



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}







class       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   A__      : int       =						"layoutlm"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Optional[int]=30522  ,lowerCamelCase_:     Any=768  ,lowerCamelCase_:     str=12  ,lowerCamelCase_:     List[str]=12  ,lowerCamelCase_:     List[str]=3072  ,lowerCamelCase_:     Dict="gelu"  ,lowerCamelCase_:     Dict=0.1  ,lowerCamelCase_:     List[Any]=0.1  ,lowerCamelCase_:     List[str]=512  ,lowerCamelCase_:     List[Any]=2  ,lowerCamelCase_:     Any=0.0_2  ,lowerCamelCase_:     List[Any]=1e-12  ,lowerCamelCase_:     Dict=0  ,lowerCamelCase_:     str="absolute"  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     Optional[Any]=1024  ,**lowerCamelCase_:     Union[str, Any]  ,)	->      Dict:
       super().__init__(pad_token_id=lowerCAmelCase_  ,**lowerCAmelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					vocab_size
       UpperCAmelCase_     :       List[str]  		=					hidden_size
       UpperCAmelCase_     :       Union[str, Any]  		=					num_hidden_layers
       UpperCAmelCase_     :       Optional[Any]  		=					num_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					hidden_act
       UpperCAmelCase_     :       Optional[int]  		=					intermediate_size
       UpperCAmelCase_     :       str  		=					hidden_dropout_prob
       UpperCAmelCase_     :       Union[str, Any]  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       int  		=					max_position_embeddings
       UpperCAmelCase_     :       Dict  		=					type_vocab_size
       UpperCAmelCase_     :       Dict  		=					initializer_range
       UpperCAmelCase_     :       List[Any]  		=					layer_norm_eps
       UpperCAmelCase_     :       Union[str, Any]  		=					position_embedding_type
       UpperCAmelCase_     :       Optional[int]  		=					use_cache
       UpperCAmelCase_     :       str  		=					max_ad_position_embeddings







class       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     List[Any] = "default"  ,lowerCamelCase_:     Optional[Any] = None  ,)	->      Union[str, Any]:
       super().__init__(lowerCAmelCase_  ,task=lowerCAmelCase_  ,patching_specs=lowerCAmelCase_							)
       UpperCAmelCase_     :       Tuple  		=					config.max_ad_position_embeddings - 1


   @property
   def       A__    (    self:     Tuple							)	->      str:
       return OrderedDict(
           [
               ("""input_ids""", {0: """batch""", 1: """sequence"""}),
               ("""bbox""", {0: """batch""", 1: """sequence"""}),
               ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
               ("""token_type_ids""", {0: """batch""", 1: """sequence"""}),
           ]							)






   def       A__    (    self:     Tuple  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     Tuple = -1  ,lowerCamelCase_:     List[Any] = -1  ,lowerCamelCase_:     Tuple = False  ,lowerCamelCase_:     List[str] = None  ,)	->      Dict:
       UpperCAmelCase_     :       int  		=					super().generate_dummy_inputs(
           lowerCAmelCase_  ,batch_size=lowerCAmelCase_  ,seq_length=lowerCAmelCase_  ,is_pair=lowerCAmelCase_  ,framework=lowerCAmelCase_							)

       # Generate a dummy bbox
       UpperCAmelCase_     :       str  		=					[48, 84, 73, 128]

       if not framework == TensorType.PYTORCH:
           raise NotImplementedError("""Exporting LayoutLM to ONNX is currently only supported for PyTorch."""							)

       if not is_torch_available():
           raise ValueError("""Cannot generate dummy inputs without PyTorch installed."""							)
       import torch

       UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					input_dict["""input_ids"""].shape
       UpperCAmelCase_     :       Optional[int]  		=					torch.tensor([*[box] * seq_length]							).tile(lowerCAmelCase_  ,1  ,1							)
       return input_dict

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:
						lowerCAmelCase__  	=      None

lowerCAmelCase__  	=      logging.get_logger(__name__)
lowerCAmelCase__  	=      {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__	,					lowerCAmelCase__  	=      "[INST]", "[/INST]"
lowerCAmelCase__	,					lowerCAmelCase__  	=      "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowerCAmelCase__  	=      "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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE              =					LlamaTokenizer
	__SCREAMING_SNAKE_CASE              =					'left'
	__SCREAMING_SNAKE_CASE              =					['input_ids', 'attention_mask']


	def __init__(   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_="<unk>"       ,  lowerCAmelCase_="<s>"       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=False       ,  **lowerCAmelCase_       ,  ):
		super().__init__(
		    vocab_file=lowerCAmelCase_       ,  tokenizer_file=lowerCAmelCase_       ,  clean_up_tokenization_spaces=lowerCAmelCase_       ,  unk_token=lowerCAmelCase_       ,  bos_token=lowerCAmelCase_       ,  eos_token=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )
		_lowercase   =add_bos_token
		_lowercase   =add_eos_token
		self.update_post_processor()

		_lowercase   =vocab_file
		_lowercase   =False if not self.vocab_file else True


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.bos_token
		_lowercase   =self.bos_token_id

		_lowercase   =self.eos_token
		_lowercase   =self.eos_token_id

		_lowercase   =F'''{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}'''
		_lowercase   =F'''{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}'''

		_lowercase   =[]
		if self.add_bos_token:
			special_tokens.append((bos, bos_token_id)       )
		if self.add_eos_token:
			special_tokens.append((eos, eos_token_id)       )
		_lowercase   =processors.TemplateProcessing(
		    single=lowerCAmelCase_       ,  pair=lowerCAmelCase_       ,  special_tokens=lowerCAmelCase_       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self._add_eos_token


	@property
	def __lowerCAmelCase      (   self       ):
		return self._add_bos_token


	@add_eos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	@add_bos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 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
		_lowercase   =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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =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...)"       )

		_lowercase   =[]
		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]:
				_lowercase   =(
				    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]:
			_lowercase   =(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




