






'''simple docstring'''
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def   lowercase_	(   lowerCAmelCase__	:  Union[str, Any] ):

			"""simple docstring"""





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


def   lowercase_	(   lowerCAmelCase__	:  str ):

			"""simple docstring"""





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

			__UpperCAmelCase   :      Optional[int]										=     state_dict["""x_embedder.proj.weight"""]
			__UpperCAmelCase   :      Optional[int]										=     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   :      List[Any]										=     state_dict[
						    """t_embedder.mlp.0.bias"""
						]
						__UpperCAmelCase   :      int										=     state_dict[
						    """t_embedder.mlp.2.weight"""
						]
						__UpperCAmelCase   :      List[Any]										=     state_dict[
						    """t_embedder.mlp.2.bias"""
						]
						__UpperCAmelCase   :      int										=     state_dict[
						    """y_embedder.embedding_table.weight"""
						]

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

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

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

						__UpperCAmelCase   :      Dict										=     state_dict[
						    f'blocks.{depth}.attn.proj.weight'
						]
						__UpperCAmelCase   :      Optional[int]										=     state_dict[f'blocks.{depth}.attn.proj.bias']

						__UpperCAmelCase   :      Union[str, Any]										=     state_dict[f'blocks.{depth}.mlp.fc1.weight']
						__UpperCAmelCase   :      Optional[Any]										=     state_dict[f'blocks.{depth}.mlp.fc1.bias']
						__UpperCAmelCase   :      int										=     state_dict[f'blocks.{depth}.mlp.fc2.weight']
						__UpperCAmelCase   :      Optional[int]										=     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[int]										=     state_dict["""final_layer.adaLN_modulation.1.weight"""]
			__UpperCAmelCase   :      Any										=     state_dict["""final_layer.adaLN_modulation.1.bias"""]
			__UpperCAmelCase   :      Optional[Any]										=     state_dict["""final_layer.linear.weight"""]
			__UpperCAmelCase   :      str										=     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   :      str										=     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=lowerCAmelCase__			,							num_attention_heads=16			,							activation_fn="""gelu-approximate"""			,							num_embeds_ada_norm=1000			,							norm_type="""ada_norm_zero"""			,							norm_elementwise_affine=lowerCAmelCase__			,							)
			transformer.load_state_dict(lowerCAmelCase__			,							strict=lowerCAmelCase__ )

			__UpperCAmelCase   :      Tuple										=     DDIMScheduler(
			    num_train_timesteps=1000			,							beta_schedule="""linear"""			,							prediction_type="""epsilon"""			,							clip_sample=lowerCAmelCase__			,							)

			__UpperCAmelCase   :      Tuple										=     AutoencoderKL.from_pretrained(args.vae_model )

			__UpperCAmelCase   :      Optional[int]										=     DiTPipeline(transformer=lowerCAmelCase__			,							vae=lowerCAmelCase__			,							scheduler=lowerCAmelCase__ )

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


if __name__ == "__main__":
					_UpperCamelCase											=			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											=			parser.parse_args()
					main(args)









'''simple docstring'''
import torch

from transformers import AutoTokenizer



class _A  (    __SCREAMING_SNAKE_CASE    ):





		def __init__(     self ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     AutoTokenizer.from_pretrained(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      Dict										=     None
								for l in x:
											__UpperCAmelCase   :      Dict										=     self.tokenizer(
											    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )
											__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor([len(__UpperCAmelCase				)]				)
											if d is not None:
														for k in d.keys():
																	__UpperCAmelCase   :      Dict										=     torch.cat((d[k], t[k]) ,       0				)
											else:
														__UpperCAmelCase   :      List[str]										=     t

								__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""				)				)
								__UpperCAmelCase   :      Any										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""				)				)

					elif isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      int										=     self.tokenizer(
								    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )

					else:
								raise Exception(
								    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
								    """ strings` for supports are supported."""				)

					return d





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=0.70				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     []
					for idx in range(len(W_query["""input_ids"""]				)				):
								__UpperCAmelCase   :      Optional[int]										=     range(p_start.shape[1]				)

								__UpperCAmelCase   :      Union[str, Any]										=     []
								for start_id in start_indexes:
											for end_id in end_indexes:
														if start_id < end_id:
																	output.append(
																	    (
																	        start_id,
																	        end_id,
																	        p_start[idx][start_id].item(),
																	        p_end[idx][end_id].item(),
																	    )				)

								output.sort(key=lambda __UpperCAmelCase				: (tup[2] * tup[3]) ,       reverse=__UpperCAmelCase				)
								__UpperCAmelCase   :      Optional[Any]										=     []
								for k in range(len(__UpperCAmelCase				)				):
											if output[k][2] * output[k][3] >= thresh:
														__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     output[k][0], output[k][1]
														__UpperCAmelCase   :      int										=     self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]				)
														temp.append((decoded, output[k][2] * output[k][3])				)

								final_outputs.append(__UpperCAmelCase				)

					return final_outputs




