




'''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  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      Union[str, Any]						       =    "char"
			lowerCAmelCase_     :      Any						       =    "bpe"
			lowerCAmelCase_     :      Optional[Any]						       =    "wp"

_SCREAMING_SNAKE_CASE							:   Optional[int]      		= (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      str						       =    ["image_processor", "char_tokenizer"]
			lowerCAmelCase_     :      int						       =    "ViTImageProcessor"
			lowerCAmelCase_     :      str						       =    "MgpstrTokenizer"




			def __init__(					self				,							a__=None				,							a__=None				,							**a__							)				->				Any:

										'''simple docstring'''




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

										snake_case_						=      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`."							)

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

										super().__init__(__lowerCAmelCase				,							__lowerCAmelCase							)




			def __call__(					self				,							a__=None				,							a__=None				,							a__=None				,							**a__							)				->				Dict:

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

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




			def    lowerCAmelCase__				(					self				,							a__							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_					,      snake_case_					,      snake_case_						=      sequences
										snake_case_						=      char_preds.size(0							)

										snake_case_					,      snake_case_						=      self._decode_helper(__lowerCAmelCase				,							"char"							)
										snake_case_					,      snake_case_						=      self._decode_helper(__lowerCAmelCase				,							"bpe"							)
										snake_case_					,      snake_case_						=      self._decode_helper(__lowerCAmelCase				,							"wp"							)

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

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




			def    lowerCAmelCase__				(					self				,							a__				,							a__							)				->				List[str]:

										'''simple docstring'''




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

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

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

										return dec_strs, conf_scores




			def    lowerCAmelCase__				(					self				,							a__							)				->				Tuple:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							a__							)				->				Union[str, Any]:

										'''simple docstring'''




										return self.bpe_tokenizer.batch_decode(__lowerCAmelCase							)




			def    lowerCAmelCase__				(					self				,							a__							)				->				Dict:

										'''simple docstring'''




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











from typing import List, Optional, Union

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






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						["""image_processor""", """tokenizer"""]
			lowerCAmelCase_							     =						"""BlipImageProcessor"""
			lowerCAmelCase_							     =						("""BertTokenizer""", """BertTokenizerFast""")





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		False
				super().__init__(__lowerCAmelCase       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		self.image_processor





			def __call__(    self       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = 0       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''							)

				# Get only text
				if images is None:
					lowerCamelCase__								=		self.tokenizer
					lowerCamelCase__								=		self.tokenizer(
					    text=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  truncation=__lowerCAmelCase       ,  max_length=__lowerCAmelCase       ,  stride=__lowerCAmelCase       ,  pad_to_multiple_of=__lowerCAmelCase       ,  return_attention_mask=__lowerCAmelCase       ,  return_overflowing_tokens=__lowerCAmelCase       ,  return_special_tokens_mask=__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  return_token_type_ids=__lowerCAmelCase       ,  return_length=__lowerCAmelCase       ,  verbose=__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )
					return text_encoding

				# add pixel_values
				lowerCamelCase__								=		self.image_processor(__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase							)

				if text is not None:
					lowerCamelCase__								=		self.tokenizer(
					    text=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  truncation=__lowerCAmelCase       ,  max_length=__lowerCAmelCase       ,  stride=__lowerCAmelCase       ,  pad_to_multiple_of=__lowerCAmelCase       ,  return_attention_mask=__lowerCAmelCase       ,  return_overflowing_tokens=__lowerCAmelCase       ,  return_special_tokens_mask=__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  return_token_type_ids=__lowerCAmelCase       ,  return_length=__lowerCAmelCase       ,  verbose=__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )
				else:
					lowerCamelCase__								=		None

				if text_encoding is not None:
					encoding_image_processor.update(__lowerCAmelCase							)

				return encoding_image_processor





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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



