# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCamelCase		:       int									=     logging.get_logger(__name__)

__UpperCamelCase		:       Dict[Optional[str], Type[Formatter]]									=     {}
__UpperCamelCase		:       Dict[Optional[str], str]									=     {}
__UpperCamelCase		:       Dict[Optional[str], Exception]									=     {}
def       A	(    _lowercase		,  _lowercase		,  _lowercase = None		,  ):
						SCREAMING_SNAKE_CASE							:    str							=							aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							formatter_cls
						for alias in set(aliases + [format_type]						):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							format_type
def       A	(    _lowercase		,  _lowercase		,  _lowercase = None						):
						SCREAMING_SNAKE_CASE							:    Dict							=							aliases if aliases is not None else []
						for alias in set(aliases + [format_type]						):
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
					from .torch_formatter import TorchFormatter

					_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
					__UpperCamelCase		:       Tuple									=     ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
					_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
					from .tf_formatter import TFFormatter

					_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
					__UpperCamelCase		:       Dict									=     ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
					_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
					from .jax_formatter import JaxFormatter

					_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
					__UpperCamelCase		:       Any									=     ValueError('JAX needs to be installed to be able to return JAX arrays.')
					_register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def       A	(    _lowercase						):
						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type



def       A	(    _lowercase		,  **_lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							get_format_type_from_alias(_lowercase						)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowercase						)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got '{format_type}'"""						)






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


__UpperCamelCase		:       Any									=     logging.get_logger(__name__)

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





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""nllb-moe"""
						UpperCamelCase_     =		["""past_key_values"""]
						UpperCamelCase_     =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


						def __init__(       self	:				List[str]    ,		UpperCamelCase__	:				List[str]=12_8112    ,		UpperCamelCase__	:				str=1024    ,		UpperCamelCase__	:				Optional[int]=12    ,		UpperCamelCase__	:				Union[str, Any]=4096    ,		UpperCamelCase__	:				Optional[Any]=16    ,		UpperCamelCase__	:				Optional[Any]=12    ,		UpperCamelCase__	:				str=4096    ,		UpperCamelCase__	:				Dict=16    ,		UpperCamelCase__	:				Any=0.05    ,		UpperCamelCase__	:				Any=0.05    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				List[Any]="relu"    ,		UpperCamelCase__	:				Union[str, Any]=1024    ,		UpperCamelCase__	:				Optional[int]=0.1    ,		UpperCamelCase__	:				Any=0.1    ,		UpperCamelCase__	:				Optional[Any]=0.0    ,		UpperCamelCase__	:				str=0.02    ,		UpperCamelCase__	:				Tuple=2    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				Dict=False    ,		UpperCamelCase__	:				Any="float32"    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				Tuple=128    ,		UpperCamelCase__	:				Tuple=64    ,		UpperCamelCase__	:				List[str]=4    ,		UpperCamelCase__	:				Union[str, Any]=4    ,		UpperCamelCase__	:				Dict=0.001    ,		UpperCamelCase__	:				Optional[Any]=0.001    ,		UpperCamelCase__	:				Optional[Any]="all"    ,		UpperCamelCase__	:				Tuple=False    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				List[Any]=1.0    ,		UpperCamelCase__	:				str=0.2    ,		UpperCamelCase__	:				List[Any]=1    ,		UpperCamelCase__	:				List[Any]=0    ,		UpperCamelCase__	:				Optional[int]=2    ,		UpperCamelCase__	:				Tuple=False    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							router_ignore_padding_tokens
												SCREAMING_SNAKE_CASE							:    int							=							batch_prioritized_routing
												SCREAMING_SNAKE_CASE							:    str							=							second_expert_policy
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							normalize_router_prob_before_dropping
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							moe_eval_capacity_token_fraction
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							moe_token_dropout
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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__    ,		)





