


from typing import Mapping

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


A :	Optional[int]												=     logging.get_logger(__name__)

A :	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 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				'''longt5'''
					A__    =				['''past_key_values''']
					A__    =				{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

					def __init__(self					:		Tuple   ,      _UpperCAmelCase					:		Union[str, Any]=3_2128   ,      _UpperCAmelCase					:		Tuple=512   ,      _UpperCAmelCase					:		Optional[Any]=64   ,      _UpperCAmelCase					:		Tuple=2048   ,      _UpperCAmelCase					:		str=6   ,      _UpperCAmelCase					:		Optional[Any]=None   ,      _UpperCAmelCase					:		Optional[Any]=8   ,      _UpperCAmelCase					:		Union[str, Any]=127   ,      _UpperCAmelCase					:		str=16   ,      _UpperCAmelCase					:		Dict=32   ,      _UpperCAmelCase					:		Optional[int]=128   ,      _UpperCAmelCase					:		List[str]=0.1   ,      _UpperCAmelCase					:		List[Any]=1E-6   ,      _UpperCAmelCase					:		Optional[int]=1.0   ,      _UpperCAmelCase					:		Any="relu"   ,      _UpperCAmelCase					:		Tuple=True   ,      _UpperCAmelCase					:		Any="local"   ,      _UpperCAmelCase					:		int=True   ,      _UpperCAmelCase					:		Optional[int]=0   ,      _UpperCAmelCase					:		Optional[int]=1   ,      **_UpperCAmelCase					:		List[Any]   ,      )		->     Optional[int]:




							"""simple docstring"""


							lowercase__      =	vocab_size
							lowercase__      =	d_model
							lowercase__      =	d_kv
							lowercase__      =	d_ff
							lowercase__      =	num_layers
							# default = symmetry
							lowercase__      =	num_decoder_layers if num_decoder_layers is not None else self.num_layers
							lowercase__      =	num_heads
							lowercase__      =	local_radius
							lowercase__      =	global_block_size
							lowercase__      =	relative_attention_num_buckets
							lowercase__      =	relative_attention_max_distance
							lowercase__      =	dropout_rate
							lowercase__      =	layer_norm_epsilon
							lowercase__      =	initializer_factor
							lowercase__      =	feed_forward_proj
							lowercase__      =	encoder_attention_type
							lowercase__      =	use_cache

							lowercase__      =	self.feed_forward_proj.split("""-"""	)
							lowercase__      =	act_info[-1]
							lowercase__      =	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":
									lowercase__      =	"""gelu_new"""

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






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def 						lowerCamelCase__  (self					:		Dict	)		->     Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							lowercase__      =	{
							    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
							    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
							}
							if self.use_past:
									lowercase__      =	"""past_encoder_sequence + sequence"""
									lowercase__      =	{0: """batch"""}
									lowercase__      =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
							else:
									lowercase__      =	{0: """batch""", 1: """decoder_sequence"""}
									lowercase__      =	{0: """batch""", 1: """decoder_sequence"""}

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

							return common_inputs


					@property
					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     int:




							"""simple docstring"""


							return 13





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A :	Optional[Any]												=     logging.get_logger(__name__)



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      *_UpperCAmelCase					:		Union[str, Any]   ,      **_UpperCAmelCase					:		Any	)		->     None:




							"""simple docstring"""


							warnings.warn(
							    """The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV1ImageProcessor instead."""   ,      _UpperCAmelCase   ,      )
							super().__init__(*_UpperCAmelCase   ,      **_UpperCAmelCase	)

