




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()
__a			:	Tuple							     =		logging.get_logger(__name__)

__a			:	Optional[Any]							     =		"""Hello world! cécé herlolip"""




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_					)
			roberta.eval()  # disable dropout
			UpperCamelCase_     				= roberta.model.encoder.sentence_encoder
			UpperCamelCase_     				= 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:
						UpperCamelCase_     				= roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
			print('Our BERT config:'     ,		SCREAMING_SNAKE_CASE_					)

			UpperCamelCase_     				= RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_					) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_					)
			model.eval()

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

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

						# self attention
						UpperCamelCase_     				= 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)					)
						)

						UpperCamelCase_     				= roberta_layer.self_attn.q_proj.weight
						UpperCamelCase_     				= roberta_layer.self_attn.q_proj.bias
						UpperCamelCase_     				= roberta_layer.self_attn.k_proj.weight
						UpperCamelCase_     				= roberta_layer.self_attn.k_proj.bias
						UpperCamelCase_     				= roberta_layer.self_attn.v_proj.weight
						UpperCamelCase_     				= roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

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

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


if __name__ == "__main__":
				__a			:	Optional[Any]							     =		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."""
				)
				__a			:	Tuple							     =		parser.parse_args()
				convert_roberta_checkpoint_to_pytorch(
				    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	A__				(	__magic_name__      ):






				def __init__(		self   : Any       ,	a   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a       ,	scheduler=a  )






				@torch.no_grad()
				def __call__(		self   : Optional[Any]       ,	a   : int = 1       ,	a   : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	a   : int = 1_000       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	):






							'''simple docstring'''







							if isinstance(self.unet.config.sample_size       ,	a  ):
										lowerCAmelCase__ : Union[str, Any]   =						(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										lowerCAmelCase__ : str   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										lowerCAmelCase__ : Optional[int]   =						randn_tensor(a       ,	generator=a  )
										lowerCAmelCase__ : Optional[int]   =						image.to(self.device  )
							else:
										lowerCAmelCase__ : Dict   =						randn_tensor(a       ,	generator=a       ,	device=self.device  )

							# set step values
							self.scheduler.set_timesteps(a  )

							for t in self.progress_bar(self.scheduler.timesteps  ):
										# 1. predict noise model_output
										lowerCAmelCase__ : Tuple   =						self.unet(a       ,	a  ).sample

										# 2. compute previous image: x_t -> x_t-1
										lowerCAmelCase__ : Optional[Any]   =						self.scheduler.step(a       ,	a       ,	a       ,	generator=a  ).prev_sample

							lowerCAmelCase__ : str   =						(image / 2 + 0.5).clamp(0       ,	1  )
							lowerCAmelCase__ : Optional[int]   =						image.cpu().permute(0       ,	2       ,	3       ,	1  ).numpy()
							if output_type == "pil":
										lowerCAmelCase__ : int   =						self.numpy_to_pil(a  )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a  )