



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_    (  UpperCamelCase_			):

						'''simple docstring'''




						UpperCamelCase__     :					Optional[Any]							    =    None
						UpperCamelCase__     :					int							    =    None
						UpperCamelCase__     :					List[str]							    =    None
						UpperCamelCase__     :					Optional[int]							    =    None







class   UpperCAmelCase_    (  UpperCamelCase_			):

						'''simple docstring'''




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







													'''simple docstring'''

													super().__init__(pad_token_id=lowerCamelCase__ ,				bos_token_id=lowerCamelCase__ ,				eos_token_id=lowerCamelCase__ ,				**lowerCamelCase__						)
													__SCREAMING_SNAKE_CASE										=   project_dim
													__SCREAMING_SNAKE_CASE										=   pooler_fn
													__SCREAMING_SNAKE_CASE										=   learn_encoder
													__SCREAMING_SNAKE_CASE										=   use_attention_mask







class   UpperCAmelCase_    (  UpperCamelCase_			):

						'''simple docstring'''




						UpperCamelCase__     :					str							    =    [R'''pooler''', R'''logit_scale''']
						UpperCamelCase__     :					Tuple							    =    [R'''position_ids''', R'''predictions.decoder.bias''']
						UpperCamelCase__     :					Optional[Any]							    =    '''roberta'''
						UpperCamelCase__     :					Any							    =    RobertaSeriesConfig






						def __init__(       self ,				_A						):







													'''simple docstring'''

													super().__init__(lowerCamelCase__						)
													__SCREAMING_SNAKE_CASE										=   XLMRobertaModel(lowerCamelCase__						)
													__SCREAMING_SNAKE_CASE										=   nn.Linear(config.hidden_size ,				config.project_dim						)
													__SCREAMING_SNAKE_CASE										=   getattr(lowerCamelCase__ ,				'has_pre_transformation' ,				lowerCamelCase__						)
													if self.has_pre_transformation:
																				__SCREAMING_SNAKE_CASE										=   nn.Linear(config.hidden_size ,				config.project_dim						)
																				__SCREAMING_SNAKE_CASE										=   nn.LayerNorm(config.hidden_size ,				eps=config.layer_norm_eps						)
													self.post_init()






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







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   return_dict if return_dict is not None else self.config.use_return_dict

													__SCREAMING_SNAKE_CASE										=   self.base_model(
													    input_ids=lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				token_type_ids=lowerCamelCase__ ,				position_ids=lowerCamelCase__ ,				head_mask=lowerCamelCase__ ,				inputs_embeds=lowerCamelCase__ ,				encoder_hidden_states=lowerCamelCase__ ,				encoder_attention_mask=lowerCamelCase__ ,				output_attentions=lowerCamelCase__ ,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states ,				return_dict=lowerCamelCase__ ,				)

													if self.has_pre_transformation:
																				__SCREAMING_SNAKE_CASE										=   outputs['hidden_states'][-2]
																				__SCREAMING_SNAKE_CASE										=   self.pre_LN(lowerCamelCase__						)
																				__SCREAMING_SNAKE_CASE										=   self.transformation_pre(lowerCamelCase__						)

																				return TransformationModelOutput(
																				    projection_state=lowerCamelCase__ ,				last_hidden_state=outputs.last_hidden_state ,				hidden_states=outputs.hidden_states ,				attentions=outputs.attentions ,				)
													else:
																				__SCREAMING_SNAKE_CASE										=   self.transformation(outputs.last_hidden_state						)
																				return TransformationModelOutput(
																				    projection_state=lowerCamelCase__ ,				last_hidden_state=outputs.last_hidden_state ,				hidden_states=outputs.hidden_states ,				attentions=outputs.attentions ,				)












# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser







def      lowerCamelCase_						(			)	->			Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase									=       ArgumentParser('Accelerate CLI tool'					,	usage='accelerate <command> [<args>]'					,	allow_abbrev=UpperCamelCase__ )
				__lowerCamelCase									=       parser.add_subparsers(help='accelerate command helpers' )

				# Register commands
				get_config_parser(subparsers=UpperCamelCase__ )
				env_command_parser(subparsers=UpperCamelCase__ )
				launch_command_parser(subparsers=UpperCamelCase__ )
				tpu_command_parser(subparsers=UpperCamelCase__ )
				test_command_parser(subparsers=UpperCamelCase__ )

				# Let's go
				__lowerCamelCase									=       parser.parse_args()

				if not hasattr(UpperCamelCase__					,	'func' ):
								parser.print_help()
								exit(1 )

				# Run
				args.func(UpperCamelCase__ )


if __name__ == "__main__":
					main()


