




'''simple docstring'''




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


snake_case__ 							=    {5_12: """DiT-XL-2-512x512.pt""", 2_56: """DiT-XL-2-256x256.pt"""}




def     snake_case__     (			lowerCamelCase__					: List[str]      ) -> str:
					A_			:  Union[str, Any]			=				f'pretrained_models/{model_name}'
					if not os.path.isfile(UpperCamelCase__      ):
										os.makedirs('''pretrained_models'''      ,		exist_ok=UpperCamelCase__      )
										A_			:  List[Any]			=				f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
										download_url(UpperCamelCase__      ,		'''pretrained_models'''      )
					A_			:  Optional[int]			=				torch.load(UpperCamelCase__      ,		map_location=lambda lowerCamelCase__      ,		lowerCamelCase__      : storage      )
					return model




def     snake_case__     (			lowerCamelCase__					: Optional[Any]      ) -> Optional[int]:
					A_			:  Optional[Any]			=				download_model(pretrained_models[args.image_size]      )

					A_			:  Optional[Any]			=				state_dict['''x_embedder.proj.weight''']
					A_			:  List[Any]			=				state_dict['''x_embedder.proj.bias''']
					state_dict.pop('''x_embedder.proj.weight'''      )
					state_dict.pop('''x_embedder.proj.bias'''      )

					for depth in range(2_8      ):
										A_			:  int			=				state_dict[
										    '''t_embedder.mlp.0.weight'''
										]
										A_			:  Dict			=				state_dict[
										    '''t_embedder.mlp.0.bias'''
										]
										A_			:  List[Any]			=				state_dict[
										    '''t_embedder.mlp.2.weight'''
										]
										A_			:  int			=				state_dict[
										    '''t_embedder.mlp.2.bias'''
										]
										A_			:  List[str]			=				state_dict[
										    '''y_embedder.embedding_table.weight'''
										]

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

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

										A_			:  Any			=				q
										A_			:  int			=				q_bias
										A_			:  Any			=				k
										A_			:  List[Any]			=				k_bias
										A_			:  Optional[Any]			=				v
										A_			:  int			=				v_bias

										A_			:  str			=				state_dict[
										    f'blocks.{depth}.attn.proj.weight'
										]
										A_			:  Union[str, Any]			=				state_dict[f'blocks.{depth}.attn.proj.bias']

										A_			:  int			=				state_dict[f'blocks.{depth}.mlp.fc1.weight']
										A_			:  str			=				state_dict[f'blocks.{depth}.mlp.fc1.bias']
										A_			:  Any			=				state_dict[f'blocks.{depth}.mlp.fc2.weight']
										A_			:  Tuple			=				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'''      )

					A_			:  Any			=				state_dict['''final_layer.adaLN_modulation.1.weight''']
					A_			:  Optional[int]			=				state_dict['''final_layer.adaLN_modulation.1.bias''']
					A_			:  List[str]			=				state_dict['''final_layer.linear.weight''']
					A_			:  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
					A_			:  List[Any]			=				TransformeraDModel(
					    sample_size=args.image_size // 8      ,		num_layers=2_8      ,		attention_head_dim=7_2      ,		in_channels=4      ,		out_channels=8      ,		patch_size=2      ,		attention_bias=UpperCamelCase__      ,		num_attention_heads=1_6      ,		activation_fn='''gelu-approximate'''      ,		num_embeds_ada_norm=1_0_0_0      ,		norm_type='''ada_norm_zero'''      ,		norm_elementwise_affine=UpperCamelCase__      ,		)
					transformer.load_state_dict(UpperCamelCase__      ,		strict=UpperCamelCase__      )

					A_			:  List[Any]			=				DDIMScheduler(
					    num_train_timesteps=1_0_0_0      ,		beta_schedule='''linear'''      ,		prediction_type='''epsilon'''      ,		clip_sample=UpperCamelCase__      ,		)

					A_			:  List[str]			=				AutoencoderKL.from_pretrained(args.vae_model      )

					A_			:  Any			=				DiTPipeline(transformer=UpperCamelCase__      ,		vae=UpperCamelCase__      ,		scheduler=UpperCamelCase__      )

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


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

							parser.add_argument(
							    """--image_size""",
							    default=2_56,
							    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."""
							)

							snake_case__ 							=    parser.parse_args()
							main(args)










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCAmelCase_   				=  getLogger(__name__)


UpperCAmelCase_   				=  """cuda""" if torch.cuda.is_available() else """cpu"""





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							int = 8							,     UpperCamelCase__      :							str = DEFAULT_DEVICE							,     UpperCamelCase__      :							str=False							,     UpperCamelCase__      :							str="summarization"							,     UpperCamelCase__      :							Optional[int]=None							,     **UpperCamelCase__      :							int							,     )      ->			Dict:



					'''simple docstring'''




					_snake_case						=					Path(UpperCamelCase__    ).open('w'							,     encoding='utf-8'    )
					_snake_case						=					str(UpperCamelCase__    )
					_snake_case						=					AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__    ).to(UpperCamelCase__    )
					if fpaa:
										_snake_case						=					model.half()

					_snake_case						=					AutoTokenizer.from_pretrained(UpperCamelCase__    )
					logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''    )  # if this is wrong, check config.model_type.

					_snake_case						=					time.time()
					# update config with task specific params
					use_task_specific_params(UpperCamelCase__							,     UpperCamelCase__    )
					if prefix is None:
										_snake_case						=					prefix or getattr(model.config							,     'prefix'							,     ''    ) or ''
					for examples_chunk in tqdm(list(chunks(UpperCamelCase__							,     UpperCamelCase__    )    )    ):
										_snake_case						=					[prefix + text for text in examples_chunk]
										_snake_case						=					tokenizer(UpperCamelCase__							,     return_tensors='pt'							,     truncation=UpperCamelCase__							,     padding='longest'    ).to(UpperCamelCase__    )
										_snake_case						=					model.generate(
										    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **UpperCamelCase__							,     )
										_snake_case						=					tokenizer.batch_decode(UpperCamelCase__							,     skip_special_tokens=UpperCamelCase__							,     clean_up_tokenization_spaces=UpperCamelCase__    )
										for hypothesis in dec:
															fout.write(hypothesis + '\n'    )
															fout.flush()
					fout.close()
					_snake_case						=					int(time.time() - start_time    )  # seconds
					_snake_case						=					len(UpperCamelCase__    )
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4    )}





def     lowerCamelCase__  (  )      ->			Dict:



					'''simple docstring'''




					return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]=True    )      ->			List[str]:



					'''simple docstring'''




					_snake_case						=					argparse.ArgumentParser()
					parser.add_argument('model_name'							,     type=UpperCamelCase__							,     help='like facebook/bart-large-cnn,t5-base, etc.'    )
					parser.add_argument('input_path'							,     type=UpperCamelCase__							,     help='like cnn_dm/test.source'    )
					parser.add_argument('save_path'							,     type=UpperCamelCase__							,     help='where to save summaries'    )
					parser.add_argument('--reference_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     help='like cnn_dm/test.target'    )
					parser.add_argument('--score_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default='metrics.json'							,     help='where to save metrics'    )
					parser.add_argument('--device'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='cuda, cuda:1, cpu etc.'    )
					parser.add_argument(
					    '--prefix'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='will be added to the begininng of src examples'    )
					parser.add_argument('--task'							,     type=UpperCamelCase__							,     default='summarization'							,     help='used for task_specific_params + metrics'    )
					parser.add_argument('--bs'							,     type=UpperCamelCase__							,     default=8							,     required=UpperCamelCase__							,     help='batch size'    )
					parser.add_argument(
					    '--n_obs'							,     type=UpperCamelCase__							,     default=-1							,     required=UpperCamelCase__							,     help='How many observations. Defaults to all.'    )
					parser.add_argument('--fp16'							,     action='store_true'    )
					parser.add_argument('--dump-args'							,     action='store_true'							,     help='print the custom hparams with the results'    )
					parser.add_argument(
					    '--info'							,     nargs='?'							,     type=UpperCamelCase__							,     const=datetime_now()							,     help=(
					        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
					        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
					    )							,     )
					# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
					_snake_case				,  _snake_case						=					parser.parse_known_args()
					_snake_case						=					parse_numeric_n_bool_cl_kwargs(UpperCamelCase__    )
					if parsed_args and verbose:
										print(F'''parsed the following generate kwargs: {parsed_args}'''    )
					with open(args.input_path    ) as f:
										_snake_case						=					[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										_snake_case						=					examples[: args.n_obs]
					Path(args.save_path    ).parent.mkdir(exist_ok=UpperCamelCase__    )
					if args.reference_path is None and Path(args.score_path    ).exists():
										warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''    )
					_snake_case						=					generate_summaries_or_translations(
					    UpperCamelCase__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **UpperCamelCase__							,     )

					if args.reference_path is None:
										return {}

					# Compute scores
					_snake_case						=					calculate_bleu if 'translation' in args.task else calculate_rouge
					_snake_case						=					[x.rstrip() for x in open(args.save_path    ).readlines()]
					_snake_case						=					[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(UpperCamelCase__    )]
					_snake_case						=					score_fn(UpperCamelCase__							,     UpperCamelCase__    )
					scores.update(UpperCamelCase__    )

					if args.dump_args:
										scores.update(UpperCamelCase__    )
					if args.info:
										_snake_case						=					args.info

					if verbose:
										print(UpperCamelCase__    )

					if args.score_path is not None:
										json.dump(UpperCamelCase__							,     open(args.score_path							,     'w'    )    )

					return scores


if __name__ == "__main__":
					# Usage for MT:
					# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
					run_generate(verbose=True)



