






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					(						a_ ,						a_ ,						a_	)	->			List[str]:
							lowerCAmelCase_										=     FairseqRobertaModel.from_pretrained(a_	)
							roberta.eval()  # disable dropout
							lowerCAmelCase_										=     roberta.model.encoder.sentence_encoder
							lowerCAmelCase_										=     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=514 ,						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 BERT config:' ,						a_	)

							lowerCAmelCase_										=     RobertaForSequenceClassification(a_	) if classification_head else RobertaForMaskedLM(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.emb_layer_norm.weight
							lowerCAmelCase_										=     roberta_sent_encoder.emb_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]

														# 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
														lowerCAmelCase_										=     roberta_layer.self_attn_layer_norm.weight
														lowerCAmelCase_										=     roberta_layer.self_attn_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
														lowerCAmelCase_										=     roberta_layer.final_layer_norm.weight
														lowerCAmelCase_										=     roberta_layer.final_layer_norm.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__":
		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
		)











import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_=1.0   )					->					Any:
														'''simple docstring'''

														super().__init__()

														lowerCAmelCase_										=     cut_size
														lowerCAmelCase_										=     cut_power






							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_						,   lowerCAmelCase_										=     pixel_values.shape[2:4]
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_			,			self.cut_size   )
														lowerCAmelCase_										=     []
														for _ in range(lowercase_   ):
																					lowerCAmelCase_										=     int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
																					lowerCAmelCase_										=     torch.randint(0			,			sideX - size + 1			,			()   )
																					lowerCAmelCase_										=     torch.randint(0			,			sideY - size + 1			,			()   )
																					lowerCAmelCase_										=     pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
																					cutouts.append(F.adaptive_avg_poolad(lowercase_			,			self.cut_size   )   )
														return torch.cat(lowercase_   )


def      lowerCamelCase					(						a_ ,						a_	)	->			Optional[int]:
							lowerCAmelCase_										=     F.normalize(a_ ,						dim=-1	)
							lowerCAmelCase_										=     F.normalize(a_ ,						dim=-1	)
							return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)
def      lowerCamelCase					(						a_ ,						a_	)	->			int:
							for param in model.parameters():
														lowerCAmelCase_										=     value




class 							a_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)					->					str:
														'''simple docstring'''

														super().__init__()
														self.register_modules(
														    vae=lowercase_			,			text_encoder=lowercase_			,			clip_model=lowercase_			,			tokenizer=lowercase_			,			unet=lowercase_			,			scheduler=lowercase_			,			feature_extractor=lowercase_			,			)

														lowerCAmelCase_										=     transforms.Normalize(mean=feature_extractor.image_mean			,			std=feature_extractor.image_std   )
														lowerCAmelCase_										=     (
														    feature_extractor.size
														    if isinstance(feature_extractor.size			,			lowercase_   )
														    else feature_extractor.size['shortest_edge']
														)
														lowerCAmelCase_										=     MakeCutouts(self.cut_out_size   )

														set_requires_grad(self.text_encoder			,			lowercase_   )
														set_requires_grad(self.clip_model			,			lowercase_   )

							def      _lowercase       (		self			,			lowercase_ = "auto"   )					->					Union[str, Any]:
														'''simple docstring'''

														if slice_size == "auto":
																					# half the attention head size is usually a good trade-off between
																					# speed and memory
																					lowerCAmelCase_										=     self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(lowercase_   )

							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														self.enable_attention_slicing(lowercase_   )

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														set_requires_grad(self.vae			,			lowercase_   )

							def      _lowercase       (		self   )					->					List[str]:
														'''simple docstring'''

														set_requires_grad(self.vae			,			lowercase_   )

							def      _lowercase       (		self   )					->					List[str]:
														'''simple docstring'''

														set_requires_grad(self.unet			,			lowercase_   )

							def      _lowercase       (		self   )					->					Tuple:
														'''simple docstring'''

														set_requires_grad(self.unet			,			lowercase_   )

							@torch.enable_grad()
							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=True			,			)					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     latents.detach().requires_grad_()

														lowerCAmelCase_										=     self.scheduler.scale_model_input(lowercase_			,			lowercase_   )

														# predict the noise residual
														lowerCAmelCase_										=     self.unet(lowercase_			,			lowercase_			,			encoder_hidden_states=lowercase_   ).sample

														if isinstance(self.scheduler			,			(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
																					lowerCAmelCase_										=     self.scheduler.alphas_cumprod[timestep]
																					lowerCAmelCase_										=     1 - alpha_prod_t
																					# compute predicted original sample from predicted noise also called
																					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
																					lowerCAmelCase_										=     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																					lowerCAmelCase_										=     torch.sqrt(lowercase_   )
																					lowerCAmelCase_										=     pred_original_sample * (fac) + latents * (1 - fac)
														elif isinstance(self.scheduler			,			lowercase_   ):
																					lowerCAmelCase_										=     self.scheduler.sigmas[index]
																					lowerCAmelCase_										=     latents - sigma * noise_pred
														else:
																					raise ValueError(f'''scheduler type {type(self.scheduler   )} not supported'''   )

														lowerCAmelCase_										=     1 / self.vae.config.scaling_factor * sample
														lowerCAmelCase_										=     self.vae.decode(lowercase_   ).sample
														lowerCAmelCase_										=     (image / 2 + 0.5).clamp(0			,			1   )

														if use_cutouts:
																					lowerCAmelCase_										=     self.make_cutouts(lowercase_			,			lowercase_   )
														else:
																					lowerCAmelCase_										=     transforms.Resize(self.cut_out_size   )(lowercase_   )
														lowerCAmelCase_										=     self.normalize(lowercase_   ).to(latents.dtype   )

														lowerCAmelCase_										=     self.clip_model.get_image_features(lowercase_   )
														lowerCAmelCase_										=     image_embeddings_clip / image_embeddings_clip.norm(p=2			,			dim=-1			,			keepdim=lowercase_   )

														if use_cutouts:
																					lowerCAmelCase_										=     spherical_dist_loss(lowercase_			,			lowercase_   )
																					lowerCAmelCase_										=     dists.view([num_cutouts, sample.shape[0], -1]   )
																					lowerCAmelCase_										=     dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
														else:
																					lowerCAmelCase_										=     spherical_dist_loss(lowercase_			,			lowercase_   ).mean() * clip_guidance_scale

														lowerCAmelCase_										=     -torch.autograd.grad(lowercase_			,			lowercase_   )[0]

														if isinstance(self.scheduler			,			lowercase_   ):
																					lowerCAmelCase_										=     latents.detach() + grads * (sigma**2)
																					lowerCAmelCase_										=     noise_pred_original
														else:
																					lowerCAmelCase_										=     noise_pred_original - torch.sqrt(lowercase_   ) * grads
														return noise_pred, latents






							@torch.no_grad()
							def __call__(		self			,			lowercase_			,			lowercase_ = 5_1_2			,			lowercase_ = 5_1_2			,			lowercase_ = 5_0			,			lowercase_ = 7.5			,			lowercase_ = 1			,			lowercase_ = 0.0			,			lowercase_ = 1_0_0			,			lowercase_ = None			,			lowercase_ = 4			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = "pil"			,			lowercase_ = True			,			)					->					str:
														'''simple docstring'''

														if isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     1
														elif isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     len(lowercase_   )
														else:
																					raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase_   )}'''   )

														if height % 8 != 0 or width % 8 != 0:
																					raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

														# get prompt text embeddings
														lowerCAmelCase_										=     self.tokenizer(
														    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			return_tensors='pt'			,			)
														lowerCAmelCase_										=     self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
														# duplicate text embeddings for each generation per prompt
														lowerCAmelCase_										=     text_embeddings.repeat_interleave(lowercase_			,			dim=0   )

														if clip_guidance_scale > 0:
																					if clip_prompt is not None:
																												lowerCAmelCase_										=     self.tokenizer(
																												    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			return_tensors='pt'			,			).input_ids.to(self.device   )
																					else:
																												lowerCAmelCase_										=     text_input.input_ids.to(self.device   )
																					lowerCAmelCase_										=     self.clip_model.get_text_features(lowercase_   )
																					lowerCAmelCase_										=     text_embeddings_clip / text_embeddings_clip.norm(p=2			,			dim=-1			,			keepdim=lowercase_   )
																					# duplicate text embeddings clip for each generation per prompt
																					lowerCAmelCase_										=     text_embeddings_clip.repeat_interleave(lowercase_			,			dim=0   )

														# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
														# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
														# corresponds to doing no classifier free guidance.
														lowerCAmelCase_										=     guidance_scale > 1.0
														# get unconditional embeddings for classifier free guidance
														if do_classifier_free_guidance:
																					lowerCAmelCase_										=     text_input.input_ids.shape[-1]
																					lowerCAmelCase_										=     self.tokenizer(['']			,			padding='max_length'			,			max_length=lowercase_			,			return_tensors='pt'   )
																					lowerCAmelCase_										=     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																					# duplicate unconditional embeddings for each generation per prompt
																					lowerCAmelCase_										=     uncond_embeddings.repeat_interleave(lowercase_			,			dim=0   )

																					# For classifier free guidance, we need to do two forward passes.
																					# Here we concatenate the unconditional and text embeddings into a single batch
																					# to avoid doing two forward passes
																					lowerCAmelCase_										=     torch.cat([uncond_embeddings, text_embeddings]   )

														# get the initial random noise unless the user supplied it

														# Unlike in other pipelines, latents need to be generated in the target device
														# for 1-to-1 results reproducibility with the CompVis implementation.
														# However this currently doesn't work in `mps`.
														lowerCAmelCase_										=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
														lowerCAmelCase_										=     text_embeddings.dtype
														if latents is None:
																					if self.device.type == "mps":
																												# randn does not work reproducibly on mps
																												lowerCAmelCase_										=     torch.randn(lowercase_			,			generator=lowercase_			,			device='cpu'			,			dtype=lowercase_   ).to(
																												    self.device   )
																					else:
																												lowerCAmelCase_										=     torch.randn(lowercase_			,			generator=lowercase_			,			device=self.device			,			dtype=lowercase_   )
														else:
																					if latents.shape != latents_shape:
																												raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
																					lowerCAmelCase_										=     latents.to(self.device   )

														# set timesteps
														lowerCAmelCase_										=     'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
														lowerCAmelCase_										=     {}
														if accepts_offset:
																					lowerCAmelCase_										=     1

														self.scheduler.set_timesteps(lowercase_			,			**lowercase_   )

														# Some schedulers like PNDM have timesteps as arrays
														# It's more optimized to move all timesteps to correct device beforehand
														lowerCAmelCase_										=     self.scheduler.timesteps.to(self.device   )

														# scale the initial noise by the standard deviation required by the scheduler
														lowerCAmelCase_										=     latents * self.scheduler.init_noise_sigma

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
														# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
														# and should be between [0, 1]
														lowerCAmelCase_										=     'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
														lowerCAmelCase_										=     {}
														if accepts_eta:
																					lowerCAmelCase_										=     eta

														# check if the scheduler accepts generator
														lowerCAmelCase_										=     'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
														if accepts_generator:
																					lowerCAmelCase_										=     generator

														for i, t in enumerate(self.progress_bar(lowercase_   )   ):
																					# expand the latents if we are doing classifier free guidance
																					lowerCAmelCase_										=     torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
																					lowerCAmelCase_										=     self.scheduler.scale_model_input(lowercase_			,			lowercase_   )

																					# predict the noise residual
																					lowerCAmelCase_										=     self.unet(lowercase_			,			lowercase_			,			encoder_hidden_states=lowercase_   ).sample

																					# perform classifier free guidance
																					if do_classifier_free_guidance:
																												lowerCAmelCase_						,   lowerCAmelCase_										=     noise_pred.chunk(2   )
																												lowerCAmelCase_										=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					# perform clip guidance
																					if clip_guidance_scale > 0:
																												lowerCAmelCase_										=     (
																												    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
																												)
																												lowerCAmelCase_						,   lowerCAmelCase_										=     self.cond_fn(
																												    lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)

																					# compute the previous noisy sample x_t -> x_t-1
																					lowerCAmelCase_										=     self.scheduler.step(lowercase_			,			lowercase_			,			lowercase_			,			**lowercase_   ).prev_sample

														# scale and decode the image latents with vae
														lowerCAmelCase_										=     1 / self.vae.config.scaling_factor * latents
														lowerCAmelCase_										=     self.vae.decode(lowercase_   ).sample

														lowerCAmelCase_										=     (image / 2 + 0.5).clamp(0			,			1   )
														lowerCAmelCase_										=     image.cpu().permute(0			,			2			,			3			,			1   ).numpy()

														if output_type == "pil":
																					lowerCAmelCase_										=     self.numpy_to_pil(lowercase_   )

														if not return_dict:
																					return (image, None)

														return StableDiffusionPipelineOutput(images=lowercase_			,			nsfw_content_detected=lowercase_   )



