


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
	)










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






