



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A       ={5_1_2: "DiT-XL-2-512x512.pt", 2_5_6: "DiT-XL-2-256x256.pt"}

def 			a		( _UpperCAmelCase				:   Dict		):







		'''simple docstring'''





		__UpperCAmelCase       :	int						=      f'pretrained_models/{model_name}'
		if not os.path.isfile(__UpperCAmelCase		):
				os.makedirs('''pretrained_models'''					,  exist_ok=__UpperCAmelCase		)
				__UpperCAmelCase       :	Any						=      f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
				download_url(__UpperCAmelCase					,  '''pretrained_models'''		)
		__UpperCAmelCase       :	List[str]						=      torch.load(__UpperCAmelCase					,  map_location=lambda _UpperCAmelCase					,  _UpperCAmelCase		: storage		)
		return model

def 			a		( _UpperCAmelCase				:   List[Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      download_model(pretrained_models[args.image_size]		)

		__UpperCAmelCase       :	Dict						=      state_dict['''x_embedder.proj.weight''']
		__UpperCAmelCase       :	str						=      state_dict['''x_embedder.proj.bias''']
		state_dict.pop('''x_embedder.proj.weight'''		)
		state_dict.pop('''x_embedder.proj.bias'''		)

		for depth in range(28		):
				__UpperCAmelCase       :	Optional[Any]						=      state_dict[
				    '''t_embedder.mlp.0.weight'''
				]
				__UpperCAmelCase       :	int						=      state_dict[
				    '''t_embedder.mlp.0.bias'''
				]
				__UpperCAmelCase       :	int						=      state_dict[
				    '''t_embedder.mlp.2.weight'''
				]
				__UpperCAmelCase       :	List[str]						=      state_dict[
				    '''t_embedder.mlp.2.bias'''
				]
				__UpperCAmelCase       :	Tuple						=      state_dict[
				    '''y_embedder.embedding_table.weight'''
				]

				__UpperCAmelCase       :	Any						=      state_dict[
				    f'blocks.{depth}.adaLN_modulation.1.weight'
				]
				__UpperCAmelCase       :	str						=      state_dict[
				    f'blocks.{depth}.adaLN_modulation.1.bias'
				]

				__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase       :	List[str]						=      torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']					,  3					,  dim=0		)
				__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase       :	List[Any]						=      torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']					,  3					,  dim=0		)

				__UpperCAmelCase       :	Any						=      q
				__UpperCAmelCase       :	Any						=      q_bias
				__UpperCAmelCase       :	Union[str, Any]						=      k
				__UpperCAmelCase       :	Any						=      k_bias
				__UpperCAmelCase       :	Optional[int]						=      v
				__UpperCAmelCase       :	Union[str, Any]						=      v_bias

				__UpperCAmelCase       :	List[Any]						=      state_dict[
				    f'blocks.{depth}.attn.proj.weight'
				]
				__UpperCAmelCase       :	List[str]						=      state_dict[f'blocks.{depth}.attn.proj.bias']

				__UpperCAmelCase       :	Tuple						=      state_dict[f'blocks.{depth}.mlp.fc1.weight']
				__UpperCAmelCase       :	Optional[int]						=      state_dict[f'blocks.{depth}.mlp.fc1.bias']
				__UpperCAmelCase       :	List[Any]						=      state_dict[f'blocks.{depth}.mlp.fc2.weight']
				__UpperCAmelCase       :	Union[str, Any]						=      state_dict[f'blocks.{depth}.mlp.fc2.bias']

				state_dict.pop(f'blocks.{depth}.attn.qkv.weight'		)
				state_dict.pop(f'blocks.{depth}.attn.qkv.bias'		)
				state_dict.pop(f'blocks.{depth}.attn.proj.weight'		)
				state_dict.pop(f'blocks.{depth}.attn.proj.bias'		)
				state_dict.pop(f'blocks.{depth}.mlp.fc1.weight'		)
				state_dict.pop(f'blocks.{depth}.mlp.fc1.bias'		)
				state_dict.pop(f'blocks.{depth}.mlp.fc2.weight'		)
				state_dict.pop(f'blocks.{depth}.mlp.fc2.bias'		)
				state_dict.pop(f'blocks.{depth}.adaLN_modulation.1.weight'		)
				state_dict.pop(f'blocks.{depth}.adaLN_modulation.1.bias'		)

		state_dict.pop('''t_embedder.mlp.0.weight'''		)
		state_dict.pop('''t_embedder.mlp.0.bias'''		)
		state_dict.pop('''t_embedder.mlp.2.weight'''		)
		state_dict.pop('''t_embedder.mlp.2.bias'''		)
		state_dict.pop('''y_embedder.embedding_table.weight'''		)

		__UpperCAmelCase       :	Optional[Any]						=      state_dict['''final_layer.adaLN_modulation.1.weight''']
		__UpperCAmelCase       :	List[str]						=      state_dict['''final_layer.adaLN_modulation.1.bias''']
		__UpperCAmelCase       :	Optional[int]						=      state_dict['''final_layer.linear.weight''']
		__UpperCAmelCase       :	List[Any]						=      state_dict['''final_layer.linear.bias''']

		state_dict.pop('''final_layer.linear.weight'''		)
		state_dict.pop('''final_layer.linear.bias'''		)
		state_dict.pop('''final_layer.adaLN_modulation.1.weight'''		)
		state_dict.pop('''final_layer.adaLN_modulation.1.bias'''		)

		# DiT XL/2
		__UpperCAmelCase       :	Dict						=      TransformeraDModel(
		    sample_size=args.image_size // 8					,  num_layers=28					,  attention_head_dim=72					,  in_channels=4					,  out_channels=8					,  patch_size=2					,  attention_bias=__UpperCAmelCase					,  num_attention_heads=16					,  activation_fn='''gelu-approximate'''					,  num_embeds_ada_norm=10_00					,  norm_type='''ada_norm_zero'''					,  norm_elementwise_affine=__UpperCAmelCase					,  )
		transformer.load_state_dict(__UpperCAmelCase					,  strict=__UpperCAmelCase		)

		__UpperCAmelCase       :	Any						=      DDIMScheduler(
		    num_train_timesteps=10_00					,  beta_schedule='''linear'''					,  prediction_type='''epsilon'''					,  clip_sample=__UpperCAmelCase					,  )

		__UpperCAmelCase       :	List[Any]						=      AutoencoderKL.from_pretrained(args.vae_model		)

		__UpperCAmelCase       :	int						=      DiTPipeline(transformer=__UpperCAmelCase					,  vae=__UpperCAmelCase					,  scheduler=__UpperCAmelCase		)

		if args.save:
				pipeline.save_pretrained(args.checkpoint_path		)


if __name__ == "__main__":
				__A       =argparse.ArgumentParser()

				parser.add_argument(
				    "--image_size",
				    default=2_5_6,
				    type=int,
				    required=False,
				    help="Image size of pretrained model, either 256 or 512.",
				)
				parser.add_argument(
				    "--vae_model",
				    default="stabilityai/sd-vae-ft-ema",
				    type=str,
				    required=False,
				    help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",
				)
				parser.add_argument(
				    "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
				)
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."
				)

				__A       =parser.parse_args()
				main(args)










import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 				a_    (						unittest.TestCase							):
						UpperCamelCase__ :		Any										=EsmTokenizer

						def       __a			(       self      :Optional[int])						->   Optional[int]:
												super().setUp()
												UpperCAmelCase_					=	tempfile.mkdtemp()
												# fmt: off
												UpperCAmelCase_					=	["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
												# fmt: on
												UpperCAmelCase_					=	os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES['''vocab_file'''])
												with open(self.vocab_file  ,		'''w'''  ,		encoding='''utf-8''') as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))

						def       __a			(       self      :List[str]  ,		**_lowercase      :Union[str, Any])						->   List[PreTrainedTokenizerBase]:
												return [self.get_tokenizer(**_lowercase)]

						def       __a			(       self      :Optional[int]  ,		**_lowercase      :List[str])						->   PreTrainedTokenizer:
												return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**_lowercase)

						def       __a			(       self      :int)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	tokenizer.tokenize('''LAGVS''')
												self.assertListEqual(_lowercase  ,		['''L''', '''A''', '''G''', '''V''', '''S'''])
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)  ,		[4, 5, 6, 7, 8])

						def       __a			(       self      :List[Any])						->   Optional[int]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	'''LAGVS'''
												self.assertListEqual(tokenizer.encode(_lowercase)  ,		[0, 4, 5, 6, 7, 8, 2])

						def       __a			(       self      :Tuple)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]])

						def       __a			(       self      :str)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]])




						def       __a			(       self      :Dict)						->   Optional[int]:
												UpperCAmelCase_					=	self.get_tokenizers(fast=_lowercase)
												for tokenizer in tokenizers:
																		with self.subTest(f"{tokenizer.__class__.__name__}"):
																								UpperCAmelCase_					=	'''<unk>'''
																								UpperCAmelCase_					=	'''<mask>'''

																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)
																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)

																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(token_a[0]  ,		_lowercase)
																								self.assertEqual(token_a[0]  ,		_lowercase)





