




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    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =None
						lowerCAmelCase__			      =None
						lowerCAmelCase__			      =None
						lowerCAmelCase__			      =None



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Tuple					,							snake_case__	:   Optional[Any]=1					,							snake_case__	:   List[str]=0					,							snake_case__	:   List[Any]=2					,							snake_case__	:   Union[str, Any]=5_12					,							snake_case__	:   Union[str, Any]="cls"					,							snake_case__	:   Tuple=False					,							snake_case__	:   List[str]=True					,							**snake_case__	:   Union[str, Any]					,							):

								"""simple docstring"""

								super().__init__(pad_token_id=snake_case__					,							bos_token_id=snake_case__					,							eos_token_id=snake_case__					,							**snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							project_dim
								SCREAMING_SNAKE_CASE_					      =							pooler_fn
								SCREAMING_SNAKE_CASE_					      =							learn_encoder
								SCREAMING_SNAKE_CASE_					      =							use_attention_mask



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =[r"pooler", r"logit_scale"]
						lowerCAmelCase__			      =[r"position_ids", r"predictions.decoder.bias"]
						lowerCAmelCase__			      ="roberta"
						lowerCAmelCase__			      =RobertaSeriesConfig



						def __init__(			self	:   int					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								super().__init__(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							XLMRobertaModel(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							nn.Linear(config.hidden_size					,							config.project_dim  )
								SCREAMING_SNAKE_CASE_					      =							getattr(snake_case__					,							'has_pre_transformation'					,							snake_case__  )
								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	:   Tuple					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[torch.Tensor] = None					,							snake_case__	:   Optional[bool] = None					,							snake_case__	:   Optional[bool] = None					,							snake_case__	:   Optional[bool] = 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=snake_case__					,							attention_mask=snake_case__					,							token_type_ids=snake_case__					,							position_ids=snake_case__					,							head_mask=snake_case__					,							inputs_embeds=snake_case__					,							encoder_hidden_states=snake_case__					,							encoder_attention_mask=snake_case__					,							output_attentions=snake_case__					,							output_hidden_states=True if self.has_pre_transformation else output_hidden_states					,							return_dict=snake_case__					,							)

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

										return TransformationModelOutput(
										    projection_state=snake_case__					,							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=snake_case__					,							last_hidden_state=outputs.last_hidden_state					,							hidden_states=outputs.hidden_states					,							attentions=outputs.attentions					,							)





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
						)
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)