



'''simple docstring'''

import warnings

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


class 	a__     (				_lowercase	):
	__magic_name__		:    List[Any]	    =     ["image_processor", "tokenizer"]
	__magic_name__		:    str	    =     "ViTImageProcessor"
	__magic_name__		:    Optional[Any]	    =     ("CLIPTokenizer", "CLIPTokenizerFast")


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.''',					__UpperCAmelCase,					)
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= kwargs.pop('''feature_extractor'''      )

					SCREAMING_SNAKE_CASE  :						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__(__UpperCAmelCase,					__UpperCAmelCase      )


	def __call__(self : List[str],					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : int=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Union[str, Any]=None,					**__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					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  :						Tuple      					= self.tokenizer(__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )

					if visual_prompt is not None:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.image_processor(__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )

					if images is not None:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.image_processor(__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )

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


	def 				lowercase__  (self : Tuple,					*__UpperCAmelCase : List[str],					**__UpperCAmelCase : List[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Union[str, Any],					*__UpperCAmelCase : List[Any],					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

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


	@property
	def 				lowercase__  (self : Optional[int]      )     ->			List[Any]:

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',					__UpperCAmelCase,					)
					return self.image_processor_class






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

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',					__UpperCAmelCase,					)
					return self.image_processor









'''simple docstring'''

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Optional[Any]	    =     "nllb-moe"
	__magic_name__		:    Optional[Any]	    =     ["past_key_values"]
	__magic_name__		:    Optional[int]	    =     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


	def __init__(self : int,					__UpperCAmelCase : Tuple=128112,					__UpperCAmelCase : Any=1024,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : Optional[int]=4096,					__UpperCAmelCase : Any=16,					__UpperCAmelCase : Any=12,					__UpperCAmelCase : Optional[Any]=4096,					__UpperCAmelCase : Optional[int]=16,					__UpperCAmelCase : List[Any]=0.05,					__UpperCAmelCase : Dict=0.05,					__UpperCAmelCase : Dict=True,					__UpperCAmelCase : List[Any]=True,					__UpperCAmelCase : Any="relu",					__UpperCAmelCase : Union[str, Any]=1024,					__UpperCAmelCase : Optional[int]=0.1,					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : List[Any]=0.0,					__UpperCAmelCase : Optional[int]=0.02,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : int=True,					__UpperCAmelCase : int=False,					__UpperCAmelCase : int="float32",					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : List[str]=128,					__UpperCAmelCase : Dict=64,					__UpperCAmelCase : Dict=4,					__UpperCAmelCase : Optional[Any]=4,					__UpperCAmelCase : Optional[Any]=0.001,					__UpperCAmelCase : Optional[Any]=0.001,					__UpperCAmelCase : Optional[Any]="all",					__UpperCAmelCase : List[str]=False,					__UpperCAmelCase : Dict=False,					__UpperCAmelCase : Any=1.0,					__UpperCAmelCase : Dict=0.2,					__UpperCAmelCase : int=1,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : Any=2,					__UpperCAmelCase : Union[str, Any]=False,					**__UpperCAmelCase : Dict,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= vocab_size
					SCREAMING_SNAKE_CASE  :						int      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= d_model
					SCREAMING_SNAKE_CASE  :						Dict      					= encoder_ffn_dim
					SCREAMING_SNAKE_CASE  :						int      					= encoder_layers
					SCREAMING_SNAKE_CASE  :						str      					= encoder_attention_heads
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= decoder_ffn_dim
					SCREAMING_SNAKE_CASE  :						str      					= decoder_layers
					SCREAMING_SNAKE_CASE  :						Dict      					= decoder_attention_heads
					SCREAMING_SNAKE_CASE  :						Dict      					= dropout
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						Dict      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= activation_function
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= init_std
					SCREAMING_SNAKE_CASE  :						Any      					= encoder_layerdrop
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= decoder_layerdrop
					SCREAMING_SNAKE_CASE  :						Dict      					= use_cache
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= encoder_layers
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= scale_embedding  # scale factor will be sqrt(d_model) if True
					SCREAMING_SNAKE_CASE  :						List[str]      					= router_z_loss_coef
					SCREAMING_SNAKE_CASE  :						List[str]      					= router_aux_loss_coef
					SCREAMING_SNAKE_CASE  :						int      					= decoder_sparse_step
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= encoder_sparse_step
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= num_experts
					SCREAMING_SNAKE_CASE  :						List[Any]      					= expert_capacity
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= router_bias
					if router_dtype not in ["float32", "float16", "bfloat16"]:
									raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= router_dtype

					SCREAMING_SNAKE_CASE  :						int      					= router_ignore_padding_tokens
					SCREAMING_SNAKE_CASE  :						Dict      					= batch_prioritized_routing
					SCREAMING_SNAKE_CASE  :						Dict      					= second_expert_policy
					SCREAMING_SNAKE_CASE  :						Tuple      					= normalize_router_prob_before_dropping
					SCREAMING_SNAKE_CASE  :						List[str]      					= moe_eval_capacity_token_fraction
					SCREAMING_SNAKE_CASE  :						List[Any]      					= moe_token_dropout
					SCREAMING_SNAKE_CASE  :						List[Any]      					= output_router_logits
					super().__init__(
					    pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					is_encoder_decoder=__UpperCAmelCase,					decoder_start_token_id=__UpperCAmelCase,					**__UpperCAmelCase,					)




