




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

UpperCamelCase__ :   Any						=							'''Hello world! cécé herlolip'''





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     List[Any]  )       ->  List[str]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FairseqRobertaModel.from_pretrained(_a  )
			roberta.eval()  # disable dropout
			SCREAMING_SNAKE_CASE_   : int					  =      roberta.model.encoder.sentence_encoder
			SCREAMING_SNAKE_CASE_   : List[str]					  =      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:
						SCREAMING_SNAKE_CASE_   : List[str]					  =      roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print('Our RoBERTa config:'  ,							_a  )

			SCREAMING_SNAKE_CASE_   : Any					  =      XLMRobertaXLForSequenceClassification(_a  ) if classification_head else XLMRobertaXLForMaskedLM(_a  )
			model.eval()

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

			SCREAMING_SNAKE_CASE_   : Any					  =      roberta_sent_encoder.layer_norm.weight
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      roberta_sent_encoder.layer_norm.bias

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

						SCREAMING_SNAKE_CASE_   : RobertaAttention					  =      layer.attention
						SCREAMING_SNAKE_CASE_   : Dict					  =      roberta_layer.self_attn_layer_norm.weight
						SCREAMING_SNAKE_CASE_   : str					  =      roberta_layer.self_attn_layer_norm.bias

						# self attention
						SCREAMING_SNAKE_CASE_   : BertSelfAttention					  =      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)  )
						)

						SCREAMING_SNAKE_CASE_   : Tuple					  =      roberta_layer.self_attn.q_proj.weight
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      roberta_layer.self_attn.q_proj.bias
						SCREAMING_SNAKE_CASE_   : Dict					  =      roberta_layer.self_attn.k_proj.weight
						SCREAMING_SNAKE_CASE_   : str					  =      roberta_layer.self_attn.k_proj.bias
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      roberta_layer.self_attn.v_proj.weight
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      roberta_layer.self_attn.v_proj.bias

						# self-attention output
						SCREAMING_SNAKE_CASE_   : BertSelfOutput					  =      layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						SCREAMING_SNAKE_CASE_   : Dict					  =      roberta_layer.self_attn.out_proj.weight
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      roberta_layer.final_layer_norm.weight
						SCREAMING_SNAKE_CASE_   : Dict					  =      roberta_layer.final_layer_norm.bias

						# intermediate
						SCREAMING_SNAKE_CASE_   : BertIntermediate					  =      layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      roberta_layer.fca.weight
						SCREAMING_SNAKE_CASE_   : str					  =      roberta_layer.fca.bias

						# output
						SCREAMING_SNAKE_CASE_   : BertOutput					  =      layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						SCREAMING_SNAKE_CASE_   : Tuple					  =      roberta_layer.fca.weight
						SCREAMING_SNAKE_CASE_   : int					  =      roberta_layer.fca.bias
						# end of layer

			if classification_head:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      roberta.model.classification_heads["mnli"].dense.weight
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      roberta.model.classification_heads["mnli"].dense.bias
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      roberta.model.classification_heads["mnli"].out_proj.weight
						SCREAMING_SNAKE_CASE_   : Tuple					  =      roberta.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      roberta.model.encoder.lm_head.dense.weight
						SCREAMING_SNAKE_CASE_   : int					  =      roberta.model.encoder.lm_head.dense.bias
						SCREAMING_SNAKE_CASE_   : Dict					  =      roberta.model.encoder.lm_head.layer_norm.weight
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      roberta.model.encoder.lm_head.layer_norm.bias
						SCREAMING_SNAKE_CASE_   : Dict					  =      roberta.model.encoder.lm_head.weight
						SCREAMING_SNAKE_CASE_   : Tuple					  =      roberta.model.encoder.lm_head.bias

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

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






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							UNetaDModel(
				    block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")   ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")   ,   )
				return model






			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2






@slow
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2