






import warnings
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 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					["image_processor", "tokenizer"]
							A_						  =					"FlavaImageProcessor"
							A_						  =					("BertTokenizer", "BertTokenizerFast")

							def __init__(  self:   str		,     __A:   Dict=None		,     __A:   List[str]=None		,     **__A:   Tuple   )    ->				str:
												_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.'''		,     __A		,     )
																	_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`.'''   )

												super().__init__(__A		,     __A   )
												_A								= self.image_processor

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

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''   )

												if text is not None:
																	_A								= self.tokenizer(
																	    text=__A		,     add_special_tokens=__A		,     padding=__A		,     truncation=__A		,     max_length=__A		,     stride=__A		,     pad_to_multiple_of=__A		,     return_token_type_ids=__A		,     return_attention_mask=__A		,     return_overflowing_tokens=__A		,     return_special_tokens_mask=__A		,     return_offsets_mapping=__A		,     return_length=__A		,     verbose=__A		,     return_tensors=__A		,     **__A		,     )
												if images is not None:
																	_A								= self.image_processor(
																	    __A		,     return_image_mask=__A		,     return_codebook_pixels=__A		,     return_tensors=__A		,     **__A		,     )

												if text is not None and images is not None:
																	encoding.update(__A   )
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**__A   )		,     tensor_type=__A   )

							def       __A (  self:   int		,     *__A:   Dict		,     **__A:   Union[str, Any]   )    ->				str:
												return self.tokenizer.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Union[str, Any]		,     *__A:   Optional[Any]		,     **__A:   Tuple   )    ->				Union[str, Any]:
												return self.tokenizer.decode(*__A		,     **__A   )

							@property
							def       __A (  self:   Union[str, Any]   )    ->				str:
												_A								= self.tokenizer.model_input_names
												_A								= self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

							@property
							def       __A (  self:   Optional[int]   )    ->				Optional[int]:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     __A		,     )
												return self.image_processor_class



							@property
							def       __A (  self:   Optional[int]   )    ->				Union[str, Any]:
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,     __A		,     )
												return self.image_processor











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A      =				logging.get_logger(__name__)

__A      =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"roberta"

							def __init__(  self:   Dict		,     __A:   int=5_02_65		,     __A:   Union[str, Any]=7_68		,     __A:   Union[str, Any]=12		,     __A:   str=12		,     __A:   int=30_72		,     __A:   str="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   int=0.1		,     __A:   Optional[int]=5_12		,     __A:   Union[str, Any]=2		,     __A:   str=0.02		,     __A:   str=1e-12		,     __A:   Any=1		,     __A:   str=0		,     __A:   Any=2		,     __A:   Optional[int]="absolute"		,     __A:   Optional[Any]=True		,     __A:   Union[str, Any]=None		,     **__A:   List[str]		,     )    ->				Dict:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= use_cache
												_A								= classifier_dropout





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Dict   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )



