



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 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Optional[torch.FloatTensor] =       None
							A				:torch.FloatTensor =       None
							A				:Optional[Tuple[torch.FloatTensor]] =       None
							A				:Optional[Tuple[torch.FloatTensor]] =       None





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase=1 ,					__UpperCAmelCase=0 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=512 ,					__UpperCAmelCase="cls" ,					__UpperCAmelCase=False ,					__UpperCAmelCase=True ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											super().__init__(pad_token_id=__UpperCAmelCase ,					bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					**__UpperCAmelCase				)
											a__	:							Optional[Any]      						= project_dim
											a__	:							Optional[int]      						= pooler_fn
											a__	:							Optional[int]      						= learn_encoder
											a__	:							str      						= use_attention_mask





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Dict =       [R"pooler", R"logit_scale"]
							A				:Tuple =       [R"position_ids", R"predictions.decoder.bias"]
							A				:Optional[int] =       "roberta"
							A				:Tuple =       RobertaSeriesConfig



							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= XLMRobertaModel(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= nn.Linear(config.hidden_size ,					config.project_dim				)
											a__	:							Any      						= getattr(__UpperCAmelCase ,					"has_pre_transformation" ,					__UpperCAmelCase				)
											if self.has_pre_transformation:
															a__	:							int      						= nn.Linear(config.hidden_size ,					config.project_dim				)
															a__	:							int      						= nn.LayerNorm(config.hidden_size ,					eps=config.layer_norm_eps				)
											self.post_init()




							def 		_A	(							self ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= return_dict if return_dict is not None else self.config.use_return_dict

											a__	:							List[Any]      						= 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:
															a__	:							List[Any]      						= outputs["hidden_states"][-2]
															a__	:							str      						= self.pre_LN(__UpperCAmelCase				)
															a__	:							Optional[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:
															a__	:							Union[str, 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 ,					)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


