





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__  :     Dict   			=logging.get_logger(__name__)

UpperCAmelCase__  :     List[str]   			="Hello world! cécé herlolip"





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					str:
				lowerCamelCase			=FairseqRobertaModel.from_pretrained(__A			)
				roberta.eval()  # disable dropout
				lowerCamelCase			=roberta.model.encoder.sentence_encoder
				lowerCamelCase			=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=5_14     , type_vocab_size=1     , layer_norm_eps=1e-5     , )
				if classification_head:
								lowerCamelCase			=roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

				print("""Our RoBERTa config:"""     , __A			)

				lowerCamelCase			=XLMRobertaXLForSequenceClassification(__A			) if classification_head else XLMRobertaXLForMaskedLM(__A			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				lowerCamelCase			=roberta_sent_encoder.embed_tokens.weight
				lowerCamelCase			=roberta_sent_encoder.embed_positions.weight
				lowerCamelCase			=torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.

				lowerCamelCase			=roberta_sent_encoder.layer_norm.weight
				lowerCamelCase			=roberta_sent_encoder.layer_norm.bias

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

								lowerCamelCase			=layer.attention
								lowerCamelCase			=roberta_layer.self_attn_layer_norm.weight
								lowerCamelCase			=roberta_layer.self_attn_layer_norm.bias

								# self attention
								lowerCamelCase			=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			=roberta_layer.self_attn.q_proj.weight
								lowerCamelCase			=roberta_layer.self_attn.q_proj.bias
								lowerCamelCase			=roberta_layer.self_attn.k_proj.weight
								lowerCamelCase			=roberta_layer.self_attn.k_proj.bias
								lowerCamelCase			=roberta_layer.self_attn.v_proj.weight
								lowerCamelCase			=roberta_layer.self_attn.v_proj.bias

								# self-attention output
								lowerCamelCase			=layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								lowerCamelCase			=roberta_layer.self_attn.out_proj.weight
								lowerCamelCase			=roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								lowerCamelCase			=roberta_layer.final_layer_norm.weight
								lowerCamelCase			=roberta_layer.final_layer_norm.bias

								# intermediate
								lowerCamelCase			=layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								lowerCamelCase			=roberta_layer.fca.weight
								lowerCamelCase			=roberta_layer.fca.bias

								# output
								lowerCamelCase			=layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								lowerCamelCase			=roberta_layer.fca.weight
								lowerCamelCase			=roberta_layer.fca.bias
								# end of layer

				if classification_head:
								lowerCamelCase			=roberta.model.classification_heads["""mnli"""].dense.weight
								lowerCamelCase			=roberta.model.classification_heads["""mnli"""].dense.bias
								lowerCamelCase			=roberta.model.classification_heads["""mnli"""].out_proj.weight
								lowerCamelCase			=roberta.model.classification_heads["""mnli"""].out_proj.bias
				else:
								# LM Head
								lowerCamelCase			=roberta.model.encoder.lm_head.dense.weight
								lowerCamelCase			=roberta.model.encoder.lm_head.dense.bias
								lowerCamelCase			=roberta.model.encoder.lm_head.layer_norm.weight
								lowerCamelCase			=roberta.model.encoder.lm_head.layer_norm.bias
								lowerCamelCase			=roberta.model.encoder.lm_head.weight
								lowerCamelCase			=roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				lowerCamelCase			=roberta.encode(__A			).unsqueeze(0			)  # batch of size 1

				lowerCamelCase			=model(__A			)[0]
				if classification_head:
								lowerCamelCase			=roberta.model.classification_heads["""mnli"""](roberta.extract_features(__A			)			)
				else:
								lowerCamelCase			=roberta.model(__A			)[0]
				print(our_output.shape     , their_output.shape			)
				lowerCamelCase			=torch.max(torch.abs(our_output - their_output			)			).item()
				print(F"""max_absolute_diff = {max_absolute_diff}"""			)  # ~ 1e-7
				lowerCamelCase			=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__  :     List[str]   			=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__  :     str   			=parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__  :     Union[str, Any]   			=logging.get_logger(__name__)

UpperCAmelCase__  :     Any   			={
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class  __A       (  a      ):
				__A								=			"""vit_msn"""


				def __init__(						self				,	UpperCAmelCase_=768				,	UpperCAmelCase_=12				,	UpperCAmelCase_=12				,	UpperCAmelCase_=3072				,	UpperCAmelCase_="gelu"				,	UpperCAmelCase_=0.0				,	UpperCAmelCase_=0.0				,	UpperCAmelCase_=0.0_2				,	UpperCAmelCase_=1E-06				,	UpperCAmelCase_=224				,	UpperCAmelCase_=16				,	UpperCAmelCase_=3				,	UpperCAmelCase_=True				,	**UpperCAmelCase_				,	):
								super().__init__(**UpperCAmelCase_							)

								lowerCamelCase			=hidden_size
								lowerCamelCase			=num_hidden_layers
								lowerCamelCase			=num_attention_heads
								lowerCamelCase			=intermediate_size
								lowerCamelCase			=hidden_act
								lowerCamelCase			=hidden_dropout_prob
								lowerCamelCase			=attention_probs_dropout_prob
								lowerCamelCase			=initializer_range
								lowerCamelCase			=layer_norm_eps
								lowerCamelCase			=image_size
								lowerCamelCase			=patch_size
								lowerCamelCase			=num_channels
								lowerCamelCase			=qkv_bias





