






"""simple docstring"""



import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__A		:	str      =		{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
__A		:	Union[str, Any]      =		set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))



def 						lowercase   (    __snake_case    :			Tuple		, __snake_case    :			Optional[Any]=0		, __snake_case    :			List[str]=None     ):
		return [" ".join(x.split(''' '''     )[begin:end]     ) for x in answers if len(x.split(''' '''     )     ) > 1]



def 						lowercase   (    __snake_case    :			List[Any]		, __snake_case    :			Any=False     ):
		if make_sub_answers:
				# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
				# *e.g.* if the correct answer contains a prefix such as "the", or "a"
				lowercase_			:			List[Any]								=       (
				    given_answers + get_sub_answers(__snake_case		, begin=1     ) + get_sub_answers(__snake_case		, end=-1     )
				)
		lowercase_			:			List[str]								=       []
		for answer in given_answers:
				lowercase_			:			str								=       answer.replace('''_'''		, ''' '''     ).lower()
				lowercase_			:			Dict								=       ''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias     )
				answers.append(''' '''.join(alias.split()     ).strip()     )
		return set(__snake_case     )



def 						lowercase   (    __snake_case    :			Tuple		, __snake_case    :			Tuple		, __snake_case    :			Optional[int]=1		, __snake_case    :			List[Any]=1_0_0     ):
		lowercase_	, lowercase_			:			str								=       jax.lax.top_k(__snake_case		, __snake_case     )
		lowercase_	, lowercase_			:			int								=       jax.lax.top_k(__snake_case		, __snake_case     )

		lowercase_			:			Optional[Any]								=       best_end_idx[:, None] - best_start_idx[None, :]
		lowercase_			:			Optional[int]								=       jnp.logical_or(widths < 0		, widths > max_size     )
		lowercase_			:			List[Any]								=       (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
		lowercase_			:			Optional[int]								=       jnp.argmax(__snake_case     ).item()

		return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]



def 						lowercase   (    __snake_case    :			Union[str, Any]     ):
		lowercase_			:			int								=       sample['''question''']['''text''']
		lowercase_			:			List[str]								=       sample['''document''']['''tokens''']['''token''']
		lowercase_			:			Dict								=       sample['''document''']['''tokens''']['''is_html''']
		lowercase_			:			Union[str, Any]								=       sample['''annotations''']['''long_answer''']
		lowercase_			:			Union[str, Any]								=       sample['''annotations''']['''short_answers''']

		lowercase_			:			Any								=       ''' '''.join([context[i] for i in range(len(__snake_case     )     ) if not is_html[i]]     )

		# 0 - No ; 1 - Yes
		for answer in sample["annotations"]["yes_no_answer"]:
				if answer == 0 or answer == 1:
						return {
						    "question": question,
						    "context": context_string,
						    "short": [],
						    "long": [],
						    "category": "no" if answer == 0 else "yes",
						}

		lowercase_			:			Optional[Any]								=       []
		for s in short_answers:
				short_targets.extend(s['''text''']     )
		lowercase_			:			str								=       list(set(__snake_case     )     )

		lowercase_			:			List[Any]								=       []
		for s in long_answers:
				if s["start_token"] == -1:
						continue
				lowercase_			:			List[Any]								=       context[s['''start_token'''] : s['''end_token''']]
				lowercase_			:			Dict								=       is_html[s['''start_token'''] : s['''end_token''']]
				lowercase_			:			Tuple								=       ''' '''.join([answer[i] for i in range(len(__snake_case     )     ) if not html[i]]     )
				if new_answer not in long_targets:
						long_targets.append(__snake_case     )

		lowercase_			:			int								=       '''long_short''' if len(short_targets + long_targets     ) > 0 else '''null'''

		return {
		    "question": question,
		    "context": context_string,
		    "short": short_targets,
		    "long": long_targets,
		    "category": category,
		}



def 						lowercase   (    ):
		lowercase_			:			str								=       load_from_disk('''natural-questions-validation'''     )
		lowercase_			:			Tuple								=       dataset.map(__snake_case     ).remove_columns(['''annotations''', '''document''', '''id''']     )
		print(__snake_case     )

		lowercase_			:			Optional[Any]								=       dataset.filter(lambda __snake_case     : (len(x['''question''']     ) + len(x['''context''']     )) < 4 * 4_0_9_6     )
		lowercase_			:			Union[str, Any]								=       short_validation_dataset.filter(lambda __snake_case     : x["category"] != "null"     )
		short_validation_dataset

		lowercase_			:			Tuple								=       '''vasudevgupta/flax-bigbird-natural-questions'''
		lowercase_			:			Dict								=       FlaxBigBirdForNaturalQuestions.from_pretrained(__snake_case     )
		lowercase_			:			Union[str, Any]								=       BigBirdTokenizerFast.from_pretrained(__snake_case     )

		@jax.jit
		def forward(*__snake_case    :			Any		, **__snake_case    :			Optional[int]     ):
				lowercase_	, lowercase_	, lowercase_			:			List[str]								=       model(*__snake_case		, **__snake_case     )
				return start_logits, end_logits, jnp.argmax(__snake_case		, axis=-1     )

		def evaluate(__snake_case    :			Dict     ):
				# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
				lowercase_			:			int								=       tokenizer(
				    example['''question''']		, example['''context''']		, return_tensors='''np'''		, max_length=4_0_9_6		, padding='''max_length'''		, truncation=__snake_case		, )

				lowercase_	, lowercase_	, lowercase_			:			Any								=       forward(**__snake_case     )

				lowercase_			:			Optional[int]								=       CATEGORY_MAPPING[category.item()]

				lowercase_			:			Union[str, Any]								=       example['''long'''] + example['''short''']
				if example["category"] in ["yes", "no", "null"]:
						lowercase_			:			str								=       [example['''category''']]
				lowercase_			:			Any								=       example['''category'''] != '''null'''
				# Now target can be: "yes", "no", "null", "list of long & short answers"

				if predicted_category in ["yes", "no", "null"]:
						lowercase_			:			Dict								=       [predicted_category]
						lowercase_			:			Optional[int]								=       example['''output'''] == example['''targets''']
						lowercase_			:			Optional[int]								=       predicted_category != '''null'''
						return example

				lowercase_			:			Dict								=       3_8 if predicted_category == '''short''' else 1_0_2_4
				lowercase_	, lowercase_			:			Any								=       get_best_valid_start_end_idx(
				    start_scores[0]		, end_scores[0]		, top_k=8		, max_size=__snake_case     )

				lowercase_			:			Tuple								=       inputs['''input_ids'''][0].tolist()
				lowercase_			:			str								=       [tokenizer.decode(input_ids[start_score : end_score + 1]     )]

				lowercase_			:			List[Any]								=       expand_to_aliases(example['''targets''']		, make_sub_answers=__snake_case     )
				lowercase_			:			Optional[int]								=       expand_to_aliases(example['''output''']     )

				# some preprocessing to both prediction and answer
				lowercase_			:			Union[str, Any]								=       {''''''.join(a.split()     ) for a in answers}
				lowercase_			:			Any								=       {''''''.join(p.split()     ) for p in predictions}
				lowercase_			:			int								=       {s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

				# if there is a common element, it's a exact match
				lowercase_			:			List[Any]								=       len(list(answers & predictions     )     ) > 0
				lowercase_			:			List[str]								=       predicted_category != '''null''' and len(__snake_case     ) > 0

				return example

		lowercase_			:			Optional[Any]								=       short_validation_dataset.map(__snake_case     )

		lowercase_			:			Union[str, Any]								=       len(__snake_case     )
		lowercase_			:			Tuple								=       len(short_validation_dataset.filter(lambda __snake_case     : x["match"] == 1     )     )
		print('''EM score:'''		, (matched / total) * 1_0_0		, '''%'''     )


if __name__ == "__main__":
						main()











"""simple docstring"""



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A		:	Union[str, Any]      =		{512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}



def 						lowercase   (    __snake_case    :			Optional[int]     ):
		lowercase_			:			Any								=       F'''pretrained_models/{model_name}'''
		if not os.path.isfile(__snake_case     ):
				os.makedirs('''pretrained_models'''		, exist_ok=__snake_case     )
				lowercase_			:			str								=       F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
				download_url(__snake_case		, '''pretrained_models'''     )
		lowercase_			:			List[str]								=       torch.load(__snake_case		, map_location=lambda __snake_case		, __snake_case     : storage     )
		return model



def 						lowercase   (    __snake_case    :			Optional[int]     ):
		lowercase_			:			Dict								=       download_model(pretrained_models[args.image_size]     )

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

				lowercase_			:			List[str]								=       state_dict[
				    F'''blocks.{depth}.adaLN_modulation.1.weight'''
				]
				lowercase_			:			Optional[Any]								=       state_dict[
				    F'''blocks.{depth}.adaLN_modulation.1.bias'''
				]

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

				lowercase_			:			Union[str, Any]								=       q
				lowercase_			:			Dict								=       q_bias
				lowercase_			:			Dict								=       k
				lowercase_			:			Tuple								=       k_bias
				lowercase_			:			str								=       v
				lowercase_			:			Union[str, Any]								=       v_bias

				lowercase_			:			Optional[Any]								=       state_dict[
				    F'''blocks.{depth}.attn.proj.weight'''
				]
				lowercase_			:			List[Any]								=       state_dict[F'''blocks.{depth}.attn.proj.bias''']

				lowercase_			:			List[Any]								=       state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
				lowercase_			:			Optional[Any]								=       state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
				lowercase_			:			Tuple								=       state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
				lowercase_			:			List[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'''     )

		lowercase_			:			Union[str, Any]								=       state_dict['''final_layer.adaLN_modulation.1.weight''']
		lowercase_			:			Optional[Any]								=       state_dict['''final_layer.adaLN_modulation.1.bias''']
		lowercase_			:			Dict								=       state_dict['''final_layer.linear.weight''']
		lowercase_			:			Union[str, 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
		lowercase_			:			List[str]								=       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=__snake_case		, 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=__snake_case		, )
		transformer.load_state_dict(__snake_case		, strict=__snake_case     )

		lowercase_			:			Any								=       DDIMScheduler(
		    num_train_timesteps=1_0_0_0		, beta_schedule='''linear'''		, prediction_type='''epsilon'''		, clip_sample=__snake_case		, )

		lowercase_			:			Dict								=       AutoencoderKL.from_pretrained(args.vae_model     )

		lowercase_			:			str								=       DiTPipeline(transformer=__snake_case		, vae=__snake_case		, scheduler=__snake_case     )

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


if __name__ == "__main__":
						__A		:	int      =		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		:	Union[str, Any]      =		parser.parse_args()
						main(args)



