


import warnings

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





class 					_a							(				lowercase__):
	_a  :  Any          =							["""image_processor""", """tokenizer"""]
	_a  :  Union[str, Any]          =							"""ViTImageProcessor"""
	_a  :  int          =							("""CLIPTokenizer""", """CLIPTokenizerFast""")




	def __init__(					self						:		Dict		,					_SCREAMING_SNAKE_CASE						:		Tuple=None		,					_SCREAMING_SNAKE_CASE						:		Optional[int]=None		,					**_SCREAMING_SNAKE_CASE						:		Optional[int]		)->       Union[str, Any]:
			lowerCAmelCase__   :			str         =					None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''		,					lowercase_		,					)
					lowerCAmelCase__   :			Dict         =					kwargs.pop('''feature_extractor'''		)

			lowerCAmelCase__   :			Dict         =					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__(lowercase_		,					lowercase_		)




	def __call__(					self						:		Dict		,					_SCREAMING_SNAKE_CASE						:		str=None		,					_SCREAMING_SNAKE_CASE						:		Any=None		,					_SCREAMING_SNAKE_CASE						:		str=None		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		,					**_SCREAMING_SNAKE_CASE						:		List[Any]		)->       Optional[int]:
			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:
					lowerCAmelCase__   :			Any         =					self.tokenizer(lowercase_		,					return_tensors=lowercase_		,					**lowercase_		)

			if visual_prompt is not None:
					lowerCAmelCase__   :			Optional[int]         =					self.image_processor(lowercase_		,					return_tensors=lowercase_		,					**lowercase_		)

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

			if visual_prompt is not None and images is not None:
					lowerCAmelCase__   :			List[Any]         =					{
					    "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:
					lowerCAmelCase__   :			Tuple         =					image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			elif visual_prompt is not None:
					lowerCAmelCase__   :			Optional[Any]         =					{
					    "conditional_pixel_values": prompt_features.pixel_values,
					}
					return encoding
			else:
					return BatchEncoding(data=dict(**lowercase_		)		,					tensor_type=lowercase_		)




	def 							UpperCAmelCase__(					self						:		Optional[int]		,					*_SCREAMING_SNAKE_CASE						:		Union[str, Any]		,					**_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->       Optional[Any]:
			return self.tokenizer.batch_decode(*lowercase_		,					**lowercase_		)




	def 							UpperCAmelCase__(					self						:		Optional[int]		,					*_SCREAMING_SNAKE_CASE						:		List[str]		,					**_SCREAMING_SNAKE_CASE						:		List[str]		)->       Optional[Any]:
			return self.tokenizer.decode(*lowercase_		,					**lowercase_		)




	@property
	def 							UpperCAmelCase__(					self						:		Union[str, Any]		)->       Any:
			warnings.warn(
			    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,					lowercase_		,					)
			return self.image_processor_class




	@property
	def 							UpperCAmelCase__(					self						:		Dict		)->       Any:
			warnings.warn(
			    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,					lowercase_		,					)
			return self.image_processor






"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a		       =							{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""albert"""







  def __init__(		self				,   lowercase_=3_0000				,   lowercase_=128				,   lowercase_=4096				,   lowercase_=12				,   lowercase_=1				,   lowercase_=64				,   lowercase_=1_6384				,   lowercase_=1				,   lowercase_="gelu_new"				,   lowercase_=0				,   lowercase_=0				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=0.1				,   lowercase_="absolute"				,   lowercase_=0				,   lowercase_=2				,   lowercase_=3				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_				,   bos_token_id=lowercase_				,   eos_token_id=lowercase_				,   **lowercase_  )

     UpperCAmelCase_					:					int			     =    vocab_size
     UpperCAmelCase_					:					Optional[int]			     =    embedding_size
     UpperCAmelCase_					:					List[str]			     =    hidden_size
     UpperCAmelCase_					:					Optional[int]			     =    num_hidden_layers
     UpperCAmelCase_					:					Union[str, Any]			     =    num_hidden_groups
     UpperCAmelCase_					:					Dict			     =    num_attention_heads
     UpperCAmelCase_					:					Any			     =    inner_group_num
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_act
     UpperCAmelCase_					:					Union[str, Any]			     =    intermediate_size
     UpperCAmelCase_					:					List[str]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Union[str, Any]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    max_position_embeddings
     UpperCAmelCase_					:					Any			     =    type_vocab_size
     UpperCAmelCase_					:					List[str]			     =    initializer_range
     UpperCAmelCase_					:					Optional[int]			     =    layer_norm_eps
     UpperCAmelCase_					:					List[Any]			     =    classifier_dropout_prob
     UpperCAmelCase_					:					Tuple			     =    position_embedding_type




class A_	(lowercase__      ):




  '''simple docstring'''







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_					:					int			     =    {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]  )


