





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'char'
     __magic_name__   =			'bpe'
     __magic_name__   =			'wp'




__A							:			int         =			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['image_processor', 'char_tokenizer']
     __magic_name__   =			'ViTImageProcessor'
     __magic_name__   =			'MgpstrTokenizer'




     def __init__(    self				,     snake_case_=None				,     snake_case_=None				,     **snake_case_							):
         _A     =							None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'				,     snake_case_				,     )
             _A     =							kwargs.pop('feature_extractor'							)

         _A     =							image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'							)
         if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'							)

         _A     =							tokenizer
         _A     =							AutoTokenizer.from_pretrained('gpt2'							)
         _A     =							AutoTokenizer.from_pretrained('bert-base-uncased'							)

         super().__init__(snake_case_				,     snake_case_							)




     def __call__(    self				,     snake_case_=None				,     snake_case_=None				,     snake_case_=None				,     **snake_case_							):
         if images is None and text is None:
             raise ValueError('You need to specify either an `images` or `text` input to process.'							)

         if images is not None:
             _A     =							self.image_processor(snake_case_				,     return_tensors=snake_case_				,     **snake_case_							)
         if text is not None:
             _A     =							self.char_tokenizer(snake_case_				,     return_tensors=snake_case_				,     **snake_case_							)

         if text is None:
             return inputs
         elif images is None:
             return encodings
         else:
             _A     =							encodings['input_ids']
             return inputs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A,			_A,			_A     =							sequences
         _A     =							char_preds.size(0							)

         _A,			_A     =							self._decode_helper(snake_case_				,     'char'							)
         _A,			_A     =							self._decode_helper(snake_case_				,     'bpe'							)
         _A,			_A     =							self._decode_helper(snake_case_				,     'wp'							)

         _A     =							[]
         _A     =							[]
         for i in range(snake_case_							):
             _A     =							[char_scores[i], bpe_scores[i], wp_scores[i]]
             _A     =							[char_strs[i], bpe_strs[i], wp_strs[i]]
             _A     =							scores.index(max(snake_case_							)							)
             final_strs.append(strs[max_score_index]							)
             final_scores.append(scores[max_score_index]							)

         _A     =							{}
         _A     =							final_strs
         _A     =							final_scores
         _A     =							char_strs
         _A     =							bpe_strs
         _A     =							wp_strs
         return out




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         if format == DecodeType.CHARACTER:
             _A     =							self.char_decode
             _A     =							1
             _A     =							'[s]'
         elif format == DecodeType.BPE:
             _A     =							self.bpe_decode
             _A     =							2
             _A     =							'#'
         elif format == DecodeType.WORDPIECE:
             _A     =							self.wp_decode
             _A     =							102
             _A     =							'[SEP]'
         else:
             raise ValueError(F"Format {format} is not supported."							)

         _A,			_A     =							[], []
         _A     =							pred_logits.size(0							)
         _A     =							pred_logits.size(1							)
         _A,			_A     =							pred_logits.topk(1				,     dim=-1				,     largest=snake_case_				,     sorted=snake_case_							)
         _A     =							preds_index.view(-1				,     snake_case_							)[:, 1:]
         _A     =							decoder(snake_case_							)
         _A,			_A     =							torch.nn.functional.softmax(snake_case_				,     dim=2							).max(dim=2							)
         _A     =							preds_max_prob[:, 1:]

         for index in range(snake_case_							):
             _A     =							preds_str[index].find(snake_case_							)
             _A     =							preds_str[index][:pred_eos]
             _A     =							preds_index[index].cpu().tolist()
             _A     =							pred_index.index(snake_case_							) if eos_token in pred_index else -1
             _A     =							preds_max_prob[index][: pred_eos_index + 1]
             _A     =							pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
             dec_strs.append(snake_case_							)
             conf_scores.append(snake_case_							)

         return dec_strs, conf_scores




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							[seq.replace(' '				,     ''							) for seq in self.char_tokenizer.batch_decode(snake_case_							)]
         return decode_strs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return self.bpe_tokenizer.batch_decode(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							[seq.replace(' '				,     ''							) for seq in self.wp_tokenizer.batch_decode(snake_case_							)]
         return decode_strs











'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case : str  				=						{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[str]  				=						['LayoutLMv3TokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Optional[int]  				=						[
				    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LayoutLMv3ForQuestionAnswering',
				    'LayoutLMv3ForSequenceClassification',
				    'LayoutLMv3ForTokenClassification',
				    'LayoutLMv3Model',
				    'LayoutLMv3PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLayoutLMv3ForQuestionAnswering',
				    'TFLayoutLMv3ForSequenceClassification',
				    'TFLayoutLMv3ForTokenClassification',
				    'TFLayoutLMv3Model',
				    'TFLayoutLMv3PreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
				from .configuration_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LayoutLMvaConfig,
				    LayoutLMvaOnnxConfig,
				)
				from .processing_layoutlmva import LayoutLMvaProcessor
				from .tokenization_layoutlmva import LayoutLMvaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlmva import (
								    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMvaForQuestionAnswering,
								    LayoutLMvaForSequenceClassification,
								    LayoutLMvaForTokenClassification,
								    LayoutLMvaModel,
								    LayoutLMvaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlmva import (
								    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMvaForQuestionAnswering,
								    TFLayoutLMvaForSequenceClassification,
								    TFLayoutLMvaForTokenClassification,
								    TFLayoutLMvaModel,
								    TFLayoutLMvaPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
								from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
				import sys

				_snake_case : Any  				=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

