




"""simple docstring"""






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
			import torch


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


lowercase__			=		logging.get_logger(__name__)
lowercase__			=		{"""vocab_file""": """spiece.model"""}

lowercase__			=		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowercase__			=		{
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     VOCAB_FILES_NAMES
			lowerCamelCase__ =     PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__ =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__ =     ["""input_ids""", """attention_mask"""]
			def __init__(    self    , lowercase    , lowercase=False    , lowercase=False    , lowercase=False    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase = None    , **lowercase    , ):
							_lowerCamelCase	:     List[str]       =       {} if sp_model_kwargs is None else sp_model_kwargs

							_lowerCamelCase	:     Optional[int]       =       kwargs.get('name_or_path'    )
							if name_or_path is None:
											logger.warning(
											    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
											    ' you are testing the model, this can safely be ignored'    )
											_lowerCamelCase	:     Tuple       =       'None'

							# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
							_lowerCamelCase	:     Tuple       =       '<|endoftext|>' if eos_token is None else eos_token
							_lowerCamelCase	:     Optional[int]       =       '<unk>' if unk_token is None else unk_token
							if "gpt-sw3-7b" in name_or_path:
											_lowerCamelCase	:     Optional[Any]       =       unk_token if pad_token is None else pad_token
											_lowerCamelCase	:     Optional[int]       =       eos_token if bos_token is None else bos_token
							else:
											_lowerCamelCase	:     Optional[int]       =       '<pad>' if pad_token is None else pad_token
											_lowerCamelCase	:     Optional[Any]       =       '<s>' if bos_token is None else bos_token

							super().__init__(
							    do_lower_case=lowercase    , remove_space=lowercase    , keep_accents=lowercase    , bos_token=lowercase    , eos_token=lowercase    , unk_token=lowercase    , pad_token=lowercase    , sp_model_kwargs=self.sp_model_kwargs    , **lowercase    , )

							_lowerCamelCase	:     Optional[Any]       =       do_lower_case
							_lowerCamelCase	:     Optional[Any]       =       remove_space
							_lowerCamelCase	:     Dict       =       keep_accents
							_lowerCamelCase	:     List[Any]       =       vocab_file

							_lowerCamelCase	:     Optional[Any]       =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(lowercase    )

							# Used for whitespace normalization in input texts
							# fmt : off
							_lowerCamelCase	:     str       =       {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
							# fmt : on

							# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
							_lowerCamelCase	:     Optional[Any]       =       re.compile(
							    F'''[{''.join(map(lowercase    , list(range(0    , 9    )    ) + list(range(11    , 32    )    ) + list(range(127    , 160    )    ) + [160, 173, 8203]    )    )}]'''    )
			def __getstate__(    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.__dict__.copy()
							_lowerCamelCase	:     Dict       =       None
							return state
			def __setstate__(    self    , lowercase    ):
							_lowerCamelCase	:     Tuple       =       d

							# for backward compatibility
							if not hasattr(self    , 'sp_model_kwargs'    ):
											_lowerCamelCase	:     str       =       {}

							_lowerCamelCase	:     Any       =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )
			@property
			# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
			def    A_       (    self    ):
							return len(self.sp_model    )
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Dict       =       self.non_printing_characters_re.sub(''    , lowercase    )

							# Normalize whitespaces
							_lowerCamelCase	:     Any       =       ''.join([char if char not in self.whitespaces else ' ' for char in text]    )

							# NFC Unicode normalization
							_lowerCamelCase	:     str       =       unicodedata.normalize('NFC'    , lowercase    )
							return text
			def    A_       (    self    , lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[Any]       =       self.preprocess_text(lowercase    )
							return self.sp_model.encode(lowercase    , out_type=lowercase    )
			def    A_       (    self    , lowercase    ):
							return self.sp_model.PieceToId(lowercase    )
			def    A_       (    self    , lowercase    ):
							return self.sp_model.IdToPiece(lowercase    )
			@staticmethod
			def    A_       (    lowercase    ):
							return out_string
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     str       =       []
							_lowerCamelCase	:     Optional[Any]       =       ''
							_lowerCamelCase	:     Optional[int]       =       False
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
															if not prev_is_special:
																			out_string += " "

															out_string += self.sp_model.decode(lowercase    ) + token
															_lowerCamelCase	:     List[str]       =       True
															_lowerCamelCase	:     str       =       []
											else:
															current_sub_tokens.append(lowercase    )
															_lowerCamelCase	:     Optional[int]       =       False
							out_string += self.sp_model.decode(lowercase    )

							return out_string
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       {self.convert_ids_to_tokens(lowercase    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab
			def    A_       (    self    , lowercase    , lowercase = None    ):
							if not os.path.isdir(lowercase    ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
											return
							_lowerCamelCase	:     List[str]       =       os.path.join(
							    lowercase    , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

							if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase    ) and os.path.isfile(self.vocab_file    ):
											copyfile(self.vocab_file    , lowercase    )
							elif not os.path.isfile(self.vocab_file    ):
											with open(lowercase    , 'wb'    ) as fi:
															_lowerCamelCase	:     Dict       =       self.sp_model.serialized_model_proto()
															fi.write(lowercase    )

							return (out_vocab_file,)
			def    A_       (    self    , lowercase    , lowercase = False    ):

							if isinstance(lowercase    , lowercase    ):
											_lowerCamelCase	:     Optional[Any]       =       self.preprocess_text(lowercase    )
											_lowerCamelCase	:     Optional[int]       =       self.sp_model.encode(lowercase    )
							else:
											_lowerCamelCase	:     str       =       [self.preprocess_text(lowercase    ) for t in text]
											_lowerCamelCase	:     Optional[int]       =       self.sp_model.encode(lowercase    )

							if return_tensors is True or return_tensors == "pt":
											_lowerCamelCase	:     str       =       torch.tensor(lowercase    )

							return token_ids
			def    A_       (    self    , lowercase    ):
							return self.sp_model.decode(lowercase    )






			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Union[str, Any]       =       [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
							_lowerCamelCase	:     Any       =       (
							    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(lowercase    ) + F'''{self.bos_token}Bot:'''
							)
							return self.encode(text=lowercase    )





"""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


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """layoutlmv3"""
			def __init__(    self    , lowercase=50265    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-5    , lowercase=1    , lowercase=0    , lowercase=2    , lowercase=1024    , lowercase=128    , lowercase=128    , lowercase=True    , lowercase=32    , lowercase=128    , lowercase=64    , lowercase=256    , lowercase=True    , lowercase=True    , lowercase=True    , lowercase=224    , lowercase=3    , lowercase=16    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    vocab_size=lowercase    , hidden_size=lowercase    , num_hidden_layers=lowercase    , num_attention_heads=lowercase    , intermediate_size=lowercase    , hidden_act=lowercase    , hidden_dropout_prob=lowercase    , attention_probs_dropout_prob=lowercase    , max_position_embeddings=lowercase    , type_vocab_size=lowercase    , initializer_range=lowercase    , layer_norm_eps=lowercase    , pad_token_id=lowercase    , bos_token_id=lowercase    , eos_token_id=lowercase    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       max_ad_position_embeddings
							_lowerCamelCase	:     Optional[Any]       =       coordinate_size
							_lowerCamelCase	:     int       =       shape_size
							_lowerCamelCase	:     Optional[Any]       =       has_relative_attention_bias
							_lowerCamelCase	:     Dict       =       rel_pos_bins
							_lowerCamelCase	:     Tuple       =       max_rel_pos
							_lowerCamelCase	:     int       =       has_spatial_attention_bias
							_lowerCamelCase	:     Optional[int]       =       rel_ad_pos_bins
							_lowerCamelCase	:     List[Any]       =       max_rel_ad_pos
							_lowerCamelCase	:     Any       =       text_embed
							_lowerCamelCase	:     List[Any]       =       visual_embed
							_lowerCamelCase	:     str       =       input_size
							_lowerCamelCase	:     Optional[Any]       =       num_channels
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Optional[int]       =       classifier_dropout






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     version.parse("""1.12"""		)
			@property
			def    A_       (    self    ):
							# The order of inputs is different for question answering and sequence classification
							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    A_       (    self    ):
							return 1E-5
			@property
			def    A_       (    self    ):
							return 12






			def    A_       (    self    , lowercase    , lowercase = -1    , lowercase = -1    , lowercase = False    , lowercase = None    , lowercase = 3    , lowercase = 40    , lowercase = 40    , ):
							setattr(processor.image_processor    , 'apply_ocr'    , lowercase    )

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

							# Generate dummy bounding boxes
							_lowerCamelCase	:     List[str]       =       [[[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)
							_lowerCamelCase	:     Tuple       =       self._generate_dummy_images(lowercase    , lowercase    , lowercase    , lowercase    )

							_lowerCamelCase	:     str       =       dict(
							    processor(
							        lowercase    , text=lowercase    , boxes=lowercase    , return_tensors=lowercase    , )    )

							return inputs