



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__magic_name__       =     {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






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





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       download_model(pretrained_models[args.image_size]				)

					lowercase						   =       state_dict['x_embedder.proj.weight']
					lowercase						   =       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				):
										lowercase						   =       state_dict[
										    't_embedder.mlp.0.weight'
										]
										lowercase						   =       state_dict[
										    't_embedder.mlp.0.bias'
										]
										lowercase						   =       state_dict[
										    't_embedder.mlp.2.weight'
										]
										lowercase						   =       state_dict[
										    't_embedder.mlp.2.bias'
										]
										lowercase						   =       state_dict[
										    'y_embedder.embedding_table.weight'
										]

										lowercase						   =       state_dict[
										    f"""blocks.{depth}.adaLN_modulation.1.weight"""
										]
										lowercase						   =       state_dict[
										    f"""blocks.{depth}.adaLN_modulation.1.bias"""
										]

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

										lowercase						   =       q
										lowercase						   =       q_bias
										lowercase						   =       k
										lowercase						   =       k_bias
										lowercase						   =       v
										lowercase						   =       v_bias

										lowercase						   =       state_dict[
										    f"""blocks.{depth}.attn.proj.weight"""
										]
										lowercase						   =       state_dict[f"""blocks.{depth}.attn.proj.bias"""]

										lowercase						   =       state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
										lowercase						   =       state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
										lowercase						   =       state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
										lowercase						   =       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'				)

					lowercase						   =       state_dict['final_layer.adaLN_modulation.1.weight']
					lowercase						   =       state_dict['final_layer.adaLN_modulation.1.bias']
					lowercase						   =       state_dict['final_layer.linear.weight']
					lowercase						   =       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
					lowercase						   =       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				)

					lowercase						   =       DDIMScheduler(
					    num_train_timesteps=10_00       ,			beta_schedule='linear'       ,			prediction_type='epsilon'       ,			clip_sample=_UpperCAmelCase       ,			)

					lowercase						   =       AutoencoderKL.from_pretrained(args.vae_model				)

					lowercase						   =       DiTPipeline(transformer=_UpperCAmelCase       ,			vae=_UpperCAmelCase       ,			scheduler=_UpperCAmelCase				)

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


if __name__ == "__main__":
		__magic_name__       =     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.'''
		)

		__magic_name__       =     parser.parse_args()
		main(args)











import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					@cached_property
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'    )

					@cached_property
					def 				__UpperCAmelCase		(		self     :Any    ):
										return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'    )

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       self.tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       self.rust_tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Any    ):
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]



					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]






