'''simple docstring'''




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 						__snake_case (       lowerCAmelCase       :     Union[str, Any]      ,  lowerCAmelCase       :     str=10      ,  lowerCAmelCase       :     List[Any]=10      ,  lowerCAmelCase       :     Union[str, Any]=False      ,  lowerCAmelCase       :     Dict=False			):
	__UpperCAmelCase         =					[]
	for i in range(image_processor_tester.num_frames			):
		video.append(np.random.randint(255      ,  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
		__UpperCAmelCase         =					[Image.fromarray(np.moveaxis(lowercase__      ,  0      ,  -1			)			) for frame in video]

	if torchify:
		__UpperCAmelCase         =					[torch.from_numpy(lowercase__			) for frame in video]

	return video



def 						__snake_case (       lowerCAmelCase       :     int      ,  lowerCAmelCase       :     int=False      ,  lowerCAmelCase       :     str=False      ,  lowerCAmelCase       :     int=False			):
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

	return video_inputs

class   _lowercase(     unittest.TestCase				):



			"""simple docstring"""





			def __init__(							self:    Dict			,a:    Any			,a:    Union[str, Any]=7			,a:    List[str]=3			,a:    Dict=4			,a:    List[Any]=18			,a:    List[Any]=30			,a:    Dict=400			,a:    Any=True			,a:    str=None			,a:    Optional[Any]=True			,a:    Tuple=[0.5, 0.5, 0.5]			,a:    Dict=[0.5, 0.5, 0.5]			,a:    int=True			,a:    str=None			,):
				__UpperCAmelCase         =					size if size is not None else {'shortest_edge': 18}
				__UpperCAmelCase         =					crop_size if crop_size is not None else {'height': 18, 'width': 18}

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





			def 			snake_case	(							self:    List[Any]	):
				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   _lowercase(     A_   ,unittest.TestCase				):



			"""simple docstring"""





			__lowerCamelCase    =  TvltImageProcessor if is_vision_available() else None





			def 			snake_case	(							self:    List[str]	):
				__UpperCAmelCase         =					TvltImageProcessorTester(self	)





			@property
			def 			snake_case	(							self:    Optional[Any]	):
				return self.image_processor_tester.prepare_image_processor_dict()





			def 			snake_case	(							self:    Union[str, Any]	):
				__UpperCAmelCase         =					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 			snake_case	(							self:    int	):
				# Initialize image_processor
				__UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict	)
				# create random PIL videos
				__UpperCAmelCase         =					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         =					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
				__UpperCAmelCase         =					image_processor(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 			snake_case	(							self:    Dict	):
				# Initialize image_processor
				__UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				__UpperCAmelCase         =					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         =					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
				__UpperCAmelCase         =					image_processor(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 			snake_case	(							self:    List[str]	):
				# Initialize image_processor
				__UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				__UpperCAmelCase         =					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         =					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
				__UpperCAmelCase         =					image_processor(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'],
				    )			,)




'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_UpperCamelCase				:		int       			=		logging.get_logger(__name__)

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

_UpperCamelCase				:		int       			=		{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase				:		List[str]       			=		{
    'allenai/longformer-base-4096': 40_96,
    'allenai/longformer-large-4096': 40_96,
    'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
    'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
    'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  VOCAB_FILES_NAMES
   __lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
   __lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCamelCase    =  ['''input_ids''', '''attention_mask''']
   __lowerCamelCase    =  LongformerTokenizer





   def __init__(							self:    Any			,a:    Optional[int]=None			,a:    Optional[int]=None			,a:    int=None			,a:    Any="replace"			,a:    Optional[int]="<s>"			,a:    Any="</s>"			,a:    Optional[int]="</s>"			,a:    Dict="<s>"			,a:    Dict="<unk>"			,a:    int="<pad>"			,a:    List[str]="<mask>"			,a:    Union[str, Any]=False			,a:    Any=True			,**a:    str			,):
    super().__init__(
        a			,a			,tokenizer_file=a			,errors=a			,bos_token=a			,eos_token=a			,sep_token=a			,cls_token=a			,unk_token=a			,pad_token=a			,mask_token=a			,add_prefix_space=a			,trim_offsets=a			,**a			,)

    __UpperCAmelCase         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
    if pre_tok_state.get('add_prefix_space'			,a	) != add_prefix_space:
     __UpperCAmelCase         =					getattr(a			,pre_tok_state.pop('type'	)	)
     __UpperCAmelCase         =					add_prefix_space
     __UpperCAmelCase         =					pre_tok_class(**a	)

    __UpperCAmelCase         =					add_prefix_space

    __UpperCAmelCase         =					'post_processor'
    __UpperCAmelCase         =					getattr(self.backend_tokenizer			,a			,a	)
    if tokenizer_component_instance:
     __UpperCAmelCase         =					json.loads(tokenizer_component_instance.__getstate__()	)

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
      __UpperCAmelCase         =					tuple(state['sep']	)
     if "cls" in state:
      __UpperCAmelCase         =					tuple(state['cls']	)

     __UpperCAmelCase         =					False

     if state.get('add_prefix_space'			,a	) != add_prefix_space:
      __UpperCAmelCase         =					add_prefix_space
      __UpperCAmelCase         =					True

     if state.get('trim_offsets'			,a	) != trim_offsets:
      __UpperCAmelCase         =					trim_offsets
      __UpperCAmelCase         =					True

     if changes_to_apply:
      __UpperCAmelCase         =					getattr(a			,state.pop('type'	)	)
      __UpperCAmelCase         =					component_class(**a	)
      setattr(self.backend_tokenizer			,a			,a	)





   @property
   def 			snake_case	(							self:    List[Any]	):
    if self._mask_token is None:
     if self.verbose:
      logger.error('Using mask_token, but it is not set yet.'	)
     return None
    return str(self._mask_token	)





   @mask_token.setter
   def 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase         =					AddedToken(a			,lstrip=a			,rstrip=a	) if isinstance(a			,a	) else value
    __UpperCAmelCase         =					value





   def 			snake_case	(							self:    Optional[int]			,*a:    Dict			,**a:    Dict	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)
    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._batch_encode_plus(*a			,**a	)





   def 			snake_case	(							self:    str			,*a:    Dict			,**a:    str	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)

    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._encode_plus(*a			,**a	)





   def 			snake_case	(							self:    List[str]			,a:    str			,a:    Optional[str] = None	):
    __UpperCAmelCase         =					self._tokenizer.model.save(a			,name=a	)
    return tuple(a	)





   def 			snake_case	(							self:    Tuple			,a:    Dict			,a:    Tuple=None	):
    __UpperCAmelCase         =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
     return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def 			snake_case	(							self:    int			,a:    List[int]			,a:    Optional[List[int]] = None	):
    __UpperCAmelCase         =					[self.sep_token_id]
    __UpperCAmelCase         =					[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]



