



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

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
				if is_torch_available():
								import torch
				if is_tf_available():
								import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


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

_lowercase: str      			=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowercase: Any      			=		{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

_lowercase: str      			=		{
    'Salesforce/codegen-350M-mono': 2048,
}



class 	_lowercase							(   lowerCamelCase_	):





	"""simple docstring"""
	__A							=							VOCAB_FILES_NAMES
	__A							=							PRETRAINED_VOCAB_FILES_MAP
	__A							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A							=							["input_ids", "attention_mask"]
	__A							=							CodeGenTokenizer




	def __init__(self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_="<|endoftext|>"		,       lowerCamelCase_="<|endoftext|>"		,       lowerCamelCase_="<|endoftext|>"		,       lowerCamelCase_=False		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						super().__init__(
						    __a		,       __a		,       tokenizer_file=__a		,       unk_token=__a		,       bos_token=__a		,       eos_token=__a		,       add_prefix_space=__a		,       **__a		,       )

						if kwargs.pop("add_bos_token"		,       __a		):
											a					     =   kwargs.pop("name_or_path"		,       ""		)
											raise ValueError(
											    "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
											    "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
											    F'''`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'''
											    F'''`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'''
											    "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
											    " so that the fast tokenizer works correctly."		)

						a					     =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get("add_prefix_space"		,       __a		) != add_prefix_space:
											a					     =   getattr(__a		,       pre_tok_state.pop("type"		)		)
											a					     =   add_prefix_space
											a					     =   pre_tok_class(**__a		)

						a					     =   add_prefix_space




	def      UpperCamelCase_       (self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   kwargs.get("is_split_into_words"		,       __a		)
						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*__a		,       **__a		)




	def      UpperCamelCase_       (self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   kwargs.get("is_split_into_words"		,       __a		)

						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*__a		,       **__a		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   self._tokenizer.model.save(__a		,       name=__a		)
						return tuple(__a		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = False		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   super().decode(
						    token_ids=__a		,       skip_special_tokens=__a		,       clean_up_tokenization_spaces=__a		,       **__a		,       )

						if truncate_before_pattern is not None and len(__a		) > 0:
											a					     =   self.truncate(__a		,       __a		)

						return decoded_text




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						def find_re(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   pattern.search(__a		,       __a		)
											return m.start() if m else -1

						a					     =   [re.compile(__a		,       re.MULTILINE		) for pattern in truncate_before_pattern]

						a					     =   list(re.finditer("^print"		,       __a		,       re.MULTILINE		)		)

						if len(__a		) > 1:
											a					     =   completion[: prints[1].start()]

						a					     =   list(re.finditer("^def"		,       __a		,       re.MULTILINE		)		)

						if len(__a		) > 1:
											a					     =   completion[: defs[1].start()]

						a					     =   0

						a					     =   [
						    pos for pos in [find_re(__a		,       __a		,       __a		) for terminal in terminals] if pos != -1
						]

						if len(__a		) > 0:
											return completion[: min(__a		)]
						else:
											return completion







'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
												_a								= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= init_xavier_std
								_a								= encoder_layerdrop
								_a								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# Hungarian matcher
								_a								= class_cost
								_a								= bbox_cost
								_a								= giou_cost
								# Loss coefficients
								_a								= mask_loss_coefficient
								_a								= dice_loss_coefficient
								_a								= bbox_loss_coefficient
								_a								= giou_loss_coefficient
								_a								= eos_coefficient
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.encoder_attention_heads




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output
