


'''simple docstring'''


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase_		:       Dict							       =		{512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	str							=				f"""pretrained_models/{model_name}"""
				if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
								os.makedirs("""pretrained_models"""							,				exist_ok=SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	int							=				f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
								download_url(SCREAMING_SNAKE_CASE__							,				"""pretrained_models"""					)
				_SCREAMING_SNAKE_CASE			:	Dict							=				torch.load(SCREAMING_SNAKE_CASE__							,				map_location=lambda SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					: storage					)
				return model







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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

				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				state_dict['x_embedder.proj.weight']
				_SCREAMING_SNAKE_CASE			:	List[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					):
								_SCREAMING_SNAKE_CASE			:	str							=				state_dict[
								    't_embedder.mlp.0.weight'
								]
								_SCREAMING_SNAKE_CASE			:	Tuple							=				state_dict[
								    't_embedder.mlp.0.bias'
								]
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				state_dict[
								    't_embedder.mlp.2.weight'
								]
								_SCREAMING_SNAKE_CASE			:	List[str]							=				state_dict[
								    't_embedder.mlp.2.bias'
								]
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				state_dict[
								    'y_embedder.embedding_table.weight'
								]

								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				state_dict[
								    f"""blocks.{depth}.adaLN_modulation.1.weight"""
								]
								_SCREAMING_SNAKE_CASE			:	List[str]							=				state_dict[
								    f"""blocks.{depth}.adaLN_modulation.1.bias"""
								]

								_SCREAMING_SNAKE_CASE			:	int							=				torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]							,				3							,				dim=0					)
								_SCREAMING_SNAKE_CASE			:	int							=				torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]							,				3							,				dim=0					)

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

								_SCREAMING_SNAKE_CASE			:	int							=				state_dict[
								    f"""blocks.{depth}.attn.proj.weight"""
								]
								_SCREAMING_SNAKE_CASE			:	Tuple							=				state_dict[f"""blocks.{depth}.attn.proj.bias"""]

								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
								_SCREAMING_SNAKE_CASE			:	int							=				state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
								_SCREAMING_SNAKE_CASE			:	str							=				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"""					)

				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				state_dict['final_layer.adaLN_modulation.1.weight']
				_SCREAMING_SNAKE_CASE			:	str							=				state_dict['final_layer.adaLN_modulation.1.bias']
				_SCREAMING_SNAKE_CASE			:	str							=				state_dict['final_layer.linear.weight']
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				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=SCREAMING_SNAKE_CASE__							,				num_attention_heads=16							,				activation_fn="""gelu-approximate"""							,				num_embeds_ada_norm=1000							,				norm_type="""ada_norm_zero"""							,				norm_elementwise_affine=SCREAMING_SNAKE_CASE__							,				)
				transformer.load_state_dict(SCREAMING_SNAKE_CASE__							,				strict=SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				DDIMScheduler(
				    num_train_timesteps=1000							,				beta_schedule="""linear"""							,				prediction_type="""epsilon"""							,				clip_sample=SCREAMING_SNAKE_CASE__							,				)

				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				AutoencoderKL.from_pretrained(args.vae_model					)

				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				DiTPipeline(transformer=SCREAMING_SNAKE_CASE__							,				vae=SCREAMING_SNAKE_CASE__							,				scheduler=SCREAMING_SNAKE_CASE__					)

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


if __name__ == "__main__":
						UpperCAmelCase_		:       Optional[Any]							       =		argparse.ArgumentParser()

						parser.add_argument(
						    '--image_size',
						    default=256,
						    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.'
						)

						UpperCAmelCase_		:       int							       =		parser.parse_args()
						main(args)








'''simple docstring'''


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    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							A_							:				Optional[Any]				     =  EsmTokenizer






							def UpperCAmelCase_     (   self  ):
											super().setUp()
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tempfile.mkdtemp()
											# fmt: off
											_SCREAMING_SNAKE_CASE			:	List[str]							=				["<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
											_SCREAMING_SNAKE_CASE			:	Any							=				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 UpperCAmelCase_     (   self   , **__snake_case  ):
											return [self.get_tokenizer(**__snake_case  )]






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return self.tokenizer_class.from_pretrained(self.tmpdirname   , **__snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	int							=				tokenizer.tokenize("""LAGVS"""  )
											self.assertListEqual(__snake_case   , ["""L""", """A""", """G""", """V""", """S"""]  )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case  )   , [4, 5, 6, 7, 8]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				"""LAGVS"""
											self.assertListEqual(tokenizer.encode(__snake_case  )   , [0, 4, 5, 6, 7, 8, 2]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

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






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	str							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

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






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_tokenizers(fast=__snake_case  )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""  ):
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<unk>"""
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<mask>"""

																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer.tokenize(__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.tokenize(__snake_case  )

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




