



"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


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

__snake_case    :		List[str]  ={
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""xlm-roberta"""



		def __init__(self		,__lowerCamelCase=3_05_22		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=5_12		,__lowerCamelCase=2		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-12		,__lowerCamelCase=1		,__lowerCamelCase=0		,__lowerCamelCase=2		,__lowerCamelCase="absolute"		,__lowerCamelCase=True		,__lowerCamelCase=None		,**__lowerCamelCase		,)					->      Dict:

						"""simple docstring"""






						super().__init__(pad_token_id=lowerCamelCase_		,bos_token_id=lowerCamelCase_		,eos_token_id=lowerCamelCase_		,**lowerCamelCase_			)

						lowerCAmelCase__					:    Tuple												=					vocab_size
						lowerCAmelCase__					:    Optional[Any]												=					hidden_size
						lowerCAmelCase__					:    Union[str, Any]												=					num_hidden_layers
						lowerCAmelCase__					:    Optional[int]												=					num_attention_heads
						lowerCAmelCase__					:    Tuple												=					hidden_act
						lowerCAmelCase__					:    List[Any]												=					intermediate_size
						lowerCAmelCase__					:    List[Any]												=					hidden_dropout_prob
						lowerCAmelCase__					:    Dict												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    Dict												=					max_position_embeddings
						lowerCAmelCase__					:    Optional[Any]												=					type_vocab_size
						lowerCAmelCase__					:    Optional[int]												=					initializer_range
						lowerCAmelCase__					:    Optional[Any]												=					layer_norm_eps
						lowerCAmelCase__					:    Dict												=					position_embedding_type
						lowerCAmelCase__					:    List[Any]												=					use_cache
						lowerCAmelCase__					:    int												=					classifier_dropout



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



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

						"""simple docstring"""






						if self.task == "multiple-choice":
										lowerCAmelCase__					:    Union[str, Any]												=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
										lowerCAmelCase__					:    Any												=					{0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						    ]			)






import flax.linen as nn
import jax
import jax.numpy as jnp



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					nn.Conv(
						    self.out_channels		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					hidden_states.shape
						lowerCAmelCase__					:    Dict												=					jax.image.resize(
						    __lowerCamelCase		,shape=(batch, height * 2, width * 2, channels)		,method='''nearest'''		,)
						lowerCAmelCase__					:    Dict												=					self.conv(__lowerCamelCase			)
						return hidden_states



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					nn.Conv(
						    self.out_channels		,kernel_size=(3, 3)		,strides=(2, 2)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.conv(__lowerCamelCase			)
						return hidden_states



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =None
		snake_case_     =0.0
		snake_case_     =None
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.in_channels if self.out_channels is None else self.out_channels

						lowerCAmelCase__					:    Union[str, Any]												=					nn.GroupNorm(num_groups=32		,epsilon=1e-5			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.Conv(
						    __lowerCamelCase		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

						lowerCAmelCase__					:    int												=					nn.Dense(__lowerCamelCase		,dtype=self.dtype			)

						lowerCAmelCase__					:    List[Any]												=					nn.GroupNorm(num_groups=32		,epsilon=1e-5			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.Dropout(self.dropout_prob			)
						lowerCAmelCase__					:    Optional[int]												=					nn.Conv(
						    __lowerCamelCase		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

						lowerCAmelCase__					:    str												=					self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

						lowerCAmelCase__					:    Union[str, Any]												=					None
						if use_nin_shortcut:
										lowerCAmelCase__					:    Optional[Any]												=					nn.Conv(
										    __lowerCamelCase		,kernel_size=(1, 1)		,strides=(1, 1)		,padding='''VALID'''		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=True			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					hidden_states
						lowerCAmelCase__					:    Dict												=					self.norma(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.swish(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conva(__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					self.time_emb_proj(nn.swish(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					jnp.expand_dims(jnp.expand_dims(__lowerCamelCase		,1			)		,1			)
						lowerCAmelCase__					:    Optional[int]												=					hidden_states + temb

						lowerCAmelCase__					:    Optional[int]												=					self.norma(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					nn.swish(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					self.dropout(__lowerCamelCase		,__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conva(__lowerCamelCase			)

						if self.conv_shortcut is not None:
										lowerCAmelCase__					:    Optional[int]												=					self.conv_shortcut(__lowerCamelCase			)

						return hidden_states + residual

