






"""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 _a (		_snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       F'''pretrained_models/{model_name}'''
      if not os.path.isfile(_snake_case							):
            os.makedirs("""pretrained_models"""      ,       exist_ok=_snake_case							)
            UpperCAmelCase							=       F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
            download_url(_snake_case      ,       """pretrained_models"""							)
      UpperCAmelCase							=       torch.load(_snake_case      ,       map_location=lambda _snake_case      ,       _snake_case							: storage							)
      return model

def _a (		_snake_case							):







      """simple docstring"""






      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=_snake_case      ,       num_attention_heads=16      ,       activation_fn="""gelu-approximate"""      ,       num_embeds_ada_norm=1000      ,       norm_type="""ada_norm_zero"""      ,       norm_elementwise_affine=_snake_case      ,       )
      transformer.load_state_dict(_snake_case      ,       strict=_snake_case							)

      UpperCAmelCase							=       DDIMScheduler(
          num_train_timesteps=1000      ,       beta_schedule="""linear"""      ,       prediction_type="""epsilon"""      ,       clip_sample=_snake_case      ,       )

      UpperCAmelCase							=       AutoencoderKL.from_pretrained(args.vae_model							)

      UpperCAmelCase							=       DiTPipeline(transformer=_snake_case      ,       vae=_snake_case      ,       scheduler=_snake_case							)

      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"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

_UpperCamelCase     			=  {
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

_UpperCamelCase     			=  datasets.load_dataset("""xnli""", split="""test+validation""")

_UpperCamelCase     			=  0
_UpperCamelCase     			=  0
_UpperCamelCase     			=  0
_UpperCamelCase     			=  0

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      if spm_diff == list(reversed(_snake_case							)							):
            # AAA -> AA+A vs A+AA case.
            return True
      elif len(_snake_case							) == len(_snake_case							) and fast.decode(_snake_case							) == fast.decode(_snake_case							):
            # Second order OK
            # Barrich -> Barr + ich vs Bar + rich
            return True
      UpperCAmelCase							=       slow.encode(slow.decode(_snake_case							)							)
      UpperCAmelCase							=       fast.encode(fast.decode(_snake_case							)							)
      if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
            # Type 3 error.
            # Snehagatha ->
            #       Sne, h, aga, th, a
            #       Sne, ha, gat, ha
            # Encoding the wrong with sp does not even recover what spm gave us
            # It fits tokenizer however...
            return True
      return False

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       fast.encode_plus(_snake_case							)[0]
      UpperCAmelCase							=       enc.offsets
      UpperCAmelCase			,					UpperCAmelCase							=       offsets[idx], offsets[idx - 1]
      if curr is not None and line[curr[0] : curr[1]] == "\u200f":
            return True
      if prev is not None and line[prev[0] : prev[1]] == "\u200f":
            return True

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      for i, (spm_id, tok_id) in enumerate(zip(_snake_case      ,       _snake_case							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(_snake_case							)      ,       reversed(_snake_case							)							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       len(_snake_case							) - i

      UpperCAmelCase							=       spm_ids[first:last]
      UpperCAmelCase							=       tok_ids[first:last]

      if check_diff(_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):
            return True

      if check_LTR_mark(_snake_case      ,       _snake_case      ,       _snake_case							):
            return True

      if last - first > 5:
            # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
            UpperCAmelCase							=       Counter(spm_ids[first:last]							)
            UpperCAmelCase							=       Counter(tok_ids[first:last]							)

            UpperCAmelCase							=       {spm_ for (spm_, si) in spms.items() if toks.get(spm_      ,       0							) == si}
            UpperCAmelCase							=       3
            for i in range(last - first - min_width							):
                  if all(spm_ids[first + i + j] in removable_tokens for j in range(_snake_case							)							):
                        UpperCAmelCase							=       [
                            k
                            for k in range(last - first - min_width							)
                            if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                        ]
                        for j in possible_matches:
                              if check_diff(spm_ids[first : first + i]      ,       tok_ids[first : first + j]      ,       _snake_case      ,       _snake_case							) and check_details(
                                  _snake_case      ,       spm_ids[first + i : last]      ,       tok_ids[first + j : last]      ,       _snake_case      ,       _snake_case      ,       ):
                                    return True

      print(F'''Spm: {[fast.decode([spm_ids[i]]							) for i in range(_snake_case      ,       _snake_case							)]}'''							)
      try:
            print(F'''Tok: {[fast.decode([tok_ids[i]]							) for i in range(_snake_case      ,       _snake_case							)]}'''							)
      except Exception:
            pass

      UpperCAmelCase							=       fast.decode(spm_ids[:first]							)
      UpperCAmelCase							=       fast.decode(spm_ids[last:]							)
      UpperCAmelCase							=       fast.decode(spm_ids[first:last]							)
      print()
      print(_snake_case							)
      return False

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      global perfect
      global imperfect
      global wrong
      global total

      UpperCAmelCase							=       slow.encode(_snake_case							)
      UpperCAmelCase							=       fast.encode(_snake_case							)

      UpperCAmelCase							=       False
      total += 1

      if slow_ids != fast_ids:
            if check_details(_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):
                  UpperCAmelCase							=       True
                  imperfect += 1
            else:
                  wrong += 1
      else:
            perfect += 1

      if total % 1_0000 == 0:
            print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''							)

      if skip_assert:
            return

      assert (
          slow_ids == fast_ids
      ), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(_snake_case							)}\n{fast.tokenize(_snake_case							)}'''

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      global batch_total
      for i in range(len(_snake_case							)							):
            # premise, all languages
            for text in dataset[i]["premise"].values():
                  test_string(_snake_case      ,       _snake_case      ,       _snake_case							)

            # hypothesis, all languages
            for text in dataset[i]["hypothesis"]["translation"]:
                  test_string(_snake_case      ,       _snake_case      ,       _snake_case							)


if __name__ == "__main__":
  for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
    _UpperCamelCase     			=  list(slow_class.max_model_input_sizes.keys())
    for checkpoint in checkpoint_names:
      _UpperCamelCase     			=  0
      _UpperCamelCase     			=  0
      _UpperCamelCase     			=  0
      _UpperCamelCase     			=  0

      print(F"""========================== Checking {name}: {checkpoint} ==========================""")
      _UpperCamelCase     			=  slow_class.from_pretrained(checkpoint, force_download=True)
      _UpperCamelCase     			=  fast_class.from_pretrained(checkpoint, force_download=True)
      test_tokenizer(slow, fast)
      print(F"""Accuracy {perfect * 100 / total:.2f}""")



