

import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
			from ... import PreTrainedTokenizerBase, TensorType

a__				=		logging.get_logger(__name__)

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "vision-encoder-decoder"
							UpperCAmelCase__       :						Tuple	    =    True


							def __init__(			self       ,							**_a       )    ->    List[Any]:
												super().__init__(**_a       )
												if "encoder" not in kwargs or "decoder" not in kwargs:
																	raise ValueError(
																	    F"""A configuraton of type {self.model_type} cannot be instantiated because """
																	    F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""       )

												_a	:    Optional[int]         =		kwargs.pop('''encoder'''       )
												_a	:    Optional[Any]         =		encoder_config.pop('''model_type'''       )
												_a	:    List[Any]         =		kwargs.pop('''decoder'''       )
												_a	:    Any         =		decoder_config.pop('''model_type'''       )

												_a	:    List[str]         =		AutoConfig.for_model(_a       ,							**_a       )
												_a	:    Tuple         =		AutoConfig.for_model(_a       ,							**_a       )
												_a	:    Tuple         =		True


							@classmethod
							def 				__lowercase				(			cls       ,							_a       ,							_a       ,							**_a       )    ->    PretrainedConfig:
												logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''       )
												_a	:    Tuple         =		True
												_a	:    int         =		True

												return cls(encoder=encoder_config.to_dict()       ,							decoder=decoder_config.to_dict()       ,							**_a       )




							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Optional[Any]         =		copy.deepcopy(self.__dict__       )
												_a	:    int         =		self.encoder.to_dict()
												_a	:    Optional[Any]         =		self.decoder.to_dict()
												_a	:    Tuple         =		self.__class__.model_type
												return output



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    version.parse("1.11"   )


							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												    ]       )


							@property
							def 				__lowercase				(			self       )    ->    float:
												return 1e-4




							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}       )



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												_a	:    str         =		OrderedDict()
												_a	:    Optional[Any]         =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												_a	:    Any         =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												_a	:    Any         =		{0: '''batch''', 1: '''encoder_sequence'''}

												return common_inputs




							def 				__lowercase				(			self       ,							_a       ,							_a = -1       ,							_a = -1       ,							_a = False       ,							_a = None       ,							)    ->    Mapping[str, Any]:
												import torch

												_a	:    List[Any]         =		OrderedDict()

												_a	:    int         =		super().generate_dummy_inputs(
												    _a       ,							batch_size=_a       ,							seq_length=_a       ,							is_pair=_a       ,							framework=_a       )

												_a      ,					_a	:    Optional[int]         =		dummy_input['''input_ids'''].shape
												_a	:    Union[str, Any]         =		(batch, encoder_sequence, self._config.encoder_hidden_size)
												_a	:    str         =		dummy_input.pop('''input_ids'''       )
												_a	:    Any         =		dummy_input.pop('''attention_mask'''       )
												_a	:    Optional[int]         =		torch.zeros(_a       )

												return common_inputs



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    None:
												pass


							def 				__lowercase				(			self       ,							_a       )    ->    OnnxConfig:
												return VisionEncoderDecoderEncoderOnnxConfig(_a       )




							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = "default"       )    ->    OnnxConfig:
												_a	:    Optional[int]         =		encoder_config.hidden_size
												return VisionEncoderDecoderDecoderOnnxConfig(_a       ,							_a       )






from collections import OrderedDict
from typing import Mapping

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


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_a	:    Dict         =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_a	:    List[Any]         =		{0: '''batch''', 1: '''sequence'''}
												_a	:    Union[str, Any]         =		{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]       )



