




"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=None
		UpperCamelCase							=None
		UpperCamelCase							=None
		UpperCamelCase							=None


class   UpperCAmelCase_    (			snake_case		):
		def __init__(						self				,						UpperCamelCase_=1				,						UpperCamelCase_=0				,						UpperCamelCase_=2				,						UpperCamelCase_=5_12				,						UpperCamelCase_="cls"				,						UpperCamelCase_=False				,						UpperCamelCase_=True				,						**UpperCamelCase_				,						)						->       List[Any]:
					super().__init__(pad_token_id=UpperCamelCase_				,						bos_token_id=UpperCamelCase_				,						eos_token_id=UpperCamelCase_				,						**UpperCamelCase_       )
					__lowercase			:  Optional[Any]								=       project_dim
					__lowercase			:  Union[str, Any]								=       pooler_fn
					__lowercase			:  List[Any]								=       learn_encoder
					__lowercase			:  Union[str, Any]								=       use_attention_mask


class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=[r"pooler", r"logit_scale"]
		UpperCamelCase							=[r"position_ids", r"predictions.decoder.bias"]
		UpperCamelCase							="roberta"
		UpperCamelCase							=RobertaSeriesConfig
		def __init__(						self				,						UpperCamelCase_       )						->       Dict:
					super().__init__(UpperCamelCase_       )
					__lowercase			:  Optional[int]								=       XLMRobertaModel(UpperCamelCase_       )
					__lowercase			:  Union[str, Any]								=       nn.Linear(config.hidden_size				,						config.project_dim       )
					__lowercase			:  str								=       getattr(UpperCamelCase_				,						'''has_pre_transformation'''				,						UpperCamelCase_       )
					if self.has_pre_transformation:
								__lowercase			:  int								=       nn.Linear(config.hidden_size				,						config.project_dim       )
								__lowercase			:  Optional[Any]								=       nn.LayerNorm(config.hidden_size				,						eps=config.layer_norm_eps       )
					self.post_init()
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						)						->       Tuple:
					__lowercase			:  Optional[Any]								=       return_dict if return_dict is not None else self.config.use_return_dict

					__lowercase			:  int								=       self.base_model(
					    input_ids=UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_				,						position_ids=UpperCamelCase_				,						head_mask=UpperCamelCase_				,						inputs_embeds=UpperCamelCase_				,						encoder_hidden_states=UpperCamelCase_				,						encoder_attention_mask=UpperCamelCase_				,						output_attentions=UpperCamelCase_				,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states				,						return_dict=UpperCamelCase_				,						)

					if self.has_pre_transformation:
								__lowercase			:  Any								=       outputs['''hidden_states'''][-2]
								__lowercase			:  Dict								=       self.pre_LN(UpperCamelCase_       )
								__lowercase			:  Union[str, Any]								=       self.transformation_pre(UpperCamelCase_       )

								return TransformationModelOutput(
								    projection_state=UpperCamelCase_				,						last_hidden_state=outputs.last_hidden_state				,						hidden_states=outputs.hidden_states				,						attentions=outputs.attentions				,						)
					else:
								__lowercase			:  List[Any]								=       self.transformation(outputs.last_hidden_state       )
								return TransformationModelOutput(
								    projection_state=UpperCamelCase_				,						last_hidden_state=outputs.last_hidden_state				,						hidden_states=outputs.hidden_states				,						attentions=outputs.attentions				,						)







"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




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





