



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"""






					A__				:		Optional[Any]			       =    "char"
					A__				:		Any			       =    "bpe"
					A__				:		Dict			       =    "wp"







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

class      __lowerCAmelCase     (		__snake_case      ):
					"""simple docstring"""






					A__				:		Optional[Any]			       =    ["image_processor", "char_tokenizer"]
					A__				:		List[Any]			       =    "ViTImageProcessor"
					A__				:		Optional[Any]			       =    "MgpstrTokenizer"



					def __init__(     self					:       Tuple				,					_snake_case					:       List[str]=None				,					_snake_case					:       Union[str, Any]=None				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								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.'				,					_lowercase				,					)
											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__(_lowercase				,					_lowercase					)



					def __call__(     self					:       int				,					_snake_case					:       int=None				,					_snake_case					:       Tuple=None				,					_snake_case					:       List[Any]=None				,					**_snake_case					:       int					):






								"""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:
											A__       =    self.image_processor(_lowercase				,					return_tensors=_lowercase				,					**_lowercase					)
								if text is not None:
											A__       =    self.char_tokenizer(_lowercase				,					return_tensors=_lowercase				,					**_lowercase					)

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



					def     _a							(     self					:       List[str]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    sequences
								A__       =    char_preds.size(0					)

								A__       =    self._decode_helper(_lowercase				,					'char'					)
								A__       =    self._decode_helper(_lowercase				,					'bpe'					)
								A__       =    self._decode_helper(_lowercase				,					'wp'					)

								A__       =    []
								A__       =    []
								for i in range(_lowercase					):
											A__       =    [char_scores[i], bpe_scores[i], wp_scores[i]]
											A__       =    [char_strs[i], bpe_strs[i], wp_strs[i]]
											A__       =    scores.index(max(_lowercase					)					)
											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     _a							(     self					:       Optional[Any]				,					_snake_case					:       Optional[int]				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								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__       =    1_02
											A__       =    """[SEP]"""
								else:
											raise ValueError(F'''Format {format} is not supported.'''					)

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

								for index in range(_lowercase					):
											A__       =    preds_str[index].find(_lowercase					)
											A__       =    preds_str[index][:pred_eos]
											A__       =    preds_index[index].cpu().tolist()
											A__       =    pred_index.index(_lowercase					) 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(_lowercase					)
											conf_scores.append(_lowercase					)

								return dec_strs, conf_scores



					def     _a							(     self					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    [seq.replace(' '				,					''					) for seq in self.char_tokenizer.batch_decode(_lowercase					)]
								return decode_strs



					def     _a							(     self					:       str				,					_snake_case					:       List[str]					):






								"""simple docstring"""


								return self.bpe_tokenizer.batch_decode(_lowercase					)



					def     _a							(     self					:       Dict				,					_snake_case					:       List[str]					):






								"""simple docstring"""


								A__       =    [seq.replace(' '				,					''					) for seq in self.wp_tokenizer.batch_decode(_lowercase					)]
								return decode_strs









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								return self.tokenizer.batch_decode(*_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return self.tokenizer.decode(*_snake_case				,					**_snake_case					)



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.tokenizer.model_input_names
								A__       =    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




