'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:     int  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Dict  ={
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}




class        __A      ( __A	):
    a__       :						Optional[Any]          =  "switch_transformers"
    a__       :						Tuple          =  ["past_key_values"]
    a__       :						int          =  {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



    def __init__(self    :    Tuple   ,			__a    :    Optional[int]=32128   ,			__a    :    Any=768   ,			__a    :    Optional[Any]=64   ,			__a    :    List[Any]=2048   ,			__a    :    Union[str, Any]=64   ,			__a    :    Union[str, Any]=12   ,			__a    :    List[Any]=3   ,			__a    :    str=12   ,			__a    :    Union[str, Any]=3   ,			__a    :    Tuple=12   ,			__a    :    Dict=8   ,			__a    :    Any=False   ,			__a    :    Dict=0.01   ,			__a    :    Optional[Any]="float32"   ,			__a    :    Optional[int]=False   ,			__a    :    List[str]=32   ,			__a    :    str=128   ,			__a    :    Tuple=0.1   ,			__a    :    List[str]=1E-6   ,			__a    :    Optional[int]=0.0_01   ,			__a    :    Optional[int]=0.0_01   ,			__a    :    Any=1.0   ,			__a    :    Optional[int]="relu"   ,			__a    :    Dict=True   ,			__a    :    Optional[int]=False   ,			__a    :    Dict=True   ,			__a    :    Tuple=0   ,			__a    :    List[Any]=1   ,			**__a    :    Tuple   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   d_model
         UpperCAmelCase_           =   d_kv
         UpperCAmelCase_           =   d_ff

         UpperCAmelCase_           =   num_sparse_encoder_layers

         UpperCAmelCase_           =   num_layers
         UpperCAmelCase_           =   (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCAmelCase_           =   num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
              UpperCAmelCase_           =   self.num_layers // self.num_sparse_encoder_layers
         else:
              UpperCAmelCase_           =   self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
              UpperCAmelCase_           =   self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              UpperCAmelCase_           =   self.num_decoder_layers  # HACK: this will create 0 sparse layers

         UpperCAmelCase_           =   num_heads
         UpperCAmelCase_           =   num_experts
         UpperCAmelCase_           =   expert_capacity
         UpperCAmelCase_           =   router_bias
         UpperCAmelCase_           =   router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}""" )
         UpperCAmelCase_           =   router_dtype

         UpperCAmelCase_           =   router_ignore_padding_tokens
         UpperCAmelCase_           =   relative_attention_num_buckets
         UpperCAmelCase_           =   relative_attention_max_distance

         UpperCAmelCase_           =   dropout_rate
         UpperCAmelCase_           =   layer_norm_epsilon
         UpperCAmelCase_           =   initializer_factor
         UpperCAmelCase_           =   feed_forward_proj
         UpperCAmelCase_           =   use_cache
         UpperCAmelCase_           =   add_router_probs

         UpperCAmelCase_           =   router_z_loss_coef
         UpperCAmelCase_           =   router_aux_loss_coef

         UpperCAmelCase_           =   self.feed_forward_proj.split("-" )
         UpperCAmelCase_           =   act_info[-1]
         UpperCAmelCase_           =   act_info[0] == "gated"

         if len(_UpperCamelCase ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase ) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "\'gated-gelu\' or \'relu\'" )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              UpperCAmelCase_           =   "gelu_new"

         super().__init__(
             pad_token_id=_UpperCamelCase   ,			eos_token_id=_UpperCamelCase   ,			is_encoder_decoder=_UpperCamelCase   ,			**_UpperCamelCase   ,			)







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__)