10					,		_SCREAMING_SNAKE_CASE=10					,		_SCREAMING_SNAKE_CASE=False					,		_SCREAMING_SNAKE_CASE=False      )					->  Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		[]
      for i in range(image_processor_tester.num_frames      ):
            video.append(np.random.randint(2_55					,		size=(image_processor_tester.num_channels, width, height)					,		dtype=np.uinta      )      )

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            SCREAMING_SNAKE_CASE						=		[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE					,		0					,		-1      )      ) for frame in video]

      if torchify:
            SCREAMING_SNAKE_CASE						=		[torch.from_numpy(_SCREAMING_SNAKE_CASE      ) for frame in video]

      return video



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=False					,		_SCREAMING_SNAKE_CASE=False					,		_SCREAMING_SNAKE_CASE=False      )					->  Tuple:


      '''simple docstring'''


      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      SCREAMING_SNAKE_CASE						=		[]
      for i in range(image_processor_tester.batch_size      ):
            if equal_resolution:
                  SCREAMING_SNAKE_CASE						=		SCREAMING_SNAKE_CASE						=		image_processor_tester.max_resolution
            else:
                  SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		np.random.choice(
                      np.arange(image_processor_tester.min_resolution					,		image_processor_tester.max_resolution      )					,		2      )
                  SCREAMING_SNAKE_CASE						=		prepare_video(
                      image_processor_tester=_SCREAMING_SNAKE_CASE					,		width=_SCREAMING_SNAKE_CASE					,		height=_SCREAMING_SNAKE_CASE					,		numpify=_SCREAMING_SNAKE_CASE					,		torchify=_SCREAMING_SNAKE_CASE					,		)
            video_inputs.append(_SCREAMING_SNAKE_CASE      )

      return video_inputs

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def __init__(				self		:       Union[str, Any]		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       Any=7		,lowerCamelCase__		:       str=3		,lowerCamelCase__		:       Union[str, Any]=4		,lowerCamelCase__		:       Union[str, Any]=18		,lowerCamelCase__		:       Any=30		,lowerCamelCase__		:       Union[str, Any]=400		,lowerCamelCase__		:       Any=True		,lowerCamelCase__		:       Any=None		,lowerCamelCase__		:       Tuple=True		,lowerCamelCase__		:       int=[0.5, 0.5, 0.5]		,lowerCamelCase__		:       Any=[0.5, 0.5, 0.5]		,lowerCamelCase__		:       Dict=True		,lowerCamelCase__		:       Tuple=None		,)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		size if size is not None else {"""shortest_edge""": 18}
           SCREAMING_SNAKE_CASE						=		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		batch_size
           SCREAMING_SNAKE_CASE						=		num_channels
           SCREAMING_SNAKE_CASE						=		num_frames
           SCREAMING_SNAKE_CASE						=		image_size
           SCREAMING_SNAKE_CASE						=		min_resolution
           SCREAMING_SNAKE_CASE						=		max_resolution
           SCREAMING_SNAKE_CASE						=		do_resize
           SCREAMING_SNAKE_CASE						=		size
           SCREAMING_SNAKE_CASE						=		do_normalize
           SCREAMING_SNAKE_CASE						=		image_mean
           SCREAMING_SNAKE_CASE						=		image_std
           SCREAMING_SNAKE_CASE						=		do_center_crop
           SCREAMING_SNAKE_CASE						=		crop_size






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						int:




           '''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,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }

@require_torch
@require_vision
class 						UpperCamelCase__					( lowerCAmelCase_				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      int						      =     TvltImageProcessor if is_vision_available() else None






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TvltImageProcessorTester(self			)






     @property
     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Dict:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""image_mean"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""image_std"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_normalize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_resize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_center_crop"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""size"""			)			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_processing_class(**self.image_processor_dict			)
           # create random PIL videos
           SCREAMING_SNAKE_CASE						=		prepare_video_inputs(self.image_processor_tester		,equal_resolution=lowerCamelCase__			)
           for video in video_inputs:
                 self.assertIsInstance(lowerCamelCase__		,lowerCamelCase__			)
                 self.assertIsInstance(video[0]		,Image.Image			)

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processor(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
           SCREAMING_SNAKE_CASE						=		image_processor(lowerCamelCase__		,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 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						int:




           '''simple docstring'''


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

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processor(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
           SCREAMING_SNAKE_CASE						=		image_processor(lowerCamelCase__		,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 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Optional[Any]:




           '''simple docstring'''


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

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processor(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
           SCREAMING_SNAKE_CASE						=		image_processor(lowerCamelCase__		,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"""],
               )		,)


