import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Tuple			 =						['''image_processor''', '''tokenizer''']
			__a  : List[str]			 =						'''CLIPImageProcessor'''
			__a  : List[str]			 =						('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')





			def __init__(   self      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							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__      ,   )
											__lowercase 			=							kwargs.pop('''feature_extractor'''						)

							__lowercase 			=							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__(lowerCAmelCase__      ,   lowerCAmelCase__						)





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

							'''simple docstring'''


							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:
											__lowercase 			=							self.tokenizer(lowerCAmelCase__      ,   return_tensors=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							if images is not None:
											__lowercase 			=							self.image_processor(lowerCAmelCase__      ,   return_tensors=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							if text is not None and images is not None:
											__lowercase 			=							image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**lowerCAmelCase__						)      ,   tensor_type=lowerCAmelCase__						)





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

							'''simple docstring'''


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





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

							'''simple docstring'''


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





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.tokenizer.model_input_names
							__lowercase 			=							self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)
from collections import OrderedDict
from typing import Mapping

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

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




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)