


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

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


if is_sentencepiece_available():
						from .tokenization_ta import TaTokenizer
else:
						A	:		List[str]    =       None


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

A	:		Optional[int]    =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A	:		str    =       {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
A	:		Dict    =       {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			VOCAB_FILES_NAMES
					A__      =			PRETRAINED_VOCAB_FILES_MAP
					A__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__      =			['''input_ids''', '''attention_mask''']
					A__      =			TaTokenizer

					A__      =			[]

					def __init__(self   :						int				, _UpperCAmelCase   :						Union[str, Any]=None				, _UpperCAmelCase   :						str=None				, _UpperCAmelCase   :						Optional[int]="</s>"				, _UpperCAmelCase   :						Union[str, Any]="<unk>"				, _UpperCAmelCase   :						str="<pad>"				, _UpperCAmelCase   :						List[str]=100				, _UpperCAmelCase   :						Union[str, Any]=None				, **_UpperCAmelCase   :						List[Any]				, )						->       Optional[int]:




							"""simple docstring"""


							if extra_ids > 0 and additional_special_tokens is None:
									lowercase__					      =		[f'''<extra_id_{i}>''' for i in range(_UpperCAmelCase		)]
							elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra special tokens
									lowercase__					      =		len(set(filter(lambda _UpperCAmelCase		: bool("""extra_id_""" in str(_UpperCAmelCase		)		)				, _UpperCAmelCase		)		)		)
									if extra_tokens != extra_ids:
											raise ValueError(
											    f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
											    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
											    """ tokens"""		)

							super().__init__(
							    _UpperCAmelCase				, tokenizer_file=_UpperCAmelCase				, eos_token=_UpperCAmelCase				, unk_token=_UpperCAmelCase				, pad_token=_UpperCAmelCase				, extra_ids=_UpperCAmelCase				, additional_special_tokens=_UpperCAmelCase				, **_UpperCAmelCase				, )

							lowercase__					      =		vocab_file
							lowercase__					      =		False if not self.vocab_file else True
							lowercase__					      =		extra_ids

					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						int				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
									lowercase__					      =		TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
									if init_max_model_length is not None and init_max_model_length != max_model_length:
											return init_max_model_length
									elif init_max_model_length is None:
											warnings.warn(
											    """This tokenizer was incorrectly instantiated with a model max length of"""
											    f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
											    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
											    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
											    f''' {pretrained_model_name_or_path} automatically truncating your input to'''
											    f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
											    f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
											    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
											    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""				, _UpperCAmelCase				, )

							return max_model_length

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Optional[str] = None		)						->       Tuple[str]:




							"""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(_UpperCAmelCase		):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
									return
							lowercase__					      =		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		)
									logger.info(f'''Copy vocab file to {out_vocab_file}'''		)

							return (out_vocab_file,)

					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						List[int]				, _UpperCAmelCase   :						Optional[List[int]] = None		)						->       List[int]:




							"""simple docstring"""


							lowercase__					      =		token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
									return self.prefix_tokens + token_ids_a
							else:
									lowercase__					      =		token_ids_a + [self.eos_token_id]
									return self.prefix_tokens + token_ids_a + token_ids_a

					def   lowerCamelCase__					(self   :						str				, _UpperCAmelCase   :						List[int]				, _UpperCAmelCase   :						Optional[List[int]] = None		)						->       List[int]:




							"""simple docstring"""


							lowercase__					      =		[self.eos_token_id]

							if token_ids_a is None:
									return len(token_ids_a + eos		) * [0]
							return len(token_ids_a + eos + token_ids_a + eos		) * [0]

					def   lowerCamelCase__					(self   :						Dict		)						->       str:




							"""simple docstring"""


							return list(
							    set(filter(lambda _UpperCAmelCase		: bool(re.search(r"""<extra_id_\d+>"""				, _UpperCAmelCase		)		) is not None				, self.additional_special_tokens		)		)		)


					def   lowerCamelCase__					(self   :						List[Any]		)						->       int:




							"""simple docstring"""


							return [self.convert_tokens_to_ids(_UpperCAmelCase		) for token in self.get_sentinel_tokens()]





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	:		List[str]    =       logging.get_logger(__name__)

A	:		List[Any]    =       {
    '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 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''segformer'''

					def __init__(self   :						Dict				, _UpperCAmelCase   :						Optional[int]=3				, _UpperCAmelCase   :						Dict=4				, _UpperCAmelCase   :						Union[str, Any]=[2, 2, 2, 2]				, _UpperCAmelCase   :						List[str]=[8, 4, 2, 1]				, _UpperCAmelCase   :						str=[32, 64, 160, 256]				, _UpperCAmelCase   :						Optional[int]=[7, 3, 3, 3]				, _UpperCAmelCase   :						int=[4, 2, 2, 2]				, _UpperCAmelCase   :						str=[1, 2, 5, 8]				, _UpperCAmelCase   :						Optional[int]=[4, 4, 4, 4]				, _UpperCAmelCase   :						List[Any]="gelu"				, _UpperCAmelCase   :						Optional[Any]=0.0				, _UpperCAmelCase   :						Dict=0.0				, _UpperCAmelCase   :						Dict=0.1				, _UpperCAmelCase   :						List[str]=0.02				, _UpperCAmelCase   :						str=0.1				, _UpperCAmelCase   :						Optional[Any]=1E-6				, _UpperCAmelCase   :						Optional[Any]=256				, _UpperCAmelCase   :						Any=255				, **_UpperCAmelCase   :						str				, )						->       Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase		)

							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."""				, _UpperCAmelCase				, )

							lowercase__					      =		num_channels
							lowercase__					      =		num_encoder_blocks
							lowercase__					      =		depths
							lowercase__					      =		sr_ratios
							lowercase__					      =		hidden_sizes
							lowercase__					      =		patch_sizes
							lowercase__					      =		strides
							lowercase__					      =		mlp_ratios
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		classifier_dropout_prob
							lowercase__					      =		initializer_range
							lowercase__					      =		drop_path_rate
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		decoder_hidden_size
							lowercase__					      =		kwargs.get("""reshape_last_stage"""				, _UpperCAmelCase		)
							lowercase__					      =		semantic_loss_ignore_index






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			version.parse('''1.11'''							)

					@property
					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]		)

					@property
					def   lowerCamelCase__					(self   :						Optional[int]		)						->       float:




							"""simple docstring"""


							return 1E-4


					@property
					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       int:




							"""simple docstring"""


							return 12

