




"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_				:    Tuple										=     logging.get_logger(__name__)

a_				:    int										=     {
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class __lowercase(      SCREAMING_SNAKE_CASE__				):




     '''simple docstring'''


     __a						:						str     		= 'segformer'
     def __init__(    self						,	__a=3						,	__a=4						,	__a=[2, 2, 2, 2]						,	__a=[8, 4, 2, 1]						,	__a=[32, 64, 160, 256]						,	__a=[7, 3, 3, 3]						,	__a=[4, 2, 2, 2]						,	__a=[1, 2, 5, 8]						,	__a=[4, 4, 4, 4]						,	__a="gelu"						,	__a=0.0						,	__a=0.0						,	__a=0.1						,	__a=0.02						,	__a=0.1						,	__a=1E-6						,	__a=256						,	__a=255						,	**__a						,	):
        super().__init__(**_lowercase     )

        if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
           warnings.warn(
               'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
               ' removed, as the behaviour will default to that of reshape_last_stage = True.'						,	_lowercase						,	)

        __lowerCamelCase					:	Union[str, Any]	       =				num_channels
        __lowerCamelCase					:	Optional[int]	       =				num_encoder_blocks
        __lowerCamelCase					:	Union[str, Any]	       =				depths
        __lowerCamelCase					:	Optional[int]	       =				sr_ratios
        __lowerCamelCase					:	Dict	       =				hidden_sizes
        __lowerCamelCase					:	str	       =				patch_sizes
        __lowerCamelCase					:	Optional[int]	       =				strides
        __lowerCamelCase					:	Union[str, Any]	       =				mlp_ratios
        __lowerCamelCase					:	Optional[Any]	       =				num_attention_heads
        __lowerCamelCase					:	Optional[Any]	       =				hidden_act
        __lowerCamelCase					:	Dict	       =				hidden_dropout_prob
        __lowerCamelCase					:	Any	       =				attention_probs_dropout_prob
        __lowerCamelCase					:	Union[str, Any]	       =				classifier_dropout_prob
        __lowerCamelCase					:	str	       =				initializer_range
        __lowerCamelCase					:	Optional[int]	       =				drop_path_rate
        __lowerCamelCase					:	Tuple	       =				layer_norm_eps
        __lowerCamelCase					:	List[str]	       =				decoder_hidden_size
        __lowerCamelCase					:	Optional[Any]	       =				kwargs.get('reshape_last_stage'						,	_lowercase     )
        __lowerCamelCase					:	List[str]	       =				semantic_loss_ignore_index



class __lowercase(      SCREAMING_SNAKE_CASE__				):




     '''simple docstring'''


     __a						:						List[str]     		= version.parse('1.11'				)
     @property
     def     snake_case_				(    self     ):
        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]     )
     @property
     def     snake_case_				(    self     ):
        return 1E-4





     @property
     def     snake_case_				(    self     ):
        return 12









"""simple docstring"""
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:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

a_				:    List[Any]										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_				:    int										=     {
    '''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'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Union[str, Any]     		= ['input_ids', 'attention_mask']
					__a						:						Optional[Any]     		= BarthezTokenizer
					def __init__(    self						,	__a=None						,	__a=None						,	__a="<s>"						,	__a="</s>"						,	__a="</s>"						,	__a="<s>"						,	__a="<unk>"						,	__a="<pad>"						,	__a="<mask>"						,	**__a						,	):
								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase					:	Optional[Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else mask_token

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

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	int	       =				[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     snake_case_				(    self						,	__a						,	__a = None     ):
								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(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[Any]	       =				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     ):
											copyfile(self.vocab_file						,	__a     )

								return (out_vocab_file,)



