




"""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


a								=							logging.get_logger(__name__)

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

a								=							{
    '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'
        ),
    },
}

a								=							{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  VOCAB_FILES_NAMES
						_a        =  PRETRAINED_VOCAB_FILES_MAP
						_a        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a        =  ['input_ids', 'attention_mask']
						_a        =  LongformerTokenizer


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

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

										lowerCAmelCase		=			add_prefix_space

										lowerCAmelCase		=			"""post_processor"""
										lowerCAmelCase		=			getattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )
										if tokenizer_component_instance:
														lowerCAmelCase		=			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:
																		lowerCAmelCase		=			tuple(state["""sep"""]   )
														if "cls" in state:
																		lowerCAmelCase		=			tuple(state["""cls"""]   )

														lowerCAmelCase		=			False

														if state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
																		lowerCAmelCase		=			add_prefix_space
																		lowerCAmelCase		=			True

														if state.get("""trim_offsets"""  ,  lowerCAmelCase   ) != trim_offsets:
																		lowerCAmelCase		=			trim_offsets
																		lowerCAmelCase		=			True

														if changes_to_apply:
																		lowerCAmelCase		=			getattr(lowerCAmelCase  ,  state.pop("""type"""   )   )
																		lowerCAmelCase		=			component_class(**lowerCAmelCase   )
																		setattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )


						@property
						def 					__lowercase	(							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 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			AddedToken(lowerCAmelCase  ,  lstrip=lowerCAmelCase  ,  rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase  ,  lowerCAmelCase   ) else value
										lowerCAmelCase		=			value


						def 					__lowercase	(							self			:     Tuple  ,  *lowerCAmelCase			:     Optional[int]  ,  **lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )
										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(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     str   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )

										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(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Optional[str] = None   ):
										lowerCAmelCase		=			self._tokenizer.model.save(lowerCAmelCase  ,  name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[int]=None   ):
										lowerCAmelCase		=			[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 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     List[int]  ,  lowerCAmelCase			:     Optional[List[int]] = None   ):
										lowerCAmelCase		=			[self.sep_token_id]
										lowerCAmelCase		=			[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]











"""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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor






class  SCREAMING_SNAKE_CASE__   (   unittest.TestCase   ):


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[Any]=7  ,  lowerCAmelCase			:     str=3  ,  lowerCAmelCase			:     str=18  ,  lowerCAmelCase			:     Tuple=30  ,  lowerCAmelCase			:     Union[str, Any]=400  ,  lowerCAmelCase			:     List[str]=True  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     int=True  ,  lowerCAmelCase			:     int=None  ,  lowerCAmelCase			:     Optional[int]=True  ,  lowerCAmelCase			:     Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,  lowerCAmelCase			:     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,  lowerCAmelCase			:     Any=True  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_channels
										lowerCAmelCase		=			image_size
										lowerCAmelCase		=			min_resolution
										lowerCAmelCase		=			max_resolution
										lowerCAmelCase		=			do_resize
										lowerCAmelCase		=			size if size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_center_crop
										lowerCAmelCase		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_normalize
										lowerCAmelCase		=			image_mean
										lowerCAmelCase		=			image_std
										lowerCAmelCase		=			do_convert_rgb




						def 					__lowercase	(							self			:     Tuple   ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__   (   _a  ,			unittest.TestCase   ):
						_a        =  OwlViTImageProcessor if is_vision_available() else None


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			OwlViTImageProcessingTester(self   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										return self.image_processor_tester.prepare_image_processor_dict()


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_resize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """size"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_normalize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_mean"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_std"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_convert_rgb"""   )   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 18, """width""": 18}   )

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


						def 					__lowercase	(							self			:     str   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase   )

										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  Image.Image   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )


						def 					__lowercase	(							self			:     Tuple   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  numpify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  np.ndarray   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )




						def 					__lowercase	(							self			:     Optional[int]   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  torchify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  torch.Tensor   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )





