




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:   Any      		= logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:   Optional[int]      		= {
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}




class  _snake_case		(	UpperCamelCase_							):
			lowerCAmelCase_     :      Any						       =    "nllb-moe"
			lowerCAmelCase_     :      Tuple						       =    ["past_key_values"]
			lowerCAmelCase_     :      List[str]						       =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__(					self				,							a__=128_112				,							a__=1_024				,							a__=12				,							a__=4_096				,							a__=16				,							a__=12				,							a__=4_096				,							a__=16				,							a__=0.0_5				,							a__=0.0_5				,							a__=True				,							a__=True				,							a__="relu"				,							a__=1_024				,							a__=0.1				,							a__=0.1				,							a__=0.0				,							a__=0.0_2				,							a__=2				,							a__=True				,							a__=False				,							a__="float32"				,							a__=False				,							a__=128				,							a__=64				,							a__=4				,							a__=4				,							a__=0.0_0_1				,							a__=0.0_0_1				,							a__="all"				,							a__=False				,							a__=False				,							a__=1.0				,							a__=0.2				,							a__=1				,							a__=0				,							a__=2				,							a__=False				,							**a__				,							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      vocab_size
										snake_case_						=      max_position_embeddings
										snake_case_						=      d_model
										snake_case_						=      encoder_ffn_dim
										snake_case_						=      encoder_layers
										snake_case_						=      encoder_attention_heads
										snake_case_						=      decoder_ffn_dim
										snake_case_						=      decoder_layers
										snake_case_						=      decoder_attention_heads
										snake_case_						=      dropout
										snake_case_						=      attention_dropout
										snake_case_						=      activation_dropout
										snake_case_						=      activation_function
										snake_case_						=      init_std
										snake_case_						=      encoder_layerdrop
										snake_case_						=      decoder_layerdrop
										snake_case_						=      use_cache
										snake_case_						=      encoder_layers
										snake_case_						=      scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case_						=      router_z_loss_coef
										snake_case_						=      router_aux_loss_coef
										snake_case_						=      decoder_sparse_step
										snake_case_						=      encoder_sparse_step
										snake_case_						=      num_experts
										snake_case_						=      expert_capacity
										snake_case_						=      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}'							)
										snake_case_						=      router_dtype

										snake_case_						=      router_ignore_padding_tokens
										snake_case_						=      batch_prioritized_routing
										snake_case_						=      second_expert_policy
										snake_case_						=      normalize_router_prob_before_dropping
										snake_case_						=      moe_eval_capacity_token_fraction
										snake_case_						=      moe_token_dropout
										snake_case_						=      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__				,							)





from typing import TYPE_CHECKING

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


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       Optional[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





