code: 
  
  
  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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
  	raise Exception('''requires fairseq >= 0.9.0''')
  
  
  logging.set_verbosity_info()
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  lowerCAmelCase					 =	'''Hello world! cécé herlolip'''
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    FairseqRobertaModel.from_pretrained(lowercase__			)
  		roberta.eval()  # disable dropout
  		__lowercase=    roberta.model.encoder.sentence_encoder
  		__lowercase=    RobertaConfig(
  		    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,				hidden_size=roberta.args.encoder_embed_dim     ,				num_hidden_layers=roberta.args.encoder_layers     ,				num_attention_heads=roberta.args.encoder_attention_heads     ,				intermediate_size=roberta.args.encoder_ffn_embed_dim     ,				max_position_embeddings=5_1_4     ,				type_vocab_size=1     ,				layer_norm_eps=1E-5     ,				)
  		if classification_head:
  				__lowercase=    roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
  		print('Our BERT config:'     ,				lowercase__			)
  
  		__lowercase=    RobertaForSequenceClassification(lowercase__			) if classification_head else RobertaForMaskedLM(lowercase__			)
  		model.eval()
  
  		# Now let's copy all the weights.
  		# Embeddings
  		__lowercase=    roberta_sent_encoder.embed_tokens.weight
  		__lowercase=    roberta_sent_encoder.embed_positions.weight
  		__lowercase=    torch.zeros_like(
  		    model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.
  		__lowercase=    roberta_sent_encoder.emb_layer_norm.weight
  		__lowercase=    roberta_sent_encoder.emb_layer_norm.bias
  
  		for i in range(config.num_hidden_layers			):
  				# Encoder: start of layer
  				__lowercase=    model.roberta.encoder.layer[i]
  				__lowercase=    roberta_sent_encoder.layers[i]
  
  				# self attention
  				__lowercase=    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)			)
  				)
  
  				__lowercase=    roberta_layer.self_attn.q_proj.weight
  				__lowercase=    roberta_layer.self_attn.q_proj.bias
  				__lowercase=    roberta_layer.self_attn.k_proj.weight
  				__lowercase=    roberta_layer.self_attn.k_proj.bias
  				__lowercase=    roberta_layer.self_attn.v_proj.weight
  				__lowercase=    roberta_layer.self_attn.v_proj.bias
  
  				# self-attention output
  				__lowercase=    layer.attention.output
  				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  				__lowercase=    roberta_layer.self_attn.out_proj.weight
  				__lowercase=    roberta_layer.self_attn.out_proj.bias
  				__lowercase=    roberta_layer.self_attn_layer_norm.weight
  				__lowercase=    roberta_layer.self_attn_layer_norm.bias
  
  				# intermediate
  				__lowercase=    layer.intermediate
  				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  				__lowercase=    roberta_layer.fca.weight
  				__lowercase=    roberta_layer.fca.bias
  
  				# output
  				__lowercase=    layer.output
  				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  				__lowercase=    roberta_layer.fca.weight
  				__lowercase=    roberta_layer.fca.bias
  				__lowercase=    roberta_layer.final_layer_norm.weight
  				__lowercase=    roberta_layer.final_layer_norm.bias
  				# end of layer
  
  		if classification_head:
  				__lowercase=    roberta.model.classification_heads['mnli'].dense.weight
  				__lowercase=    roberta.model.classification_heads['mnli'].dense.bias
  				__lowercase=    roberta.model.classification_heads['mnli'].out_proj.weight
  				__lowercase=    roberta.model.classification_heads['mnli'].out_proj.bias
  		else:
  				# LM Head
  				__lowercase=    roberta.model.encoder.lm_head.dense.weight
  				__lowercase=    roberta.model.encoder.lm_head.dense.bias
  				__lowercase=    roberta.model.encoder.lm_head.layer_norm.weight
  				__lowercase=    roberta.model.encoder.lm_head.layer_norm.bias
  				__lowercase=    roberta.model.encoder.lm_head.weight
  				__lowercase=    roberta.model.encoder.lm_head.bias
  
  		# Let's check that we get the same results.
  		__lowercase=    roberta.encode(lowercase__			).unsqueeze(0			)  # batch of size 1
  
  		__lowercase=    model(lowercase__			)[0]
  		if classification_head:
  				__lowercase=    roberta.model.classification_heads['mnli'](roberta.extract_features(lowercase__			)			)
  		else:
  				__lowercase=    roberta.model(lowercase__			)[0]
  		print(our_output.shape     ,				their_output.shape			)
  		__lowercase=    torch.max(torch.abs(our_output - their_output			)			).item()
  		print(F'max_absolute_diff = {max_absolute_diff}'			)  # ~ 1e-7
  		__lowercase=    torch.allclose(lowercase__     ,				lowercase__     ,				atol=1E-3			)
  		print('Do both models output the same tensors?'     ,				'🔥' if success else '💩'			)
  		if not success:
  				raise Exception('Something went wRoNg'			)
  
  		pathlib.Path(lowercase__			).mkdir(parents=lowercase__     ,				exist_ok=lowercase__			)
  		print(F'Saving model to {pytorch_dump_folder_path}'			)
  		model.save_pretrained(lowercase__			)
  
  
  if __name__ == "__main__":
  	lowerCAmelCase					 =	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.'''
  	)
  	lowerCAmelCase					 =	parser.parse_args()
  	convert_roberta_checkpoint_to_pytorch(
  	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  	)
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  