






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 DeiTImageProcessor





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def __init__(     self:      Union[str, Any]						,   __A:      int						,   __A:      Any=7						,   __A:      List[Any]=3						,   __A:      List[Any]=18						,   __A:      Optional[Any]=30						,   __A:      Optional[Any]=4_00						,   __A:      Any=True						,   __A:      int=None						,   __A:      Union[str, Any]=True						,   __A:      List[Any]=None						,   __A:      List[str]=True						,   __A:      List[str]=[0.5, 0.5, 0.5]						,   __A:      int=[0.5, 0.5, 0.5]						,   )			-> str:
												_A									=       size if size is not None else {'''height''': 20, '''width''': 20}
												_A									=       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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



							def 					__A       (     self:      Union[str, Any]      )			-> Union[str, Any]:
												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,
												}





@require_torch
@require_vision
class   SCREAMING_SNAKE_CASE (       snake_case     ,		unittest.TestCase    ):





							"""simple docstring"""

							A_       =    DeiTImageProcessor if is_vision_available() else None
							A_       =    True

							def 					__A       (     self:      List[Any]      )			-> Optional[int]:
												_A									=       DeiTImageProcessingTester(self      )

							@property
							def 					__A       (     self:      Union[str, Any]      )			-> Optional[int]:
												return self.image_processor_tester.prepare_image_processor_dict()

							def 					__A       (     self:      Optional[Any]      )			-> Any:
												_A									=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(__A						,   '''do_resize'''      )      )
												self.assertTrue(hasattr(__A						,   '''size'''      )      )
												self.assertTrue(hasattr(__A						,   '''do_center_crop'''      )      )
												self.assertTrue(hasattr(__A						,   '''center_crop'''      )      )
												self.assertTrue(hasattr(__A						,   '''do_normalize'''      )      )
												self.assertTrue(hasattr(__A						,   '''image_mean'''      )      )
												self.assertTrue(hasattr(__A						,   '''image_std'''      )      )

							def 					__A       (     self:      List[str]      )			-> int:
												_A									=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size						,   {'''height''': 20, '''width''': 20}      )
												self.assertEqual(image_processor.crop_size						,   {'''height''': 18, '''width''': 18}      )

												_A									=       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 					__A       (     self:      str      )			-> Dict:
												pass

							def 					__A       (     self:      Union[str, Any]      )			-> str:
												# Initialize image_processing
												_A									=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												_A									=       prepare_image_inputs(self.image_processor_tester						,   equal_resolution=__A      )
												for image in image_inputs:
																	self.assertIsInstance(__A						,   Image.Image      )

												# Test not batched input
												_A									=       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
												_A									=       image_processing(__A						,   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 					__A       (     self:      Any      )			-> int:
												# Initialize image_processing
												_A									=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												_A									=       prepare_image_inputs(self.image_processor_tester						,   equal_resolution=__A						,   numpify=__A      )
												for image in image_inputs:
																	self.assertIsInstance(__A						,   np.ndarray      )

												# Test not batched input
												_A									=       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
												_A									=       image_processing(__A						,   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 					__A       (     self:      Any      )			-> Optional[Any]:
												# Initialize image_processing
												_A									=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												_A									=       prepare_image_inputs(self.image_processor_tester						,   equal_resolution=__A						,   torchify=__A      )
												for image in image_inputs:
																	self.assertIsInstance(__A						,   torch.Tensor      )

												# Test not batched input
												_A									=       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
												_A									=       image_processing(__A						,   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'''],
												    )						,   )











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': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    VOCAB_FILES_NAMES
							A_       =    PRETRAINED_VOCAB_FILES_MAP
							A_       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_       =    ["input_ids", "attention_mask"]
							A_       =    LongformerTokenizer

							def __init__(     self:      List[str]						,   __A:      Dict=None						,   __A:      int=None						,   __A:      Tuple=None						,   __A:      Any="replace"						,   __A:      List[str]="<s>"						,   __A:      int="</s>"						,   __A:      Dict="</s>"						,   __A:      Tuple="<s>"						,   __A:      Optional[int]="<unk>"						,   __A:      Optional[int]="<pad>"						,   __A:      List[Any]="<mask>"						,   __A:      Tuple=False						,   __A:      Dict=True						,   **__A:      List[Any]						,   )			-> List[Any]:
												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						,   )

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

												_A									=       add_prefix_space

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

																	_A									=       False

																	if state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																						_A									=       add_prefix_space
																						_A									=       True

																	if state.get('''trim_offsets'''						,   __A      ) != trim_offsets:
																						_A									=       trim_offsets
																						_A									=       True

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

							@property
							def 					__A       (     self:      Union[str, Any]      )			-> str:
												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 					__A       (     self:      Optional[int]						,   __A:      str      )			-> Union[str, Any]:
												_A									=       AddedToken(__A						,   lstrip=__A						,   rstrip=__A      ) if isinstance(__A						,   __A      ) else value
												_A									=       value

							def 					__A       (     self:      Optional[Any]						,   *__A:      Any						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       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 					__A       (     self:      Optional[Any]						,   *__A:      List[str]						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       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 					__A       (     self:      List[str]						,   __A:      str						,   __A:      Optional[str] = None      )			-> Tuple[str]:
												_A									=       self._tokenizer.model.save(__A						,   name=__A      )
												return tuple(__A      )

							def 					__A       (     self:      Tuple						,   __A:      Dict						,   __A:      Optional[Any]=None      )			-> Optional[int]:
												_A									=       [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 					__A       (     self:      Optional[int]						,   __A:      List[int]						,   __A:      Optional[List[int]] = None      )			-> List[int]:
												_A									=       [self.sep_token_id]
												_A									=       [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]



