
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a  			=					logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:bool         =	field(default=UpperCAmelCase ,						metadata={'help': 'Whether to use SortishSampler or not.'}							)
	a			:bool         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}							)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
	            'to the `max_length` value of the model configuration.'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
	            'to the `num_beams` value of the model configuration.'
	        )
	    } ,						)
	a			:Optional[Union[str, Path, GenerationConfig]]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
	    } ,						)
	def 						_lowercase  (       self  :			Dict       )       ->     List[Any]:
					lowercase_       =					super().to_dict()
					for k, v in d.items():
									if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
													lowercase_       =					v.to_dict()
					return d







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 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						super().__init__(__snake_case					,  __snake_case					)





			def __call__(		self					,  __snake_case					,  __snake_case = None					,  __snake_case = True					,  __snake_case = False					,  __snake_case = None					,  __snake_case = None					,  __snake_case = 0					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = True					,  __snake_case = None					,  **__snake_case					,  )   -> BatchEncoding:




						'''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 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''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					)					)






