



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 __snake_case		:List[Any]   	=  None

__snake_case		:Dict   	=  logging.get_logger(__name__)

__snake_case		:Optional[int]   	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case		:Union[str, Any]   	=  {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case		:Optional[Any]   	=  {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__snake_case		:Optional[int]   	=  '''▁'''

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Tuple		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Tuple		      =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					str		      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					str		      =		['''input_ids''', '''attention_mask''']
       UpperCamelCase__						:					Dict		      =		BarthezTokenizer



       def __init__(	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Tuple="<s>"  ,	__SCREAMING_SNAKE_CASE							:  List[str]="</s>"  ,	__SCREAMING_SNAKE_CASE							:  Tuple="</s>"  ,	__SCREAMING_SNAKE_CASE							:  int="<s>"  ,	__SCREAMING_SNAKE_CASE							:  Any="<unk>"  ,	__SCREAMING_SNAKE_CASE							:  int="<pad>"  ,	__SCREAMING_SNAKE_CASE							:  Any="<mask>"  ,	**__SCREAMING_SNAKE_CASE							:  Any  ,	):


           '''simple docstring'''

           __a		 =       AddedToken(__SCREAMING_SNAKE_CASE  ,	lstrip=__SCREAMING_SNAKE_CASE  ,	rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) else mask_token

           super().__init__(
               __SCREAMING_SNAKE_CASE  ,	tokenizer_file=__SCREAMING_SNAKE_CASE  ,	bos_token=__SCREAMING_SNAKE_CASE  ,	eos_token=__SCREAMING_SNAKE_CASE  ,	unk_token=__SCREAMING_SNAKE_CASE  ,	sep_token=__SCREAMING_SNAKE_CASE  ,	cls_token=__SCREAMING_SNAKE_CASE  ,	pad_token=__SCREAMING_SNAKE_CASE  ,	mask_token=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE  ,	)

           __a		 =       vocab_file
           __a		 =       False if not self.vocab_file else True



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None):


           '''simple docstring'''

           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           __a		 =       [self.cls_token_id]
           __a		 =       [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None):


           '''simple docstring'''

           __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]





       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = None):


           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
               raise ValueError(
                   '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                   '''tokenizer.''')

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

           if os.path.abspath(self.vocab_file) != os.path.abspath(__SCREAMING_SNAKE_CASE):
               copyfile(self.vocab_file  ,	__SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)










import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor

class 					_A (			unittest.TestCase					):



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


           '''simple docstring'''

           __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
           __a		 =       do_reduce_labels





       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           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_reduce_labels": self.do_reduce_labels,
           }





def   __snake_case			(    ):
    __a		 =       load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''       )

    __a		 =       Image.open(dataset[0]['''file''']       )
    __a		 =       Image.open(dataset[1]['''file''']       )

    return image, map





def   __snake_case			(    ):
    __a		 =       load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''       )

    __a		 =       Image.open(ds[0]['''file''']       )
    __a		 =       Image.open(ds[1]['''file''']       )
    __a		 =       Image.open(ds[2]['''file''']       )
    __a		 =       Image.open(ds[3]['''file''']       )

    return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		BeitImageProcessor if is_vision_available() else None



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       BeitImageProcessingTester(self)



       @property
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_resize'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''size'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_center_crop'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''center_crop'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_normalize'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_mean'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_std'''))



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __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})
           self.assertEqual(image_processor.do_reduce_labels  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       self.image_processing_class.from_dict(
               self.image_processor_dict  ,	size=42  ,	crop_size=84  ,	reduce_labels=__SCREAMING_SNAKE_CASE)
           self.assertEqual(image_processor.size  ,	{'''height''': 42, '''width''': 42})
           self.assertEqual(image_processor.crop_size  ,	{'''height''': 84, '''width''': 84})
           self.assertEqual(image_processor.do_reduce_labels  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

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


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	numpify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	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(__SCREAMING_SNAKE_CASE  ,	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							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	torchify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	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(__SCREAMING_SNAKE_CASE  ,	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							:  Dict):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	torchify=__SCREAMING_SNAKE_CASE)
           __a		 =       []
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor)
               maps.append(torch.zeros(image.shape[-2:]).long())

           # Test not batched input
           __a		 =       image_processing(image_inputs[0]  ,	maps[0]  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape  ,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test batched
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].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'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test not batched input (PIL images)
           __a ,     __a		 =       prepare_semantic_single_inputs()

           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape  ,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test batched input (PIL images)
           __a ,     __a		 =       prepare_semantic_batch_inputs()

           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape  ,	(
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   2,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)





       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)

           # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
           __a ,     __a		 =       prepare_semantic_single_inputs()
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 150)

           __a		 =       True
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)





