






"""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_			  =    {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"}
A_			  =    set("".join(["‘", "’", "´", "`", ".", ",", "-", "\""]))




def 						_UpperCamelCase			(  A    ,       A=0    ,       A=None			):
   return [" ".join(x.split(" "			)[begin:end]			) for x in answers if len(x.split(" "			)			) > 1]




def 						_UpperCamelCase			(  A    ,       A=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"
      UpperCamelCase_     =(
          given_answers + get_sub_answers(lowercase__    ,       begin=1			) + get_sub_answers(lowercase__    ,       end=-1			)
      )
   UpperCamelCase_     =[]
   for answer in given_answers:
      UpperCamelCase_     =answer.replace("_"    ,       " "			).lower()
      UpperCamelCase_     ="".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias			)
      answers.append(" ".join(alias.split()			).strip()			)
   return set(lowercase__			)




def 						_UpperCamelCase			(  A    ,       A    ,       A=1    ,       A=100			):
   UpperCamelCase_     =jax.lax.top_k(lowercase__    ,       lowercase__			)
   UpperCamelCase_     =jax.lax.top_k(lowercase__    ,       lowercase__			)

   UpperCamelCase_     =best_end_idx[:, None] - best_start_idx[None, :]
   UpperCamelCase_     =jnp.logical_or(widths < 0    ,       widths > max_size			)
   UpperCamelCase_     =(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
   UpperCamelCase_     =jnp.argmax(lowercase__			).item()

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




def 						_UpperCamelCase			(  A			):
   UpperCamelCase_     =sample["question"]["text"]
   UpperCamelCase_     =sample["document"]["tokens"]["token"]
   UpperCamelCase_     =sample["document"]["tokens"]["is_html"]
   UpperCamelCase_     =sample["annotations"]["long_answer"]
   UpperCamelCase_     =sample["annotations"]["short_answers"]

   UpperCamelCase_     =" ".join([context[i] for i in range(len(lowercase__			)			) 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",
         }

   UpperCamelCase_     =[]
   for s in short_answers:
      short_targets.extend(s["text"]			)
   UpperCamelCase_     =list(set(lowercase__			)			)

   UpperCamelCase_     =[]
   for s in long_answers:
      if s["start_token"] == -1:
         continue
      UpperCamelCase_     =context[s["start_token"] : s["end_token"]]
      UpperCamelCase_     =is_html[s["start_token"] : s["end_token"]]
      UpperCamelCase_     =" ".join([answer[i] for i in range(len(lowercase__			)			) if not html[i]]			)
      if new_answer not in long_targets:
         long_targets.append(lowercase__			)

   UpperCamelCase_     ="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 						_UpperCamelCase			(  ):
   UpperCamelCase_     =load_from_disk("natural-questions-validation"			)
   UpperCamelCase_     =dataset.map(lowercase__			).remove_columns(["annotations", "document", "id"]			)
   print(lowercase__			)

   UpperCamelCase_     =dataset.filter(lambda A			: (len(x["question"]			) + len(x["context"]			)) < 4 * 4_096			)
   UpperCamelCase_     =short_validation_dataset.filter(lambda A			: x["category"] != "null"			)
   short_validation_dataset

   UpperCamelCase_     ="vasudevgupta/flax-bigbird-natural-questions"
   UpperCamelCase_     =FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase__			)
   UpperCamelCase_     =BigBirdTokenizerFast.from_pretrained(lowercase__			)

   @jax.jit
   def forward(*A    ,       **A			):
      UpperCamelCase_     =model(*lowercase__    ,       **lowercase__			)
      return start_logits, end_logits, jnp.argmax(lowercase__    ,       axis=-1			)

   def evaluate(A			):
      # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
      UpperCamelCase_     =tokenizer(
          example["question"]    ,       example["context"]    ,       return_tensors="np"    ,       max_length=4_096    ,       padding="max_length"    ,       truncation=lowercase__    ,       )

      UpperCamelCase_     =forward(**lowercase__			)

      UpperCamelCase_     =CATEGORY_MAPPING[category.item()]

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

      if predicted_category in ["yes", "no", "null"]:
         UpperCamelCase_     =[predicted_category]
         UpperCamelCase_     =example["output"] == example["targets"]
         UpperCamelCase_     =predicted_category != "null"
         return example

      UpperCamelCase_     =38 if predicted_category == "short" else 1_024
      UpperCamelCase_     =get_best_valid_start_end_idx(
          start_scores[0]    ,       end_scores[0]    ,       top_k=8    ,       max_size=lowercase__			)

      UpperCamelCase_     =inputs["input_ids"][0].tolist()
      UpperCamelCase_     =[tokenizer.decode(input_ids[start_score : end_score + 1]			)]

      UpperCamelCase_     =expand_to_aliases(example["targets"]    ,       make_sub_answers=lowercase__			)
      UpperCamelCase_     =expand_to_aliases(example["output"]			)

      # some preprocessing to both prediction and answer
      UpperCamelCase_     ={"".join(a.split()			) for a in answers}
      UpperCamelCase_     ={"".join(p.split()			) for p in predictions}
      UpperCamelCase_     ={s for s in predictions if s not in ["``", "''", "`", "'"]}

      # if there is a common element, it's a exact match
      UpperCamelCase_     =len(list(answers & predictions			)			) > 0
      UpperCamelCase_     =predicted_category != "null" and len(lowercase__			) > 0

      return example

   UpperCamelCase_     =short_validation_dataset.map(lowercase__			)

   UpperCamelCase_     =len(lowercase__			)
   UpperCamelCase_     =len(short_validation_dataset.filter(lambda A			: x["match"] == 1			)			)
   print("EM score:"    ,       (matched / total) * 100    ,       "%"			)


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_			  =    {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}




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




def 						_UpperCamelCase			(  A			):
   UpperCamelCase_     =download_model(pretrained_models[args.image_size]			)

   UpperCamelCase_     =state_dict["x_embedder.proj.weight"]
   UpperCamelCase_     =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_     =state_dict[
          "t_embedder.mlp.0.weight"
      ]
      UpperCamelCase_     =state_dict[
          "t_embedder.mlp.0.bias"
      ]
      UpperCamelCase_     =state_dict[
          "t_embedder.mlp.2.weight"
      ]
      UpperCamelCase_     =state_dict[
          "t_embedder.mlp.2.bias"
      ]
      UpperCamelCase_     =state_dict[
          "y_embedder.embedding_table.weight"
      ]

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

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

      UpperCamelCase_     =q
      UpperCamelCase_     =q_bias
      UpperCamelCase_     =k
      UpperCamelCase_     =k_bias
      UpperCamelCase_     =v
      UpperCamelCase_     =v_bias

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

      UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
      UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
      UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
      UpperCamelCase_     =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_     =state_dict["final_layer.adaLN_modulation.1.weight"]
   UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.bias"]
   UpperCamelCase_     =state_dict["final_layer.linear.weight"]
   UpperCamelCase_     =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_     =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=A    ,       num_attention_heads=16    ,       activation_fn="gelu-approximate"    ,       num_embeds_ada_norm=1_000    ,       norm_type="ada_norm_zero"    ,       norm_elementwise_affine=A    ,       )
   transformer.load_state_dict(A    ,       strict=A			)

   UpperCamelCase_     =DDIMScheduler(
       num_train_timesteps=1_000    ,       beta_schedule="linear"    ,       prediction_type="epsilon"    ,       clip_sample=A    ,       )

   UpperCamelCase_     =AutoencoderKL.from_pretrained(args.vae_model			)

   UpperCamelCase_     =DiTPipeline(transformer=A    ,       vae=A    ,       scheduler=A			)

   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)
