
'''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 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Optional[Any]	      =			"char"
			lowerCamelCase			:		List[str]	      =			"bpe"
			lowerCamelCase			:		Tuple	      =			"wp"
__lowercase							:   List[str]					=     (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		int	      =			["image_processor", "char_tokenizer"]
			lowerCamelCase			:		Optional[int]	      =			"ViTImageProcessor"
			lowerCamelCase			:		Optional[Any]	      =			"MgpstrTokenizer"

			def __init__(self			,   A=None			,   A=None			,   **A      ):
								lowerCamelCase_					:      Optional[int]     =		None
								if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''			,   A			,   )
													lowerCamelCase_					:      Optional[Any]     =		kwargs.pop('''feature_extractor'''      )

								lowerCamelCase_					:      List[str]     =		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_					:      str     =		tokenizer
								lowerCamelCase_					:      str     =		AutoTokenizer.from_pretrained('''gpt2'''      )
								lowerCamelCase_					:      int     =		AutoTokenizer.from_pretrained('''bert-base-uncased'''      )

								super().__init__(A			,   A      )

			def __call__(self			,   A=None			,   A=None			,   A=None			,   **A      ):
								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_					:      Optional[int]     =		self.image_processor(A			,   return_tensors=A			,   **A      )
								if text is not None:
													lowerCamelCase_					:      Optional[Any]     =		self.char_tokenizer(A			,   return_tensors=A			,   **A      )

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

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      int     =		sequences
								lowerCamelCase_					:      Dict     =		char_preds.size(0      )

								lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		self._decode_helper(A			,   '''char'''      )
								lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		self._decode_helper(A			,   '''bpe'''      )
								lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		self._decode_helper(A			,   '''wp'''      )

								lowerCamelCase_					:      Dict     =		[]
								lowerCamelCase_					:      Any     =		[]
								for i in range(A      ):
													lowerCamelCase_					:      Any     =		[char_scores[i], bpe_scores[i], wp_scores[i]]
													lowerCamelCase_					:      int     =		[char_strs[i], bpe_strs[i], wp_strs[i]]
													lowerCamelCase_					:      List[str]     =		scores.index(max(A      )      )
													final_strs.append(strs[max_score_index]      )
													final_scores.append(scores[max_score_index]      )

								lowerCamelCase_					:      List[Any]     =		{}
								lowerCamelCase_					:      Dict     =		final_strs
								lowerCamelCase_					:      List[Any]     =		final_scores
								lowerCamelCase_					:      List[str]     =		char_strs
								lowerCamelCase_					:      List[str]     =		bpe_strs
								lowerCamelCase_					:      Optional[Any]     =		wp_strs
								return out

			def        UpperCAmelCase__ (self			,   A			,   A      ):
								if format == DecodeType.CHARACTER:
													lowerCamelCase_					:      str     =		self.char_decode
													lowerCamelCase_					:      int     =		1
													lowerCamelCase_					:      List[Any]     =		'''[s]'''
								elif format == DecodeType.BPE:
													lowerCamelCase_					:      Optional[int]     =		self.bpe_decode
													lowerCamelCase_					:      Union[str, Any]     =		2
													lowerCamelCase_					:      Optional[int]     =		'''#'''
								elif format == DecodeType.WORDPIECE:
													lowerCamelCase_					:      Optional[Any]     =		self.wp_decode
													lowerCamelCase_					:      Tuple     =		1_0_2
													lowerCamelCase_					:      str     =		'''[SEP]'''
								else:
													raise ValueError(F"""Format {format} is not supported."""      )

								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		[], []
								lowerCamelCase_					:      Dict     =		pred_logits.size(0      )
								lowerCamelCase_					:      Optional[Any]     =		pred_logits.size(1      )
								lowerCamelCase_,      lowerCamelCase_					:      Any     =		pred_logits.topk(1			,   dim=-1			,   largest=A			,   sorted=A      )
								lowerCamelCase_					:      int     =		preds_index.view(-1			,   A      )[:, 1:]
								lowerCamelCase_					:      str     =		decoder(A      )
								lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		torch.nn.functional.softmax(A			,   dim=2      ).max(dim=2      )
								lowerCamelCase_					:      str     =		preds_max_prob[:, 1:]

								for index in range(A      ):
													lowerCamelCase_					:      int     =		preds_str[index].find(A      )
													lowerCamelCase_					:      Union[str, Any]     =		preds_str[index][:pred_eos]
													lowerCamelCase_					:      List[str]     =		preds_index[index].cpu().tolist()
													lowerCamelCase_					:      List[str]     =		pred_index.index(A      ) if eos_token in pred_index else -1
													lowerCamelCase_					:      str     =		preds_max_prob[index][: pred_eos_index + 1]
													lowerCamelCase_					:      Dict     =		pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
													dec_strs.append(A      )
													conf_scores.append(A      )

								return dec_strs, conf_scores

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[Any]     =		[seq.replace(''' '''			,   ''''''      ) for seq in self.char_tokenizer.batch_decode(A      )]
								return decode_strs

			def        UpperCAmelCase__ (self			,   A      ):
								return self.bpe_tokenizer.batch_decode(A      )






			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Dict     =		[seq.replace(''' '''			,   ''''''      ) for seq in self.wp_tokenizer.batch_decode(A      )]
								return decode_strs




'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase							:   str					=     {
    '''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
    '''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
    '''processing_mctct''': ['''MCTCTProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   str					=     [
					    '''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MCTCTForCTC''',
					    '''MCTCTModel''',
					    '''MCTCTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
					from .feature_extraction_mctct import MCTCTFeatureExtractor
					from .processing_mctct import MCTCTProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
					import sys

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


