




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Any							=  logging.get_logger(__name__)

__A						:							int							=  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''megatron-bert'''





							def __init__(					self       :	str       ,     a       :	Union[str, Any]=29_056       ,     a       :	Optional[int]=1_024       ,     a       :	str=24       ,     a       :	str=16       ,     a       :	str=4_096       ,     a       :	Any="gelu"       ,     a       :	Any=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Optional[int]=512       ,     a       :	Optional[Any]=2       ,     a       :	List[str]=0.02       ,     a       :	List[Any]=1E-1_2       ,     a       :	Any=0       ,     a       :	List[Any]="absolute"       ,     a       :	List[str]=True       ,     **a       :	Optional[Any]       ,     )			->					List[str]:
										super().__init__(pad_token_id=a       ,     **a			)

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












import warnings

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




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_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."""       ,     a       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_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`."""			)

										super().__init__(a       ,     a			)





							def __call__(					self       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





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





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





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






