





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowerCAmelCase_    =    logging.getLogger(__name__)



@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[float]	      =				field(
			    default=0.0,					metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			lowerCamelCase_					:					bool	      =				field(default=_a,					metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			lowerCamelCase_					:					bool	      =				field(default=_a,					metadata={'''help''': '''whether to use adafactor'''}					)
			lowerCamelCase_					:					Optional[float]	      =				field(
			    default=_a,					metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			lowerCamelCase_					:					Optional[float]	      =				field(
			    default=_a,					metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			lowerCamelCase_					:					Optional[float]	      =				field(default=_a,					metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			lowerCamelCase_					:					Optional[float]	      =				field(
			    default=_a,					metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			lowerCamelCase_					:					Optional[str]	      =				field(
			    default='''linear''',					metadata={'''help''': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}"},					)







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''image_processor''', '''tokenizer''']
			lowerCamelCase_					:					List[Any]	      =				'''BlipImageProcessor'''
			lowerCamelCase_					:					Union[str, Any]	      =				'''AutoTokenizer'''






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )

							# add QFormer tokenizer
							snake_case_						:   Optional[Any]           =  qformer_tokenizer






			def __call__(self ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = 0 ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = True ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



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

							snake_case_						:   Tuple           =  BatchFeature()

							if text is not None:
											snake_case_						:   Tuple           =  self.tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											encoding.update(__magic_name__   )
											snake_case_						:   Optional[Any]           =  self.qformer_tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											snake_case_						:   Optional[int]           =  qformer_text_encoding.pop('''input_ids'''   )
											snake_case_						:   Tuple           =  qformer_text_encoding.pop('''attention_mask'''   )

							if images is not None:
											snake_case_						:   Any           =  self.image_processor(__magic_name__ ,							return_tensors=__magic_name__   )
											encoding.update(__magic_name__   )

							return encoding






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



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






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer.model_input_names
							snake_case_						:   List[Any]           =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							if os.path.isfile(__magic_name__   ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(__magic_name__ ,							exist_ok=__magic_name__   )
							snake_case_						:   Any           =  os.path.join(__magic_name__ ,							'''qformer_tokenizer'''   )
							self.qformer_tokenizer.save_pretrained(__magic_name__   )
							return super().save_pretrained(__magic_name__ ,							**__magic_name__   )






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  AutoTokenizer.from_pretrained(__magic_name__ ,							subfolder='''qformer_tokenizer'''   )
							snake_case_						:   str           =  cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__   )
							args.append(__magic_name__   )
							return cls(*__magic_name__   )
