

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_SCREAMING_SNAKE_CASE							 =			{5_1_2: '''DiT-XL-2-512x512.pt''', 2_5_6: '''DiT-XL-2-256x256.pt'''}





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





def  _snake_case		(snake_case_					:     Any  )							->		str:
  __UpperCAmelCase	:  str		=     download_model(pretrained_models[args.image_size]  )

  __UpperCAmelCase	:  str		=     state_dict['x_embedder.proj.weight']
  __UpperCAmelCase	:  List[Any]		=     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	:  List[Any]		=     state_dict[
        't_embedder.mlp.0.weight'
    ]
    __UpperCAmelCase	:  Tuple		=     state_dict[
        't_embedder.mlp.0.bias'
    ]
    __UpperCAmelCase	:  str		=     state_dict[
        't_embedder.mlp.2.weight'
    ]
    __UpperCAmelCase	:  List[Any]		=     state_dict[
        't_embedder.mlp.2.bias'
    ]
    __UpperCAmelCase	:  Optional[Any]		=     state_dict[
        'y_embedder.embedding_table.weight'
    ]

    __UpperCAmelCase	:  str		=     state_dict[
        F"blocks.{depth}.adaLN_modulation.1.weight"
    ]
    __UpperCAmelCase	:  Union[str, Any]		=     state_dict[
        F"blocks.{depth}.adaLN_modulation.1.bias"
    ]

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

    __UpperCAmelCase	:  List[str]		=     q
    __UpperCAmelCase	:  str		=     q_bias
    __UpperCAmelCase	:  List[str]		=     k
    __UpperCAmelCase	:  Optional[Any]		=     k_bias
    __UpperCAmelCase	:  int		=     v
    __UpperCAmelCase	:  int		=     v_bias

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

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

  __UpperCAmelCase	:  Optional[int]		=     state_dict['final_layer.adaLN_modulation.1.weight']
  __UpperCAmelCase	:  Union[str, Any]		=     state_dict['final_layer.adaLN_modulation.1.bias']
  __UpperCAmelCase	:  str		=     state_dict['final_layer.linear.weight']
  __UpperCAmelCase	:  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
  __UpperCAmelCase	:  Dict		=     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	:  int		=     DDIMScheduler(
      num_train_timesteps=1000    ,       beta_schedule='linear'    ,       prediction_type='epsilon'    ,       clip_sample=snake_case_    ,       )

  __UpperCAmelCase	:  List[str]		=     AutoencoderKL.from_pretrained(args.vae_model  )

  __UpperCAmelCase	:  Any		=     DiTPipeline(transformer=snake_case_    ,       vae=snake_case_    ,       scheduler=snake_case_  )

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


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE							 =			argparse.ArgumentParser()

      parser.add_argument(
          '''--image_size''',
          default=2_5_6,
          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.'''
      )

      _SCREAMING_SNAKE_CASE							 =			parser.parse_args()
      main(args)








import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase_ (			_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	SpeechaTextaTokenizer
 _lowerCAmelCase									=	False




 def     a       (			self							):
   super().setUp()

   __UpperCAmelCase	:  List[Any]		=     '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '							)
   __UpperCAmelCase	:  Dict		=     ['he re</w> 123', 'here a 1456']
   __UpperCAmelCase	:  Optional[int]		=     dict(zip(_UpperCAmelCase  ,  range(len(_UpperCAmelCase							)							)							)							)

   __UpperCAmelCase	:  List[Any]		=     {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

   __UpperCAmelCase	:  Tuple		=     tempfile.mkdtemp()
   __UpperCAmelCase	:  str		=     os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['vocab_file']							)
   __UpperCAmelCase	:  List[Any]		=     os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['merges_file']							)

   with open(self.vocab_file  ,  'w'  ,  encoding='utf-8'							) as fp:
     fp.write(json.dumps(_UpperCAmelCase							) + '\n'							)

   with open(self.merges_file  ,  'w'							) as fp:
     fp.write('\n'.join(_UpperCAmelCase							)							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,  '<s>'							)
   self.assertEqual(vocab_keys[1]  ,  '<pad>'							)
   self.assertEqual(vocab_keys[-1]  ,  'vocab'							)
   self.assertEqual(len(_UpperCAmelCase							)  ,  14							)




 def     a       (			self							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,  14							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

   # make sure @@ is correctly concatenated
   __UpperCAmelCase	:  int		=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
   __UpperCAmelCase	:  List[str]		=     tokenizer.decode(_UpperCAmelCase							)

   self.assertTrue(output_string == 'herecouple words ofthe'							)




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

   with tempfile.TemporaryDirectory() as tmp_dirname:
     tokenizer.save_pretrained(_UpperCAmelCase							)
     os.remove(os.path.join(_UpperCAmelCase  ,  'merges.txt'							)							)

     # load tokenizer without merges file should not throw an error
     __UpperCAmelCase	:  Optional[int]		=     SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase							)

   with tempfile.TemporaryDirectory() as tmp_dirname:
     # save tokenizer and load again
     tokenizer.save_pretrained(_UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[int]		=     SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase							)

   self.assertIsNotNone(_UpperCAmelCase							)




 def     a       (			self							):
   if not self.test_slow_tokenizer:
     return

   __UpperCAmelCase	:  List[Any]		=     inspect.signature(self.tokenizer_class.__init__							)
   __UpperCAmelCase	:  List[Any]		=     self.get_tokenizer()

   for parameter_name, parameter in signature.parameters.items():
     if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
       self.assertIn(_UpperCAmelCase  ,  tokenizer.init_kwargs							)





