



"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
      from ...processing_utils import ProcessorMixin
      from ...utils import TensorType


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

A:   Optional[int]								=		{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Optional[Any]						=						'layoutlmv3'

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=50265      ,     _SCREAMING_SNAKE_CASE=768      ,     _SCREAMING_SNAKE_CASE=12      ,     _SCREAMING_SNAKE_CASE=12      ,     _SCREAMING_SNAKE_CASE=3072      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1E-5      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=1024      ,     _SCREAMING_SNAKE_CASE=128      ,     _SCREAMING_SNAKE_CASE=128      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=32      ,     _SCREAMING_SNAKE_CASE=128      ,     _SCREAMING_SNAKE_CASE=64      ,     _SCREAMING_SNAKE_CASE=256      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=224      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      List[str]:





    '''simple docstring'''




    super().__init__(
        vocab_size=_SCREAMING_SNAKE_CASE      ,     hidden_size=_SCREAMING_SNAKE_CASE      ,     num_hidden_layers=_SCREAMING_SNAKE_CASE      ,     num_attention_heads=_SCREAMING_SNAKE_CASE      ,     intermediate_size=_SCREAMING_SNAKE_CASE      ,     hidden_act=_SCREAMING_SNAKE_CASE      ,     hidden_dropout_prob=_SCREAMING_SNAKE_CASE      ,     attention_probs_dropout_prob=_SCREAMING_SNAKE_CASE      ,     max_position_embeddings=_SCREAMING_SNAKE_CASE      ,     type_vocab_size=_SCREAMING_SNAKE_CASE      ,     initializer_range=_SCREAMING_SNAKE_CASE      ,     layer_norm_eps=_SCREAMING_SNAKE_CASE      ,     pad_token_id=_SCREAMING_SNAKE_CASE      ,     bos_token_id=_SCREAMING_SNAKE_CASE      ,     eos_token_id=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )
    UpperCAmelCase	:					Dict	=	max_ad_position_embeddings
    UpperCAmelCase	:					Union[str, Any]	=	coordinate_size
    UpperCAmelCase	:					str	=	shape_size
    UpperCAmelCase	:					Dict	=	has_relative_attention_bias
    UpperCAmelCase	:					List[Any]	=	rel_pos_bins
    UpperCAmelCase	:					List[Any]	=	max_rel_pos
    UpperCAmelCase	:					Any	=	has_spatial_attention_bias
    UpperCAmelCase	:					Union[str, Any]	=	rel_ad_pos_bins
    UpperCAmelCase	:					Optional[int]	=	max_rel_ad_pos
    UpperCAmelCase	:					List[Any]	=	text_embed
    UpperCAmelCase	:					List[Any]	=	visual_embed
    UpperCAmelCase	:					Union[str, Any]	=	input_size
    UpperCAmelCase	:					Dict	=	num_channels
    UpperCAmelCase	:					Dict	=	patch_size
    UpperCAmelCase	:					str	=	classifier_dropout



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   int						=						version.parse('1.12' )

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





    '''simple docstring'''




    if self.task in ["question-answering", "sequence-classification"]:
     return OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
             ("""bbox""", {0: """batch""", 1: """sequence"""}),
             ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
         ]						)
    else:
     return OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """sequence"""}),
             ("""bbox""", {0: """batch""", 1: """sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
             ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
         ]						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      float:





    '''simple docstring'''




    return 1E-5

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return 12




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 3      ,     _SCREAMING_SNAKE_CASE = 40      ,     _SCREAMING_SNAKE_CASE = 40      ,     )  ->      Mapping[str, Any]:





    '''simple docstring'''




    setattr(processor.image_processor      ,     """apply_ocr"""      ,     _SCREAMING_SNAKE_CASE						)

    # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
    UpperCAmelCase	:					Tuple	=	compute_effective_axis_dimension(
        _SCREAMING_SNAKE_CASE      ,     fixed_dimension=OnnxConfig.default_fixed_batch      ,     num_token_to_add=0						)
    # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
    UpperCAmelCase	:					List[str]	=	processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Union[str, Any]	=	compute_effective_axis_dimension(
        _SCREAMING_SNAKE_CASE      ,     fixed_dimension=OnnxConfig.default_fixed_sequence      ,     num_token_to_add=_SCREAMING_SNAKE_CASE						)
    # Generate dummy inputs according to compute batch and sequence
    UpperCAmelCase	:					Tuple	=	[[""" """.join([processor.tokenizer.unk_token]						) * seq_length]] * batch_size

    # Generate dummy bounding boxes
    UpperCAmelCase	:					Union[str, Any]	=	[[[48, 84, 73, 128]]] * batch_size

    # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
    # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
    UpperCAmelCase	:					Optional[Any]	=	self._generate_dummy_images(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[Any]	=	dict(
        processor(
            _SCREAMING_SNAKE_CASE      ,     text=_SCREAMING_SNAKE_CASE      ,     boxes=_SCREAMING_SNAKE_CASE      ,     return_tensors=_SCREAMING_SNAKE_CASE      ,     )						)

    return inputs

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''tokenizer_file''': '''tokenizer.json'''}

__lowercase							 =					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      VOCAB_FILES_NAMES
    a__	:						Tuple						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						List[str]						=      ["""input_ids""", """attention_mask"""]
    a__	:						int						=      None







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase="<unk>" , __lowercase="<s>" , __lowercase="</s>" , __lowercase="<pad>" , __lowercase=False , __lowercase=False , **__lowercase , )       ->	List[str]:
        super().__init__(
            __lowercase , __lowercase , tokenizer_file=__lowercase , unk_token=__lowercase , bos_token=__lowercase , eos_token=__lowercase , pad_token=__lowercase , add_prefix_space=__lowercase , clean_up_tokenization_spaces=__lowercase , **__lowercase , )
        __UpperCamelCase     :int			  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space''' , __lowercase) != add_prefix_space:
            __UpperCamelCase     :Any			  =		getattr(__lowercase , pre_tok_state.pop('''type'''))
            __UpperCamelCase     :str			  =		add_prefix_space
            __UpperCamelCase     :List[str]			  =		pre_tok_class(**__lowercase)

        __UpperCamelCase     :Tuple			  =		add_prefix_space







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :Tuple			  =		kwargs.get('''is_split_into_words''' , __lowercase)
        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

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







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :List[str]			  =		kwargs.get('''is_split_into_words''' , __lowercase)

        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        __UpperCamelCase     :Optional[Any]			  =		self._tokenizer.model.save(__lowercase , name=__lowercase)
        return tuple(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[int]:
        __UpperCamelCase     :str			  =		[]
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(__lowercase , add_special_tokens=__lowercase) + [self.eos_token_id])

        if len(__lowercase) > self.model_max_length:
            __UpperCamelCase     :Any			  =		input_ids[-self.model_max_length :]
        return input_ids





