
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__SCREAMING_SNAKE_CASE			:   Tuple         =     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     {
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
  import detectrona


class 		__magic_name__   (						__SCREAMING_SNAKE_CASE    ):
  _lowerCAmelCase		=	"layoutlmv2"





  def __init__(							self    :	List[str]						,  lowerCamelCase__    :	str=3_0_5_2_2						,  lowerCamelCase__    :	Any=7_6_8						,  lowerCamelCase__    :	Dict=1_2						,  lowerCamelCase__    :	List[str]=1_2						,  lowerCamelCase__    :	Optional[int]=3_0_7_2						,  lowerCamelCase__    :	List[Any]="gelu"						,  lowerCamelCase__    :	Optional[int]=0.1						,  lowerCamelCase__    :	str=0.1						,  lowerCamelCase__    :	Optional[Any]=5_1_2						,  lowerCamelCase__    :	int=2						,  lowerCamelCase__    :	List[Any]=0.0_2						,  lowerCamelCase__    :	Optional[int]=1E-12						,  lowerCamelCase__    :	Union[str, Any]=0						,  lowerCamelCase__    :	int=1_0_2_4						,  lowerCamelCase__    :	List[Any]=1_2_8						,  lowerCamelCase__    :	List[str]=3_2						,  lowerCamelCase__    :	Optional[int]=True						,  lowerCamelCase__    :	List[str]=2_5_6						,  lowerCamelCase__    :	List[str]=6_4						,  lowerCamelCase__    :	Dict=True						,  lowerCamelCase__    :	Tuple=[7, 7, 2_5_6]						,  lowerCamelCase__    :	Optional[Any]=1_2_8						,  lowerCamelCase__    :	int=1_2_8						,  lowerCamelCase__    :	Union[str, Any]=True						,  lowerCamelCase__    :	Any=True						,  lowerCamelCase__    :	Optional[int]=False						,  lowerCamelCase__    :	List[str]=None						,  **lowerCamelCase__    :	str						,  ):
       super().__init__(
           vocab_size=UpperCamelCase__						,  hidden_size=UpperCamelCase__						,  num_hidden_layers=UpperCamelCase__						,  num_attention_heads=UpperCamelCase__						,  intermediate_size=UpperCamelCase__						,  hidden_act=UpperCamelCase__						,  hidden_dropout_prob=UpperCamelCase__						,  attention_probs_dropout_prob=UpperCamelCase__						,  max_position_embeddings=UpperCamelCase__						,  type_vocab_size=UpperCamelCase__						,  initializer_range=UpperCamelCase__						,  layer_norm_eps=UpperCamelCase__						,  pad_token_id=UpperCamelCase__						,  **UpperCamelCase__						,  )
       lowerCAmelCase		:					Optional[int]  	=     max_ad_position_embeddings
       lowerCAmelCase		:					Optional[int]  	=     max_rel_pos
       lowerCAmelCase		:					Optional[Any]  	=     rel_pos_bins
       lowerCAmelCase		:					int  	=     fast_qkv
       lowerCAmelCase		:					int  	=     max_rel_ad_pos
       lowerCAmelCase		:					str  	=     rel_ad_pos_bins
       lowerCAmelCase		:					Optional[int]  	=     convert_sync_batchnorm
       lowerCAmelCase		:					Any  	=     image_feature_pool_shape
       lowerCAmelCase		:					Optional[int]  	=     coordinate_size
       lowerCAmelCase		:					Union[str, Any]  	=     shape_size
       lowerCAmelCase		:					Optional[int]  	=     has_relative_attention_bias
       lowerCAmelCase		:					str  	=     has_spatial_attention_bias
       lowerCAmelCase		:					Any  	=     has_visual_segment_embedding
       lowerCAmelCase		:					Union[str, Any]  	=     (
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )





  @classmethod
  def       _A							(							self    :	Optional[int]				):
       return {
           "MODEL.MASK_ON": True,
           "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
           "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
           "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.ANCHOR_GENERATOR.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
           "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
           "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
           "MODEL.ROI_HEADS.NUM_CLASSES": 5,
           "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
           "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
           "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
           "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 1_4,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 1_0_1,
           "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 3_2,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }





  def       _A							(							self    :	Any				):
       lowerCAmelCase		:					int  	=     detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
            lowerCAmelCase		:					Optional[int]  	=     k.split('''.'''				)
            lowerCAmelCase		:					Optional[Any]  	=     detectrona_config
            for attribute in attributes[:-1]:
                 lowerCAmelCase		:					List[Any]  	=     getattr(UpperCamelCase__						,  UpperCamelCase__				)
            setattr(UpperCamelCase__						,  attributes[-1]						,  UpperCamelCase__				)

       return detectrona_config


import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

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


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

require_version('tokenizers>=0.13.3')

if is_sentencepiece_available():
		from .tokenization_llama import LlamaTokenizer
else:
		__SCREAMING_SNAKE_CASE			:   int         =     None

__SCREAMING_SNAKE_CASE			:   List[Any]         =     logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE			:   Optional[int]         =     {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Dict         =     '[INST]', '[/INST]'
__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   List[Any]         =     '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__SCREAMING_SNAKE_CASE			:   List[Any]         =     'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	VOCAB_FILES_NAMES
		_lowerCAmelCase		=	LlamaTokenizer
		_lowerCAmelCase		=	"left"
		_lowerCAmelCase		=	["input_ids", "attention_mask"]





		def __init__(							self    :	List[Any]						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	Union[str, Any]=None						,  lowerCamelCase__    :	List[str]=False						,  lowerCamelCase__    :	int="<unk>"						,  lowerCamelCase__    :	int="<s>"						,  lowerCamelCase__    :	str="</s>"						,  lowerCamelCase__    :	Optional[Any]=True						,  lowerCamelCase__    :	int=False						,  **lowerCamelCase__    :	List[str]						,  ):
							super().__init__(
							    vocab_file=lowerCamelCase__						,  tokenizer_file=lowerCamelCase__						,  clean_up_tokenization_spaces=lowerCamelCase__						,  unk_token=lowerCamelCase__						,  bos_token=lowerCamelCase__						,  eos_token=lowerCamelCase__						,  **lowerCamelCase__						,  )
							lowerCAmelCase		:					Optional[int]  	=     add_bos_token
							lowerCAmelCase		:					int  	=     add_eos_token
							self.update_post_processor()

							lowerCAmelCase		:					List[Any]  	=     vocab_file
							lowerCAmelCase		:					List[str]  	=     False if not self.vocab_file else True





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					List[str]  	=     self.bos_token
							lowerCAmelCase		:					Optional[Any]  	=     self.bos_token_id

							lowerCAmelCase		:					Optional[Any]  	=     self.eos_token
							lowerCAmelCase		:					List[str]  	=     self.eos_token_id

							lowerCAmelCase		:					Dict  	=     f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
							lowerCAmelCase		:					Optional[int]  	=     f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

							lowerCAmelCase		:					Any  	=     []
							if self.add_bos_token:
												special_tokens.append((bos, bos_token_id)				)
							if self.add_eos_token:
												special_tokens.append((eos, eos_token_id)				)
							lowerCAmelCase		:					Union[str, Any]  	=     processors.TemplateProcessing(
							    single=lowerCamelCase__						,  pair=lowerCamelCase__						,  special_tokens=lowerCamelCase__				)





		@property
		def       _A							(							self    :	str				):
							return self._add_eos_token





		@property
		def       _A							(							self    :	Dict				):
							return self._add_bos_token





		@add_eos_token.setter
		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	str				):
							lowerCAmelCase		:					Any  	=     value
							self.update_post_processor()





		@add_bos_token.setter
		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	Optional[Any]				):
							lowerCAmelCase		:					List[Any]  	=     value
							self.update_post_processor()





		def       _A							(							self    :	int						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Optional[str] = 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(lowerCamelCase__				):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
												return
							lowerCAmelCase		:					str  	=     os.path.join(
							    lowerCamelCase__						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
												copyfile(self.vocab_file						,  lowerCamelCase__				)

							return (out_vocab_file,)





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	"Conversation"				):
							lowerCAmelCase		:					int  	=     list(conversation.iter_texts()				)
							if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
							    [not is_user for is_user, msg in dialogue[1::2]]				):
												raise ValueError(
												    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)

							lowerCAmelCase		:					List[str]  	=     []
							if len(conversation.past_user_inputs				) > 0:
												if not conversation.past_user_inputs[0].startswith(lowerCamelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																	lowerCAmelCase		:					Tuple  	=     (
																	    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																	)
							elif not dialogue[0][1].startswith(lowerCamelCase__				) or E_SYS not in dialogue[0][1]:
												lowerCAmelCase		:					Optional[Any]  	=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

							dialog_tokens += sum(
							    [
							        [self.bos_token_id]
							        + self.encode(
							            f'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''						,  add_special_tokens=lowerCamelCase__				)
							        + [self.eos_token_id]
							        for prompt, answer in zip(dialogue[::2]						,  dialogue[1::2]				)
							    ]						,  []						,  )
							if not (dialogue[-1][0]):
												raise ValueError(f'''Last message must be from user, got {dialogue[-1]["role"]}'''				)
							dialog_tokens += [self.bos_token_id] + self.encode(
							    f'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''						,  add_special_tokens=lowerCamelCase__				)
							return dialog_tokens
