






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case    				=		{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

snake_case    				=		{
    """gpt-neox-20b""": 2_048,
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[str]			=	VOCAB_FILES_NAMES
     UpperCamelCase_							:		Tuple			=	PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_							:		int			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_							:		str			=	['''input_ids''', '''attention_mask''']


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[int]=None					, UpperCAmelCase_       :	List[Any]=None					, UpperCAmelCase_       :	List[str]=None					, UpperCAmelCase_       :	Optional[int]="<|endoftext|>"					, UpperCAmelCase_       :	List[str]="<|endoftext|>"					, UpperCAmelCase_       :	List[str]="<|endoftext|>"					, UpperCAmelCase_       :	Tuple=False					, **UpperCAmelCase_       :	Tuple					, ):
       super().__init__(
           UpperCAmelCase_					, UpperCAmelCase_					, tokenizer_file=UpperCAmelCase_					, unk_token=UpperCAmelCase_					, bos_token=UpperCAmelCase_					, eos_token=UpperCAmelCase_					, add_prefix_space=UpperCAmelCase_					, **UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      int							=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
       if pre_tok_state.get("add_prefix_space"					, UpperCAmelCase_      ) != add_prefix_space:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    getattr(UpperCAmelCase_					, pre_tok_state.pop("type"      )      )
         SCREAMING_SNAKE_CASE       :      int							=    add_prefix_space
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    pre_tok_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Dict							=    add_prefix_space


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[str] = None      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self._tokenizer.model.save(UpperCAmelCase_					, name=UpperCAmelCase_      )
       return tuple(UpperCAmelCase_      )






     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	"Conversation"      ):
       SCREAMING_SNAKE_CASE       :      Any							=    []
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_      ) + [self.eos_token_id]      )

       if len(UpperCAmelCase_      ) > self.model_max_length:
         SCREAMING_SNAKE_CASE       :      int							=    input_ids[-self.model_max_length :]
       return input_ids














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


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	'''segformer'''


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

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





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	version.parse('''1.11'''					)


     @property
     def 				_A       (				self       :	Optional[Any]      ):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ]      )


     @property
     def 				_A       (				self       :	List[str]      ):
       return 1E-4






     @property
     def 				_A       (				self       :	Any      ):
       return 12






