


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

import sentencepiece as spm

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


lowerCAmelCase       =							logging.get_logger(__name__)

lowerCAmelCase       =							{'vocab_file': 'spiece.model'}

lowerCAmelCase       =							{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowerCAmelCase       =							{'bert_for_seq_generation': 512}


class        _a							(			UpperCamelCase__			):
							_lowercase	:			List[Any]        = VOCAB_FILES_NAMES
							_lowercase	:			Dict        = PRETRAINED_VOCAB_FILES_MAP
							_lowercase	:			Optional[Any]        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase	:			List[int]        = []
							_lowercase	:			str        = ['''input_ids''', '''attention_mask''']

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





									"""simple docstring"""







									lowercase__            =      {} if sp_model_kwargs is None else sp_model_kwargs

									# Add extra_ids to the special token list
									super().__init__(
									    bos_token=UpperCamelCase_    ,						eos_token=UpperCamelCase_    ,						unk_token=UpperCamelCase_    ,						pad_token=UpperCamelCase_    ,						sep_token=UpperCamelCase_    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**UpperCamelCase_    ,						)

									lowercase__            =      vocab_file

									lowercase__            =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(UpperCamelCase_							)

							@property
							def       lowerCamelCase_ (							self:	Any							)			->   str:





									"""simple docstring"""







									return self.sp_model.get_piece_size()

							def       lowerCamelCase_ (							self:	Any							)			->   int:





									"""simple docstring"""







									lowercase__            =      {self.convert_ids_to_tokens(UpperCamelCase_							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab

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





									"""simple docstring"""







									lowercase__            =      self.__dict__.copy()
									lowercase__            =      None
									return state

							def __setstate__(							self:	Dict    ,						UpperCamelCase_:	Union[str, Any]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      d

									# for backward compatibility
									if not hasattr(self    ,						'''sp_model_kwargs'''							):
											lowercase__            =      {}

									lowercase__            =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(self.vocab_file							)

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	str							)			->   List[str]:





									"""simple docstring"""







									return self.sp_model.encode(UpperCamelCase_    ,						out_type=UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Tuple							)			->   Dict:





									"""simple docstring"""







									return self.sp_model.piece_to_id(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Tuple							)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.sp_model.IdToPiece(UpperCamelCase_							)
									return token

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	Dict							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      []
									lowercase__            =      ''''''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(UpperCamelCase_							) + token
													lowercase__            =      []
											else:
													current_sub_tokens.append(UpperCamelCase_							)
									out_string += self.sp_model.decode(UpperCamelCase_							)
									return out_string.strip()


							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	Optional[str] = None							)			->   Tuple[str]:





									"""simple docstring"""







									if not os.path.isdir(UpperCamelCase_							):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
											return
									lowercase__            =      os.path.join(
									    UpperCamelCase_    ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

									if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase_							) and os.path.isfile(self.vocab_file							):
											copyfile(self.vocab_file    ,						UpperCamelCase_							)
									elif not os.path.isfile(self.vocab_file							):
											with open(UpperCamelCase_    ,						'''wb'''							) as fi:
													lowercase__            =      self.sp_model.serialized_model_proto()
													fi.write(UpperCamelCase_							)

									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, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor


class        _a							(			unittest.TestCase			):

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





									"""simple docstring"""







									lowercase__            =      size if size is not None else {'''shortest_edge''': 18}
									lowercase__            =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      num_channels
									lowercase__            =      image_size
									lowercase__            =      min_resolution
									lowercase__            =      max_resolution
									lowercase__            =      do_resize
									lowercase__            =      size
									lowercase__            =      do_center_crop
									lowercase__            =      crop_size
									lowercase__            =      do_normalize
									lowercase__            =      image_mean
									lowercase__            =      image_std


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





									"""simple docstring"""







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


@require_torch
@require_vision
class        _a							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			List[str]        = LevitImageProcessor if is_vision_available() else None

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





									"""simple docstring"""







									lowercase__            =      LevitImageProcessingTester(self							)

							@property
							def       lowerCamelCase_ (							self:	List[Any]							)			->   Any:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

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





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_mean'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_std'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_normalize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_resize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_center_crop'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''size'''							)							)

							def       lowerCamelCase_ (							self:	str							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      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}							)

									lowercase__            =      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       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	List[str]							)			->   int:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						Image.Image							)

									# Test not batched input
									lowercase__            =      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
									lowercase__            =      image_processing(UpperCamelCase_    ,						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       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random numpy tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						numpify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						np.ndarray							)

									# Test not batched input
									lowercase__            =      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
									lowercase__            =      image_processing(UpperCamelCase_    ,						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       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PyTorch tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						torchify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						torch.Tensor							)

									# Test not batched input
									lowercase__            =      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
									lowercase__            =      image_processing(UpperCamelCase_    ,						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'''],
									    )    ,						)


