



'''simple docstring'''

from typing import TYPE_CHECKING

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


snake_case_			=     {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CTRLForSequenceClassification""",
	    """CTRLLMHeadModel""",
	    """CTRLModel""",
	    """CTRLPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFCTRLForSequenceClassification""",
	    """TFCTRLLMHeadModel""",
	    """TFCTRLModel""",
	    """TFCTRLPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
	from .tokenization_ctrl import CTRLTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ctrl import (
		    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CTRLForSequenceClassification,
		    CTRLLMHeadModel,
		    CTRLModel,
		    CTRLPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_ctrl import (
		    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCTRLForSequenceClassification,
		    TFCTRLLMHeadModel,
		    TFCTRLModel,
		    TFCTRLPreTrainedModel,
		)

else:
	import sys

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









'''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,					)




