



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 GLPNImageProcessor





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def __init__(self				,			__a				,			__a=7				,			__a=3				,			__a=18				,			__a=30				,			__a=4_00				,			__a=True				,			__a=32				,			__a=True				,			):






           '''simple docstring'''



           lowerCamelCase								=		parent
           lowerCamelCase								=		batch_size
           lowerCamelCase								=		num_channels
           lowerCamelCase								=		image_size
           lowerCamelCase								=		min_resolution
           lowerCamelCase								=		max_resolution
           lowerCamelCase								=		do_resize
           lowerCamelCase								=		size_divisor
           lowerCamelCase								=		do_rescale







     def 						_a      (self    ):






           '''simple docstring'''



           return {
               "do_resize": self.do_resize,
               "size_divisor": self.size_divisor,
               "do_rescale": self.do_rescale,
           }





@require_torch
@require_vision
class    lowerCamelCase__  (						UpperCAmelCase_       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			GLPNImageProcessor if is_vision_available() else None







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		GLPNImageProcessingTester(self    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.image_processor_tester.prepare_image_processor_dict()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           self.assertTrue(hasattr(__a				,			"do_resize"    )    )
           self.assertTrue(hasattr(__a				,			"size_divisor"    )    )
           self.assertTrue(hasattr(__a				,			"resample"    )    )
           self.assertTrue(hasattr(__a				,			"do_rescale"    )    )







     def 						_a      (self    ):






           '''simple docstring'''



           pass







     def 						_a      (self    ):






           '''simple docstring'''



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

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           lowerCamelCase								=		image_processing(image_inputs[0]				,			return_tensors="pt"    ).pixel_values
           self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
           self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           # create random numpy tensors
           lowerCamelCase								=		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 (GLPNImageProcessor doesn't support batching)
           lowerCamelCase								=		image_processing(image_inputs[0]				,			return_tensors="pt"    ).pixel_values
           self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
           self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           # create random PyTorch tensors
           lowerCamelCase								=		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 (GLPNImageProcessor doesn't support batching)
           lowerCamelCase								=		image_processing(image_inputs[0]				,			return_tensors="pt"    ).pixel_values
           self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
           self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )




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_						:     Optional[int]	     =		logging.get_logger(__name__)

a_						:     Any	     =		{'vocab_file': 'spiece.model'}

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

a_						:     List[Any]	     =		{'bert_for_seq_generation': 5_1_2}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			VOCAB_FILES_NAMES
     _A       =			PRETRAINED_VOCAB_FILES_MAP
     _A       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A       =			[]
     _A       =			['input_ids', 'attention_mask']







     def __init__(self				,			__a				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<pad>"				,			__a="<::::>"				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		{} 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				,			)

           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.sp_model.get_piece_size()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{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    ):






           '''simple docstring'''



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







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

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

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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.piece_to_id(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.sp_model.IdToPiece(__a    )
           return token







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           lowerCamelCase								=		""
           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
                       lowerCamelCase								=		[]
                 else:
                       current_sub_tokens.append(__a    )
           out_string += self.sp_model.decode(__a    )
           return out_string.strip()







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		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:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)