


'''simple docstring'''
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__magic_name__   :   int  							=logging.get_logger(__name__)

__magic_name__   :   Optional[int]  							={
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[str]         =     '''longt5'''
						UpperCAmelCase__	:       Dict         =     ['''past_key_values''']
						UpperCAmelCase__	:       Any         =     {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
						def __init__( self				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]=3_21_28	,				_lowerCamelCase				:					Optional[int]=5_12	,				_lowerCamelCase				:					str=64	,				_lowerCamelCase				:					List[Any]=20_48	,				_lowerCamelCase				:					Optional[int]=6	,				_lowerCamelCase				:					Optional[Any]=None	,				_lowerCamelCase				:					List[str]=8	,				_lowerCamelCase				:					Dict=1_27	,				_lowerCamelCase				:					List[str]=16	,				_lowerCamelCase				:					Dict=32	,				_lowerCamelCase				:					int=1_28	,				_lowerCamelCase				:					Any=0.1	,				_lowerCamelCase				:					Optional[Any]=1e-6	,				_lowerCamelCase				:					str=1.0	,				_lowerCamelCase				:					List[Any]="relu"	,				_lowerCamelCase				:					Optional[int]=True	,				_lowerCamelCase				:					Dict="local"	,				_lowerCamelCase				:					List[str]=True	,				_lowerCamelCase				:					Optional[Any]=0	,				_lowerCamelCase				:					List[str]=1	,				**_lowerCamelCase				:					Tuple	,				)							->					str:
										__magic_name__	  = vocab_size
										__magic_name__	  = d_model
										__magic_name__	  = d_kv
										__magic_name__	  = d_ff
										__magic_name__	  = num_layers
										# default = symmetry
										__magic_name__	  = num_decoder_layers if num_decoder_layers is not None else self.num_layers
										__magic_name__	  = num_heads
										__magic_name__	  = local_radius
										__magic_name__	  = global_block_size
										__magic_name__	  = relative_attention_num_buckets
										__magic_name__	  = relative_attention_max_distance
										__magic_name__	  = dropout_rate
										__magic_name__	  = layer_norm_epsilon
										__magic_name__	  = initializer_factor
										__magic_name__	  = feed_forward_proj
										__magic_name__	  = encoder_attention_type
										__magic_name__	  = use_cache

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

										if len(_lowerCamelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCamelCase  ) > 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":
														__magic_name__	  = "gelu_new"

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






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						@property
						def 					__A					( self				:					Union[str, Any]  )							->					Mapping[str, Mapping[int, str]]:
										__magic_name__	  = {
										    "input_ids": {0: "batch", 1: "encoder_sequence"},
										    "attention_mask": {0: "batch", 1: "encoder_sequence"},
										}
										if self.use_past:
														__magic_name__	  = "past_encoder_sequence + sequence"
														__magic_name__	  = {0: "batch"}
														__magic_name__	  = {0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
														__magic_name__	  = {0: "batch", 1: "decoder_sequence"}
														__magic_name__	  = {0: "batch", 1: "decoder_sequence"}

										if self.use_past:
														self.fill_with_past_key_values_(_lowerCamelCase	,				direction="inputs"  )

										return common_inputs

						@property
						def 					__A					( self				:					Optional[Any]  )							->					int:
										return 13







'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__magic_name__   :   int  							=logging.get_logger(__name__)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					List[str]	,				*_lowerCamelCase				:					List[str]	,				**_lowerCamelCase				:					Optional[Any]  )							->					None:
										warnings.warn(
										    "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConvNextImageProcessor instead."	,				_lowerCamelCase	,				)
										super().__init__(*_lowerCamelCase	,				**_lowerCamelCase  )



