






"""simple docstring"""






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
    from tokenizers import Tokenizer
    from tokenizers.models import WordPiece
class 						__lowerCAmelCase   (			unittest.TestCase       ):







  '''simple docstring'''


  def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		List[str]					):
     UpperCamelCase_     =list(tokenizer_class.max_model_input_sizes.keys()					)
     for model_name in sa_models[:1]:
        UpperCamelCase_     =tokenizer_class.from_pretrained(UpperCamelCase_					)
        self.assertIsNotNone(UpperCamelCase_					)
        self.assertIsInstance(UpperCamelCase_				,			UpperCamelCase_					)
        self.assertIsInstance(UpperCamelCase_				,			UpperCamelCase_					)

        for special_tok in tokenizer.all_special_tokens:
           self.assertIsInstance(UpperCamelCase_				,			UpperCamelCase_					)
           UpperCamelCase_     =tokenizer.convert_tokens_to_ids(UpperCamelCase_					)
           self.assertIsInstance(UpperCamelCase_				,			UpperCamelCase_					)
  def 					UpperCamelCase__   (      self:		int				,			UpperCamelCase_:		BatchEncoding				,			UpperCamelCase_:		Optional[Callable] = None					):
     UpperCamelCase_     =pickle.dumps(UpperCamelCase_					)
     self.assertIsNotNone(UpperCamelCase_					)

     UpperCamelCase_     =pickle.loads(UpperCamelCase_					)

     # Ensure is_fast is correctly restored
     self.assertEqual(be_restored.is_fast				,			be_original.is_fast					)

     # Ensure encodings are potentially correctly restored
     if be_original.is_fast:
        self.assertIsNotNone(be_restored.encodings					)
     else:
        self.assertIsNone(be_restored.encodings					)

     # Ensure the keys are the same
     for original_v, restored_v in zip(be_original.values()				,			be_restored.values()					):
        if equal_op:
           self.assertTrue(equal_op(UpperCamelCase_				,			UpperCamelCase_					)					)
        else:
           self.assertEqual(UpperCamelCase_				,			UpperCamelCase_					)
  @slow
  def 					UpperCamelCase__   (      self:		Any					):
     self.check_tokenizer_from_pretrained(UpperCamelCase_					)
  def 					UpperCamelCase__   (      self:		Optional[Any]					):
     self.assertEqual(TensorType("tf"					)				,			TensorType.TENSORFLOW					)
     self.assertEqual(TensorType("pt"					)				,			TensorType.PYTORCH					)
     self.assertEqual(TensorType("np"					)				,			TensorType.NUMPY					)
  @require_tokenizers
  def 					UpperCamelCase__   (      self:		Tuple					):
     import numpy as np

     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-cased"					)
     UpperCamelCase_     =BertTokenizerFast.from_pretrained("bert-base-cased"					)

     # Python no tensor
     with self.subTest("BatchEncoding (Python, return_tensors=None)"					):
        self.assert_dump_and_restore(tokenizer_p("Small example to encode"					)					)

     with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"					):
        self.assert_dump_and_restore(
            tokenizer_p("Small example to encode"				,			return_tensors=TensorType.NUMPY					)				,			np.array_equal					)

     with self.subTest("BatchEncoding (Rust, return_tensors=None)"					):
        self.assert_dump_and_restore(tokenizer_r("Small example to encode"					)					)

     with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"					):
        self.assert_dump_and_restore(
            tokenizer_r("Small example to encode"				,			return_tensors=TensorType.NUMPY					)				,			np.array_equal					)
  @require_tf
  @require_tokenizers
  def 					UpperCamelCase__   (      self:		Dict					):
     import tensorflow as tf

     def tf_array_equals(UpperCamelCase_:		List[str]				,			UpperCamelCase_:		Optional[int]					):
        return tf.reduce_all(tf.equal(UpperCamelCase_				,			UpperCamelCase_					)					)

     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-cased"					)
     UpperCamelCase_     =BertTokenizerFast.from_pretrained("bert-base-cased"					)

     with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"					):
        self.assert_dump_and_restore(
            tokenizer_p("Small example to encode"				,			return_tensors=TensorType.TENSORFLOW					)				,			UpperCamelCase_					)

     with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"					):
        self.assert_dump_and_restore(
            tokenizer_r("Small example to encode"				,			return_tensors=TensorType.TENSORFLOW					)				,			UpperCamelCase_					)
  @require_torch
  @require_tokenizers
  def 					UpperCamelCase__   (      self:		List[Any]					):
     import torch

     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-cased"					)
     UpperCamelCase_     =BertTokenizerFast.from_pretrained("bert-base-cased"					)

     with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"					):
        self.assert_dump_and_restore(
            tokenizer_p("Small example to encode"				,			return_tensors=TensorType.PYTORCH					)				,			torch.equal					)

     with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"					):
        self.assert_dump_and_restore(
            tokenizer_r("Small example to encode"				,			return_tensors=TensorType.PYTORCH					)				,			torch.equal					)
  @require_tokenizers
  def 					UpperCamelCase__   (      self:		Optional[int]					):
     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-cased"					)
     UpperCamelCase_     =BertTokenizerFast.from_pretrained("bert-base-cased"					)

     with self.subTest("Python Tokenizer"					):
        self.assertFalse(tokenizer_p("Small example to_encode"					).is_fast					)

     with self.subTest("Rust Tokenizer"					):
        self.assertTrue(tokenizer_r("Small example to_encode"					).is_fast					)
  @require_tokenizers
  def 					UpperCamelCase__   (      self:		Optional[int]					):
     UpperCamelCase_     =BertTokenizerFast.from_pretrained("bert-base-cased"					)
     UpperCamelCase_     =tokenizer_r(["Test", "\xad", "test"]				,			is_split_into_words=UpperCamelCase_					)

     self.assertEqual(encoded.word_to_tokens(0					)				,			TokenSpan(start=1				,			end=2					)					)
     self.assertEqual(encoded.word_to_tokens(1					)				,			UpperCamelCase_					)
     self.assertEqual(encoded.word_to_tokens(2					)				,			TokenSpan(start=2				,			end=3					)					)
  def 					UpperCamelCase__   (      self:		Any					):
     UpperCamelCase_     =BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="np"					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(2, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(2,)					)
     # test converting the converted
     with CaptureStderr() as cs:
        UpperCamelCase_     =batch.convert_to_tensors(tensor_type="np"					)
     self.assertFalse(len(cs.err					)				,			msg=f"""should have no warning, but got {cs.err}"""					)

     UpperCamelCase_     =BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="np"				,			prepend_batch_axis=UpperCamelCase_					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(1, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(1,)					)
  @require_torch
  def 					UpperCamelCase__   (      self:		Dict					):
     UpperCamelCase_     =BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="pt"					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(2, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(2,)					)
     # test converting the converted
     with CaptureStderr() as cs:
        UpperCamelCase_     =batch.convert_to_tensors(tensor_type="pt"					)
     self.assertFalse(len(cs.err					)				,			msg=f"""should have no warning, but got {cs.err}"""					)

     UpperCamelCase_     =BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="pt"				,			prepend_batch_axis=UpperCamelCase_					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(1, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(1,)					)
  @require_tf
  def 					UpperCamelCase__   (      self:		Dict					):
     UpperCamelCase_     =BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="tf"					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(2, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(2,)					)
     # test converting the converted
     with CaptureStderr() as cs:
        UpperCamelCase_     =batch.convert_to_tensors(tensor_type="tf"					)
     self.assertFalse(len(cs.err					)				,			msg=f"""should have no warning, but got {cs.err}"""					)

     UpperCamelCase_     =BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="tf"				,			prepend_batch_axis=UpperCamelCase_					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(1, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(1,)					)
  @require_flax
  def 					UpperCamelCase__   (      self:		Union[str, Any]					):
     UpperCamelCase_     =BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="jax"					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(2, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(2,)					)
     # test converting the converted
     with CaptureStderr() as cs:
        UpperCamelCase_     =batch.convert_to_tensors(tensor_type="jax"					)
     self.assertFalse(len(cs.err					)				,			msg=f"""should have no warning, but got {cs.err}"""					)

     UpperCamelCase_     =BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
     UpperCamelCase_     =batch.convert_to_tensors(tensor_type="jax"				,			prepend_batch_axis=UpperCamelCase_					)
     self.assertEqual(tensor_batch["inputs"].shape				,			(1, 3)					)
     self.assertEqual(tensor_batch["labels"].shape				,			(1,)					)
  def 					UpperCamelCase__   (      self:		Union[str, Any]					):
     UpperCamelCase_     =[{"input_ids": np.array([0, 1, 2]					)}, {"input_ids": np.array([0, 1, 2, 3]					)}]
     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-cased"					)

     UpperCamelCase_     =tokenizer.pad(UpperCamelCase_				,			padding=UpperCamelCase_					)
     self.assertTrue(isinstance(batch["input_ids"]				,			np.ndarray					)					)
     self.assertEqual(batch["input_ids"].tolist()				,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
     UpperCamelCase_     =tokenizer.pad(UpperCamelCase_				,			padding=UpperCamelCase_				,			return_tensors="np"					)
     self.assertTrue(isinstance(batch["input_ids"]				,			np.ndarray					)					)
     self.assertEqual(batch["input_ids"].tolist()				,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
  @require_torch
  def 					UpperCamelCase__   (      self:		int					):
     import torch

     UpperCamelCase_     =[{"input_ids": torch.tensor([0, 1, 2]					)}, {"input_ids": torch.tensor([0, 1, 2, 3]					)}]
     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-cased"					)

     UpperCamelCase_     =tokenizer.pad(UpperCamelCase_				,			padding=UpperCamelCase_					)
     self.assertTrue(isinstance(batch["input_ids"]				,			torch.Tensor					)					)
     self.assertEqual(batch["input_ids"].tolist()				,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
     UpperCamelCase_     =tokenizer.pad(UpperCamelCase_				,			padding=UpperCamelCase_				,			return_tensors="pt"					)
     self.assertTrue(isinstance(batch["input_ids"]				,			torch.Tensor					)					)
     self.assertEqual(batch["input_ids"].tolist()				,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
  @require_tf
  def 					UpperCamelCase__   (      self:		List[Any]					):
     import tensorflow as tf

     UpperCamelCase_     =[{"input_ids": tf.constant([0, 1, 2]					)}, {"input_ids": tf.constant([0, 1, 2, 3]					)}]
     UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-cased"					)

     UpperCamelCase_     =tokenizer.pad(UpperCamelCase_				,			padding=UpperCamelCase_					)
     self.assertTrue(isinstance(batch["input_ids"]				,			tf.Tensor					)					)
     self.assertEqual(batch["input_ids"].numpy().tolist()				,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
     UpperCamelCase_     =tokenizer.pad(UpperCamelCase_				,			padding=UpperCamelCase_				,			return_tensors="tf"					)
     self.assertTrue(isinstance(batch["input_ids"]				,			tf.Tensor					)					)
     self.assertEqual(batch["input_ids"].numpy().tolist()				,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
  @require_tokenizers
  def 					UpperCamelCase__   (      self:		Dict					):
     UpperCamelCase_     =Tokenizer(WordPiece(unk_token="[UNK]"					)					)
     PreTrainedTokenizerFast(tokenizer_object=UpperCamelCase_					)





  @require_tokenizers
  def 					UpperCamelCase__   (      self:		Any					):
     UpperCamelCase_     =Tokenizer(WordPiece(unk_token="[UNK]"					)					)
     with tempfile.TemporaryDirectory() as tmpdirname:
        bert_tokenizer.save(os.path.join(UpperCamelCase_				,			"tokenizer.json"					)					)
        PreTrainedTokenizerFast(tokenizer_file=os.path.join(UpperCamelCase_				,			"tokenizer.json"					)					)








"""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)
