



"""simple docstring"""







import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase:   Optional[int]       			=							{512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


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


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase	):
		_lowercase							:      str			=					download_model(pretrained_models[args.image_size]	)

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

				_lowercase							:      Any			=					state_dict[
				    F"""blocks.{depth}.adaLN_modulation.1.weight"""
				]
				_lowercase							:      Tuple			=					state_dict[
				    F"""blocks.{depth}.adaLN_modulation.1.bias"""
				]

				_lowercase			,   _lowercase			,   _lowercase							:      Tuple			=					torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.weight"""]					,							3					,							dim=0	)
				_lowercase			,   _lowercase			,   _lowercase							:      int			=					torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.bias"""]					,							3					,							dim=0	)

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

				_lowercase							:      Tuple			=					state_dict[
				    F"""blocks.{depth}.attn.proj.weight"""
				]
				_lowercase							:      List[Any]			=					state_dict[F"""blocks.{depth}.attn.proj.bias"""]

				_lowercase							:      Optional[int]			=					state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
				_lowercase							:      Optional[Any]			=					state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
				_lowercase							:      int			=					state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
				_lowercase							:      Optional[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"""	)

		_lowercase							:      Dict			=					state_dict["""final_layer.adaLN_modulation.1.weight"""]
		_lowercase							:      List[str]			=					state_dict["""final_layer.adaLN_modulation.1.bias"""]
		_lowercase							:      Union[str, Any]			=					state_dict["""final_layer.linear.weight"""]
		_lowercase							:      Optional[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
		_lowercase							:      Optional[int]			=					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	)

		_lowercase							:      List[Any]			=					DDIMScheduler(
		    num_train_timesteps=1000					,							beta_schedule="""linear"""					,							prediction_type="""epsilon"""					,							clip_sample=_snake_case					,							)

		_lowercase							:      Any			=					AutoencoderKL.from_pretrained(args.vae_model	)

		_lowercase							:      List[Any]			=					DiTPipeline(transformer=_snake_case					,							vae=_snake_case					,							scheduler=_snake_case	)

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


if __name__ == "__main__":
		UpperCAmelCase:   List[Any]       			=							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:   Optional[int]       			=							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}""")



