




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a__		    =    logging.getLogger(__name__)


@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	__magic_name__      :					str			  =		field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
	__magic_name__      :					Optional[str]			  =		field(
	    default=_UpperCamelCase  ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
	__magic_name__      :					Optional[str]			  =		field(
	    default=_UpperCamelCase  ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
	__magic_name__      :					Optional[str]			  =		field(
	    default=_UpperCamelCase  ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,     )






@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	__magic_name__      :					str			  =		field(
	    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()      )}      )
	__magic_name__      :					str			  =		field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}      )
	__magic_name__      :					int			  =		field(
	    default=128  ,     metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }  ,     )
	__magic_name__      :					bool			  =		field(
	    default=_UpperCamelCase  ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}      )






def 							A__   (snake_case      :		List[InputFeatures]							)    ->	Dict[str, torch.Tensor]:
					__UpperCamelCase				:  int								=			default_data_collator(snake_case							)
					__UpperCamelCase				:  int								=			batch.pop("""pairID"""		,  snake_case							)
					return batch





def 							A__   ()    ->	List[str]:
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					__UpperCamelCase				:  Optional[Any]								=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
					__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase				:  List[Any]								=			parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir							)
					    and os.listdir(training_args.output_dir							)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    """ --overwrite_output_dir to overcome."""							)

					# Setup logging
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,  datefmt="""%m/%d/%Y %H:%M:%S"""		,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,  )
					logger.warning(
					    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""		,  training_args.local_rank		,  training_args.device		,  training_args.n_gpu		,  bool(training_args.local_rank != -1							)		,  training_args.fpaa		,  )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank							):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info("""Training/evaluation parameters %s"""		,  snake_case							)

					# Set seed
					set_seed(training_args.seed							)

					try:
										__UpperCamelCase				:  str								=			hans_tasks_num_labels[data_args.task_name]
					except KeyError:
										raise ValueError("""Task not found: %s""" % (data_args.task_name)							)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__UpperCamelCase				:  int								=			AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,  num_labels=snake_case		,  finetuning_task=data_args.task_name		,  cache_dir=model_args.cache_dir		,  )
					__UpperCamelCase				:  Optional[int]								=			AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )
					__UpperCamelCase				:  Any								=			AutoModelForSequenceClassification.from_pretrained(
					    model_args.model_name_or_path		,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path							)		,  config=snake_case		,  cache_dir=model_args.cache_dir		,  )

					# Get datasets
					__UpperCamelCase				:  Any								=			(
					    HansDataset(
					        data_dir=data_args.data_dir		,  tokenizer=snake_case		,  task=data_args.task_name		,  max_seq_length=data_args.max_seq_length		,  overwrite_cache=data_args.overwrite_cache		,  )
					    if training_args.do_train
					    else None
					)
					__UpperCamelCase				:  Union[str, Any]								=			(
					    HansDataset(
					        data_dir=data_args.data_dir		,  tokenizer=snake_case		,  task=data_args.task_name		,  max_seq_length=data_args.max_seq_length		,  overwrite_cache=data_args.overwrite_cache		,  evaluate=snake_case		,  )
					    if training_args.do_eval
					    else None
					)

					# Initialize our Trainer
					__UpperCamelCase				:  Dict								=			Trainer(
					    model=snake_case		,  args=snake_case		,  train_dataset=snake_case		,  eval_dataset=snake_case		,  data_collator=snake_case		,  )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path							) else None							)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir							)

    # Evaluation
					if training_args.do_eval:
										logger.info("""*** Evaluate ***"""							)

										__UpperCamelCase				:  Optional[Any]								=			trainer.predict(snake_case							)
										__UpperCamelCase				:  Any								=			output.predictions
										__UpperCamelCase				:  Optional[Any]								=			np.argmax(snake_case		,  axis=1							)

										__UpperCamelCase				:  Tuple								=			[ex.pairID for ex in eval_dataset]
										__UpperCamelCase				:  Dict								=			os.path.join(training_args.output_dir		,  """hans_predictions.txt"""							)
										__UpperCamelCase				:  int								=			eval_dataset.get_labels()
										if trainer.is_world_master():
															with open(snake_case		,  """w"""							) as writer:
																				writer.write("""pairID,gold_label\n"""							)
																				for pid, pred in zip(snake_case		,  snake_case							):
																									writer.write("""ex""" + str(snake_case							) + """,""" + label_list[int(snake_case							)] + """\n"""							)

										trainer._log(output.metrics							)





def 							A__   (snake_case      :		Union[str, Any]							)    ->	Optional[Any]:
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
	main()












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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





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





def 							A__   (snake_case      :		Optional[Any]							)    ->	Union[str, Any]:
					__UpperCamelCase				:  Any								=			download_model(pretrained_models[args.image_size]							)

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

										__UpperCamelCase				:  List[str]								=			state_dict[
										    F'''blocks.{depth}.adaLN_modulation.1.weight'''
										]
										__UpperCamelCase				:  Union[str, Any]								=			state_dict[
										    F'''blocks.{depth}.adaLN_modulation.1.bias'''
										]

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

										__UpperCamelCase				:  Optional[Any]								=			q
										__UpperCamelCase				:  Tuple								=			q_bias
										__UpperCamelCase				:  List[str]								=			k
										__UpperCamelCase				:  Tuple								=			k_bias
										__UpperCamelCase				:  List[Any]								=			v
										__UpperCamelCase				:  Dict								=			v_bias

										__UpperCamelCase				:  Tuple								=			state_dict[
										    F'''blocks.{depth}.attn.proj.weight'''
										]
										__UpperCamelCase				:  Union[str, Any]								=			state_dict[F'''blocks.{depth}.attn.proj.bias''']

										__UpperCamelCase				:  List[Any]								=			state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
										__UpperCamelCase				:  List[str]								=			state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
										__UpperCamelCase				:  Optional[Any]								=			state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
										__UpperCamelCase				:  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				:  str								=			state_dict["""final_layer.adaLN_modulation.1.weight"""]
					__UpperCamelCase				:  List[str]								=			state_dict["""final_layer.adaLN_modulation.1.bias"""]
					__UpperCamelCase				:  Union[str, Any]								=			state_dict["""final_layer.linear.weight"""]
					__UpperCamelCase				:  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				:  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=snake_case		,  num_attention_heads=16		,  activation_fn="""gelu-approximate"""		,  num_embeds_ada_norm=10_00		,  norm_type="""ada_norm_zero"""		,  norm_elementwise_affine=snake_case		,  )
					transformer.load_state_dict(snake_case		,  strict=snake_case							)

					__UpperCamelCase				:  Union[str, Any]								=			DDIMScheduler(
					    num_train_timesteps=10_00		,  beta_schedule="""linear"""		,  prediction_type="""epsilon"""		,  clip_sample=snake_case		,  )

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

					__UpperCamelCase				:  str								=			DiTPipeline(transformer=snake_case		,  vae=snake_case		,  scheduler=snake_case							)

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


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

	a__		    =    parser.parse_args()
	main(args)






