






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:
		__UpperCAmelCase     							=						None

__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     							=						{
    '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'
        ),
    },
}

__UpperCAmelCase     							=						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__UpperCAmelCase     							=						'▁'



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Union[str, Any]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Dict							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Dict							=  ['''input_ids''', '''attention_mask''']
		_snake_case   :   Union[str, Any]							=  BarthezTokenizer



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase="<mask>"    ,		**_UpperCamelCase    ,		)     ->							str:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_   :				List[Any]						=						AddedToken(_UpperCamelCase    ,		lstrip=_UpperCamelCase    ,		rstrip=_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else mask_token

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

							UpperCAmelCase_   :				Tuple						=						vocab_file
							UpperCAmelCase_   :				Tuple						=						False if not self.vocab_file else True



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:

							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_   :				Union[str, Any]						=						[self.cls_token_id]
							UpperCAmelCase_   :				Optional[Any]						=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Any						=						[self.sep_token_id]
							UpperCAmelCase_   :				List[Any]						=						[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  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							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(_UpperCamelCase       ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
												return
							UpperCAmelCase_   :				List[Any]						=						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       ):
												copyfile(self.vocab_file    ,		_UpperCamelCase       )

							return (out_vocab_file,)














import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCAmelCase     							=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCAmelCase     							=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir    ,		'schedulers/'       )       )
							UpperCAmelCase_   :				int						=						self.diffusers_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase    ,		'src/diffusers/schedulers/scheduling_ddpm.py'       )    ,		os.path.join(self.diffusers_dir    ,		'schedulers/scheduling_ddpm.py'       )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						'src/diffusers'
							shutil.rmtree(self.diffusers_dir       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Dict						=						comment + f"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
												UpperCAmelCase_   :				Optional[Any]						=						comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
							UpperCAmelCase_   :				Any						=						black.Mode(target_versions={black.TargetVersion.PYaa}    ,		line_length=1_1_9       )
							UpperCAmelCase_   :				Optional[Any]						=						black.format_str(_UpperCamelCase    ,		mode=_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						os.path.join(self.diffusers_dir    ,		'new_code.py'       )
							with open(_UpperCamelCase    ,		'w'    ,		newline='\n'       ) as f:
												f.write(_UpperCamelCase       )
							if overwrite_result is None:
												self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase       )       ) == 0       )
							else:
												check_copies.is_copy_consistent(f.name    ,		overwrite=_UpperCamelCase       )
												with open(_UpperCamelCase    ,		'r'       ) as f:
																	self.assertTrue(f.read()    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'       )
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							# Base copy consistency
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		REFERENCE_CODE + '\n'    ,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		_UpperCamelCase    ,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with a really long name
							UpperCAmelCase_   :				Optional[int]						=						'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"    ,		f"{long_class_name}SchedulerOutput"    ,		re.sub('Bert'    ,		_UpperCamelCase    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		_UpperCamelCase    ,		overwrite_result=re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)






