


'''simple docstring'''


from typing import Mapping

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


lowerCamelCase__     						=  logging.get_logger(__name__)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):
			lowerCAmelCase			:						List[Any]							 =  "mt5"
			lowerCAmelCase			:						List[Any]							 =  ["past_key_values"]




			def __init__(		self	:  Optional[Any]						,	lowerCamelCase__	:  Dict=25_01_12						,	lowerCamelCase__	:  List[Any]=5_12						,	lowerCamelCase__	:  List[str]=64						,	lowerCamelCase__	:  Dict=10_24						,	lowerCamelCase__	:  List[Any]=8						,	lowerCamelCase__	:  Any=None						,	lowerCamelCase__	:  Tuple=6						,	lowerCamelCase__	:  Union[str, Any]=32						,	lowerCamelCase__	:  List[Any]=1_28						,	lowerCamelCase__	:  Optional[int]=0.1						,	lowerCamelCase__	:  str=1E-6						,	lowerCamelCase__	:  Any=1.0						,	lowerCamelCase__	:  Union[str, Any]="gated-gelu"						,	lowerCamelCase__	:  List[str]=True						,	lowerCamelCase__	:  Any=True						,	lowerCamelCase__	:  Optional[int]="T5Tokenizer"						,	lowerCamelCase__	:  Optional[int]=False						,	lowerCamelCase__	:  Optional[Any]=0						,	lowerCamelCase__	:  List[str]=1						,	lowerCamelCase__	:  Dict=0						,	**lowerCamelCase__	:  List[Any]						,	) ->Union[str, Any]:



					'''simple docstring'''







					super().__init__(
					    is_encoder_decoder=lowerCamelCase__						,	tokenizer_class=lowerCamelCase__						,	tie_word_embeddings=lowerCamelCase__						,	pad_token_id=lowerCamelCase__						,	eos_token_id=lowerCamelCase__						,	decoder_start_token_id=lowerCamelCase__						,	**lowerCamelCase__						,	)
					_UpperCAmelCase		:							Tuple             =   vocab_size
					_UpperCAmelCase		:							Optional[int]             =   d_model
					_UpperCAmelCase		:							int             =   d_kv
					_UpperCAmelCase		:							Dict             =   d_ff
					_UpperCAmelCase		:							Union[str, Any]             =   num_layers
					_UpperCAmelCase		:							Optional[int]             =   (
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					_UpperCAmelCase		:							Tuple             =   num_heads
					_UpperCAmelCase		:							Optional[int]             =   relative_attention_num_buckets
					_UpperCAmelCase		:							Optional[Any]             =   relative_attention_max_distance
					_UpperCAmelCase		:							str             =   dropout_rate
					_UpperCAmelCase		:							List[Any]             =   layer_norm_epsilon
					_UpperCAmelCase		:							Optional[Any]             =   initializer_factor
					_UpperCAmelCase		:							Any             =   feed_forward_proj
					_UpperCAmelCase		:							Dict             =   use_cache

					_UpperCAmelCase		:							Union[str, Any]             =   self.feed_forward_proj.split("-"			)
					_UpperCAmelCase		:							List[Any]             =   act_info[-1]
					_UpperCAmelCase		:							Optional[Any]             =   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":
							_UpperCAmelCase		:							Optional[int]             =   "gelu_new"




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->List[Any]:



					'''simple docstring'''







					return self.d_model




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Optional[Any]:



					'''simple docstring'''







					return self.num_heads






			@property
			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->Optional[Any]:



					'''simple docstring'''







					return self.num_layers






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Mapping[str, Mapping[int, str]]:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   {
					    "input_ids": {0: "batch", 1: "encoder_sequence"},
					    "attention_mask": {0: "batch", 1: "encoder_sequence"},
					}
					if self.use_past:
							_UpperCAmelCase		:							Union[str, Any]             =   "past_encoder_sequence + sequence"
							_UpperCAmelCase		:							Union[str, Any]             =   {0: "batch"}
							_UpperCAmelCase		:							Union[str, Any]             =   {0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
							_UpperCAmelCase		:							Dict             =   {0: "batch", 1: "decoder_sequence"}
							_UpperCAmelCase		:							Tuple             =   {0: "batch", 1: "decoder_sequence"}

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

					return common_inputs




			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->int:



					'''simple docstring'''







					return 13






			@property
			def 				lowerCAmelCase__     (		self	:  Any			) ->float:



					'''simple docstring'''







					return 5E-4







'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__     						=  logging.get_logger(__name__)

lowerCamelCase__     						=  {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__						):
			lowerCAmelCase			:						Optional[Any]							 =  "convnext"




			def __init__(		self	:  Optional[int]						,	lowerCamelCase__	:  List[str]=3						,	lowerCamelCase__	:  List[str]=4						,	lowerCamelCase__	:  Optional[int]=4						,	lowerCamelCase__	:  Dict=None						,	lowerCamelCase__	:  Tuple=None						,	lowerCamelCase__	:  List[str]="gelu"						,	lowerCamelCase__	:  List[Any]=0.0_2						,	lowerCamelCase__	:  Dict=1E-12						,	lowerCamelCase__	:  Tuple=1E-6						,	lowerCamelCase__	:  Dict=0.0						,	lowerCamelCase__	:  Optional[int]=2_24						,	lowerCamelCase__	:  List[Any]=None						,	lowerCamelCase__	:  Any=None						,	**lowerCamelCase__	:  int						,	) ->str:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__			)

					_UpperCAmelCase		:							Optional[int]             =   num_channels
					_UpperCAmelCase		:							Tuple             =   patch_size
					_UpperCAmelCase		:							Dict             =   num_stages
					_UpperCAmelCase		:							Optional[int]             =   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
					_UpperCAmelCase		:							Dict             =   [3, 3, 9, 3] if depths is None else depths
					_UpperCAmelCase		:							Union[str, Any]             =   hidden_act
					_UpperCAmelCase		:							Tuple             =   initializer_range
					_UpperCAmelCase		:							Tuple             =   layer_norm_eps
					_UpperCAmelCase		:							Tuple             =   layer_scale_init_value
					_UpperCAmelCase		:							str             =   drop_path_rate
					_UpperCAmelCase		:							str             =   image_size
					_UpperCAmelCase		:							Tuple             =   ["stem"] + [F"""stage{idx}""" for idx in range(1						,	len(self.depths			) + 1			)]
					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   get_aligned_output_features_output_indices(
					    out_features=lowerCamelCase__						,	out_indices=lowerCamelCase__						,	stage_names=self.stage_names			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):
			lowerCAmelCase			:						str							 =  version.parse("1.11"						)




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



					'''simple docstring'''







					return OrderedDict(
					    [
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ]			)






			@property
			def 				lowerCAmelCase__     (		self	:  str			) ->float:



					'''simple docstring'''







					return 1E-5



