
'''simple docstring'''



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   UpperCAmelCase_					(		lowercase__			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       'char'
						_lowercase		:				Optional[Any]									=       'bpe'
						_lowercase		:				List[str]									=       'wp'






_lowercase    =		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class   UpperCAmelCase_					(		lowercase__			):






						'''simple docstring'''






						_lowercase		:				Tuple									=       ['image_processor', 'char_tokenizer']
						_lowercase		:				Optional[int]									=       'ViTImageProcessor'
						_lowercase		:				Tuple									=       'MgpstrTokenizer'




						def __init__( self   ,       _lowercase=None   ,       _lowercase=None   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			None
												if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""   ,       _UpperCamelCase   ,       )
																		_lowerCAmelCase  						=			kwargs.pop("""feature_extractor"""				)

												_lowerCAmelCase  						=			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`."""				)

												_lowerCAmelCase  						=			tokenizer
												_lowerCAmelCase  						=			AutoTokenizer.from_pretrained("""gpt2"""				)
												_lowerCAmelCase  						=			AutoTokenizer.from_pretrained("""bert-base-uncased"""				)

												super().__init__(_UpperCamelCase   ,       _UpperCamelCase				)




						def __call__( self   ,       _lowercase=None   ,       _lowercase=None   ,       _lowercase=None   ,       **_lowercase				):

												"""simple docstring"""



												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:
																		_lowerCAmelCase  						=			self.image_processor(_UpperCamelCase   ,       return_tensors=_UpperCamelCase   ,       **_UpperCamelCase				)
												if text is not None:
																		_lowerCAmelCase  						=			self.char_tokenizer(_UpperCamelCase   ,       return_tensors=_UpperCamelCase   ,       **_UpperCamelCase				)

												if text is None:
																		return inputs
												elif images is None:
																		return encodings
												else:
																		_lowerCAmelCase  						=			encodings["""input_ids"""]
																		return inputs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			sequences
												_lowerCAmelCase  						=			char_preds.size(0				)

												_lowerCAmelCase  						=			self._decode_helper(_UpperCamelCase   ,       """char"""				)
												_lowerCAmelCase  						=			self._decode_helper(_UpperCamelCase   ,       """bpe"""				)
												_lowerCAmelCase  						=			self._decode_helper(_UpperCamelCase   ,       """wp"""				)

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

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




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



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

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

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

												return dec_strs, conf_scores




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[seq.replace(""" """   ,       """"""				) for seq in self.char_tokenizer.batch_decode(_UpperCamelCase				)]
												return decode_strs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												return self.bpe_tokenizer.batch_decode(_UpperCamelCase				)




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[seq.replace(""" """   ,       """"""				) for seq in self.wp_tokenizer.batch_decode(_UpperCamelCase				)]
												return decode_strs


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    =		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TableTransformerForObjectDetection""",
						    """TableTransformerModel""",
						    """TableTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_table_transformer import (
						    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TableTransformerConfig,
						    TableTransformerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_table_transformer import (
												    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TableTransformerForObjectDetection,
												    TableTransformerModel,
												    TableTransformerPreTrainedModel,
												)

else:
						import sys

						_lowercase    =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
