
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


_A						      =				logging.get_logger(__name__)

_A						      =				{"vocab_file": "spiece.model"}

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

_A						      =				{"bert_for_seq_generation": 5_12}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Optional[Any]     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							str     =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							Any     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							List[int]     =	[]
     UpperCAmelCase__       :							List[str]     =	["input_ids", "attention_mask"]





     def __init__(self   :					Dict       ,					_A   :					Dict       ,					_A   :					Optional[Any]="<s>"       ,					_A   :					Tuple="</s>"       ,					_A   :					Union[str, Any]="<unk>"       ,					_A   :					str="<pad>"       ,					_A   :					int="<::::>"       ,					_A   :					Optional[Dict[str, Any]] = None       ,					**_A   :					Union[str, Any]       ,					)		->		None:
         snake_case				  =      {} if sp_model_kwargs is None else sp_model_kwargs

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

         snake_case				  =      vocab_file

         snake_case				  =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(_A     )





     @property
     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         return self.sp_model.get_piece_size()





     def 	UpperCAmelCase(self   :					int     )		->		Union[str, Any]:
         snake_case				  =      {self.convert_ids_to_tokens(_A     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab





     def __getstate__(self   :					List[Any]     )		->		int:
         snake_case				  =      self.__dict__.copy()
         snake_case				  =      None
         return state





     def __setstate__(self   :					Optional[int]       ,					_A   :					str     )		->		int:
         snake_case				  =      d

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

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





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					str     )		->		List[str]:
         return self.sp_model.encode(_A       ,					out_type=_A     )





     def 	UpperCAmelCase(self   :					str       ,					_A   :					List[str]     )		->		Any:
         return self.sp_model.piece_to_id(_A     )





     def 	UpperCAmelCase(self   :					int       ,					_A   :					str     )		->		Optional[Any]:
         snake_case				  =      self.sp_model.IdToPiece(_A     )
         return token





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Tuple     )		->		Optional[Any]:
         snake_case				  =      []
         snake_case				  =      ""
         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(_A     ) + token
                 snake_case				  =      []
             else:
                 current_sub_tokens.append(_A     )
         out_string += self.sp_model.decode(_A     )
         return out_string.strip()





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					str       ,					_A   :					Optional[str] = None     )		->		Tuple[str]:
         if not os.path.isdir(_A     ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
             return
         snake_case				  =      os.path.join(
             _A       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

         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 MobileNetVaImageProcessor







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Optional[int]       ,					_A   :					Dict       ,					_A   :					Optional[int]=7       ,					_A   :					Union[str, Any]=3       ,					_A   :					Tuple=1_8       ,					_A   :					str=3_0       ,					_A   :					Union[str, Any]=4_0_0       ,					_A   :					Optional[Any]=True       ,					_A   :					Union[str, Any]=None       ,					_A   :					str=True       ,					_A   :					Optional[int]=None       ,					)		->		int:
         snake_case				  =      size if size is not None else {"shortest_edge": 2_0}
         snake_case				  =      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_center_crop
         snake_case				  =      crop_size





     def 	UpperCAmelCase(self   :					str     )		->		int:
         return {
             "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   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[int]     =	MobileNetVaImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         snake_case				  =      MobileNetVaImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      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       ,					"crop_size"     )     )





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 2_0}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 1_8, "width": 1_8}     )

         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 4_2}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 8_4, "width": 8_4}     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         pass





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      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
         snake_case				  =      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 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				  =      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
         snake_case				  =      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
         snake_case				  =      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 	UpperCAmelCase(self   :					List[str]     )		->		List[str]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      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
         snake_case				  =      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
         snake_case				  =      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"],
             )       ,					)

