




"""simple docstring"""






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
				raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			'''Hello world! cécé herlolip'''





def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str      ,       snake_case			:					bool    ):
					_lowerCAmelCase:Dict									=  FairseqRobertaModel.from_pretrained(A__    )
					roberta.eval()  # disable dropout
					_lowerCAmelCase:int									=  roberta.model.encoder.sentence_encoder
					_lowerCAmelCase:Union[str, Any]									=  XLMRobertaConfig(
					    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,       hidden_size=roberta.cfg.model.encoder_embed_dim      ,       num_hidden_layers=roberta.cfg.model.encoder_layers      ,       num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,       intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,       max_position_embeddings=514      ,       type_vocab_size=1      ,       layer_norm_eps=1e-5      ,       )
					if classification_head:
										_lowerCAmelCase:List[Any]									=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

					print('''Our RoBERTa config:'''      ,       A__    )

					_lowerCAmelCase:Any									=  XLMRobertaXLForSequenceClassification(A__    ) if classification_head else XLMRobertaXLForMaskedLM(A__    )
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					_lowerCAmelCase:List[str]									=  roberta_sent_encoder.embed_tokens.weight
					_lowerCAmelCase:Dict									=  roberta_sent_encoder.embed_positions.weight
					_lowerCAmelCase:Tuple									=  torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.

					_lowerCAmelCase:int									=  roberta_sent_encoder.layer_norm.weight
					_lowerCAmelCase:str									=  roberta_sent_encoder.layer_norm.bias

					for i in range(config.num_hidden_layers    ):
										# Encoder: start of layer
										_lowerCAmelCase:str									=  model.roberta.encoder.layer[i]
										_lowerCAmelCase:str									=  roberta_sent_encoder.layers[i]

										_lowerCAmelCase:Tuple									=  layer.attention
										_lowerCAmelCase:str									=  roberta_layer.self_attn_layer_norm.weight
										_lowerCAmelCase:Dict									=  roberta_layer.self_attn_layer_norm.bias

										# self attention
										_lowerCAmelCase:int									=  layer.attention.self
										assert (
										    roberta_layer.self_attn.k_proj.weight.data.shape
										    == roberta_layer.self_attn.q_proj.weight.data.shape
										    == roberta_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size)    )
										)

										_lowerCAmelCase:List[str]									=  roberta_layer.self_attn.q_proj.weight
										_lowerCAmelCase:int									=  roberta_layer.self_attn.q_proj.bias
										_lowerCAmelCase:Optional[Any]									=  roberta_layer.self_attn.k_proj.weight
										_lowerCAmelCase:str									=  roberta_layer.self_attn.k_proj.bias
										_lowerCAmelCase:List[Any]									=  roberta_layer.self_attn.v_proj.weight
										_lowerCAmelCase:int									=  roberta_layer.self_attn.v_proj.bias

										# self-attention output
										_lowerCAmelCase:str									=  layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										_lowerCAmelCase:int									=  roberta_layer.self_attn.out_proj.weight
										_lowerCAmelCase:Dict									=  roberta_layer.self_attn.out_proj.bias

										# this one is final layer norm
										_lowerCAmelCase:Union[str, Any]									=  roberta_layer.final_layer_norm.weight
										_lowerCAmelCase:Tuple									=  roberta_layer.final_layer_norm.bias

										# intermediate
										_lowerCAmelCase:List[str]									=  layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										_lowerCAmelCase:Optional[int]									=  roberta_layer.fca.weight
										_lowerCAmelCase:str									=  roberta_layer.fca.bias

										# output
										_lowerCAmelCase:Optional[Any]									=  layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										_lowerCAmelCase:Optional[Any]									=  roberta_layer.fca.weight
										_lowerCAmelCase:Any									=  roberta_layer.fca.bias
										# end of layer

					if classification_head:
										_lowerCAmelCase:Dict									=  roberta.model.classification_heads['''mnli'''].dense.weight
										_lowerCAmelCase:Dict									=  roberta.model.classification_heads['''mnli'''].dense.bias
										_lowerCAmelCase:str									=  roberta.model.classification_heads['''mnli'''].out_proj.weight
										_lowerCAmelCase:Optional[Any]									=  roberta.model.classification_heads['''mnli'''].out_proj.bias
					else:
										# LM Head
										_lowerCAmelCase:str									=  roberta.model.encoder.lm_head.dense.weight
										_lowerCAmelCase:Any									=  roberta.model.encoder.lm_head.dense.bias
										_lowerCAmelCase:Any									=  roberta.model.encoder.lm_head.layer_norm.weight
										_lowerCAmelCase:Optional[Any]									=  roberta.model.encoder.lm_head.layer_norm.bias
										_lowerCAmelCase:str									=  roberta.model.encoder.lm_head.weight
										_lowerCAmelCase:Dict									=  roberta.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					_lowerCAmelCase:Tuple									=  roberta.encode(A__    ).unsqueeze(0    )  # batch of size 1

					_lowerCAmelCase:Optional[int]									=  model(A__    )[0]
					if classification_head:
										_lowerCAmelCase:Tuple									=  roberta.model.classification_heads['''mnli'''](roberta.extract_features(A__    )    )
					else:
										_lowerCAmelCase:Any									=  roberta.model(A__    )[0]
					print(our_output.shape      ,       their_output.shape    )
					_lowerCAmelCase:Optional[int]									=  torch.max(torch.abs(our_output - their_output    )    ).item()
					print(F'max_absolute_diff = {max_absolute_diff}'    )  # ~ 1e-7
					_lowerCAmelCase:Any									=  torch.allclose(A__      ,       A__      ,       atol=1e-3    )
					print('''Do both models output the same tensors?'''      ,       '''🔥''' if success else '''💩'''    )
					if not success:
										raise Exception('''Something went wRoNg'''    )

					pathlib.Path(A__    ).mkdir(parents=A__      ,       exist_ok=A__    )
					print(F'Saving model to {pytorch_dump_folder_path}'    )
					model.save_pretrained(A__    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
				)
				UpperCamelCase__						=			parser.parse_args()
				convert_xlm_roberta_xl_checkpoint_to_pytorch(
				    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)












"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__						=			logging.get_logger(__name__)




class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Dict     ,*a__      :	List[str]     ,**a__      :	str)						->       None:




											"""simple docstring"""







											warnings.warn(
											    '''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
											    ''' Please use PoolFormerImageProcessor instead.'''     ,a__     ,)
											super().__init__(*a__     ,**a__)






