





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

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






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  '''nllb-moe'''
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_   :      Any							=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




       def __init__(						self		,SCREAMING_SNAKE_CASE__=12_81_12		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=0.0_5		,SCREAMING_SNAKE_CASE__=0.0_5		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__="relu"		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__="float32"		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=1_28		,SCREAMING_SNAKE_CASE__=64		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=0.0_0_1		,SCREAMING_SNAKE_CASE__=0.0_0_1		,SCREAMING_SNAKE_CASE__="all"		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=1.0		,SCREAMING_SNAKE_CASE__=0.2		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,**SCREAMING_SNAKE_CASE__		,)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  vocab_size
             __SCREAMING_SNAKE_CASE   :Any						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Tuple						=  d_model
             __SCREAMING_SNAKE_CASE   :int						=  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE   :List[Any]						=  encoder_layers
             __SCREAMING_SNAKE_CASE   :Any						=  encoder_attention_heads
             __SCREAMING_SNAKE_CASE   :List[Any]						=  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE   :str						=  decoder_layers
             __SCREAMING_SNAKE_CASE   :Tuple						=  decoder_attention_heads
             __SCREAMING_SNAKE_CASE   :int						=  dropout
             __SCREAMING_SNAKE_CASE   :List[Any]						=  attention_dropout
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  activation_dropout
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  activation_function
             __SCREAMING_SNAKE_CASE   :List[Any]						=  init_std
             __SCREAMING_SNAKE_CASE   :List[Any]						=  encoder_layerdrop
             __SCREAMING_SNAKE_CASE   :List[Any]						=  decoder_layerdrop
             __SCREAMING_SNAKE_CASE   :List[Any]						=  use_cache
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  encoder_layers
             __SCREAMING_SNAKE_CASE   :Union[str, 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   :Tuple						=  decoder_sparse_step
             __SCREAMING_SNAKE_CASE   :Tuple						=  encoder_sparse_step
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  num_experts
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  expert_capacity
             __SCREAMING_SNAKE_CASE   :str						=  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   :int						=  router_dtype

             __SCREAMING_SNAKE_CASE   :Tuple						=  router_ignore_padding_tokens
             __SCREAMING_SNAKE_CASE   :str						=  batch_prioritized_routing
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  second_expert_policy
             __SCREAMING_SNAKE_CASE   :Any						=  normalize_router_prob_before_dropping
             __SCREAMING_SNAKE_CASE   :Tuple						=  moe_eval_capacity_token_fraction
             __SCREAMING_SNAKE_CASE   :str						=  moe_token_dropout
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  output_router_logits
             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		["DPTFeatureExtractor"]
  lowerCamelCase_								=		["DPTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DPTForDepthEstimation",
      "DPTForSemanticSegmentation",
      "DPTModel",
      "DPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)