



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()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       '''Hello world! cécé herlolip'''

def       A_   (						__a  :					Tuple			,		__a  :					str			,		__a  :					int		):

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

				a__					   =		RobertaForSequenceClassification(A__		) if classification_head else RobertaForMaskedLM(A__		)
				model.eval()

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

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

								# self attention
								a__					   =		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)		)
								)

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

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

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

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

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

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

				a__					   =		model(A__		)[0]
				if classification_head:
								a__					   =		roberta.model.classification_heads['mnli'](roberta.extract_features(A__		)		)
				else:
								a__					   =		roberta.model(A__		)[0]
				print(our_output.shape			,		their_output.shape		)
				a__					   =		torch.max(torch.abs(our_output - their_output		)		).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
				a__					   =		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							=       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							=       parser.parse_args()
	convert_roberta_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__	:  Union[str, Any]						=  False





@nightly
@require_torch_gpu
class 					__snake_case    (     unittest.TestCase      ):




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      str   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

									snake_case__   :      int   				= image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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


