






"""simple docstring"""



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Union[str, Any]							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Optional[Any]				=				f'pretrained_models/{model_name}'
     if not os.path.isfile(snake_case							):
          os.makedirs("pretrained_models"       , exist_ok=snake_case							)
          UpperCAmelCase__		:  Any				=				f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
          download_url(snake_case       , "pretrained_models"							)
     UpperCAmelCase__		:  str				=				torch.load(snake_case       , map_location=lambda snake_case       , snake_case							: storage							)
     return model


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Optional[int]							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				download_model(pretrained_models[args.image_size]							)

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

          UpperCAmelCase__		:  Optional[Any]				=				state_dict[
              f'blocks.{depth}.adaLN_modulation.1.weight'
          ]
          UpperCAmelCase__		:  List[str]				=				state_dict[
              f'blocks.{depth}.adaLN_modulation.1.bias'
          ]

          UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  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__		:  Union[str, Any]				=				q
          UpperCAmelCase__		:  str				=				q_bias
          UpperCAmelCase__		:  Union[str, Any]				=				k
          UpperCAmelCase__		:  str				=				k_bias
          UpperCAmelCase__		:  Optional[Any]				=				v
          UpperCAmelCase__		:  Any				=				v_bias

          UpperCAmelCase__		:  List[str]				=				state_dict[
              f'blocks.{depth}.attn.proj.weight'
          ]
          UpperCAmelCase__		:  Dict				=				state_dict[f'blocks.{depth}.attn.proj.bias']

          UpperCAmelCase__		:  Union[str, Any]				=				state_dict[f'blocks.{depth}.mlp.fc1.weight']
          UpperCAmelCase__		:  Optional[int]				=				state_dict[f'blocks.{depth}.mlp.fc1.bias']
          UpperCAmelCase__		:  int				=				state_dict[f'blocks.{depth}.mlp.fc2.weight']
          UpperCAmelCase__		:  List[str]				=				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__		:  Any				=				state_dict["final_layer.adaLN_modulation.1.weight"]
     UpperCAmelCase__		:  Any				=				state_dict["final_layer.adaLN_modulation.1.bias"]
     UpperCAmelCase__		:  Dict				=				state_dict["final_layer.linear.weight"]
     UpperCAmelCase__		:  List[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__		:  Union[str, Any]				=				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__		:  List[str]				=				DDIMScheduler(
         num_train_timesteps=1000       , beta_schedule="linear"       , prediction_type="epsilon"       , clip_sample=snake_case       , )

     UpperCAmelCase__		:  Optional[int]				=				AutoencoderKL.from_pretrained(args.vae_model							)

     UpperCAmelCase__		:  Optional[Any]				=				DiTPipeline(transformer=snake_case       , vae=snake_case       , scheduler=snake_case							)

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


if __name__ == "__main__":
 _lowerCAmelCase     :     Optional[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."""
 )

 _lowerCAmelCase     :     int            =	parser.parse_args()
 main(args)




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		["input_values", "attention_mask"]





     def __init__(self      :    Tuple				,    _A      :    Optional[Any]=1				,    _A      :    Dict=1_6_0_0_0				,    _A      :    int=0.0				,    _A      :    List[str]=False				,    _A      :    Optional[int]=True				,    **_A      :    List[Any]				,    )						->						str:
         super().__init__(feature_size=_A				,    sampling_rate=_A				,    padding_value=_A				,    **_A   )
         snake_case      =					return_attention_mask
         snake_case      =					do_normalize





     @staticmethod
     def 		UpperCAmelCase(_A      :    List[np.ndarray]				,    _A      :    List[np.ndarray]				,    _A      :    float = 0.0   )						->						List[np.ndarray]:
         if attention_mask is not None:
             snake_case      =					np.array(_A				,    np.intaa   )
             snake_case      =					[]

             for vector, length in zip(_A				,    attention_mask.sum(-1   )   ):
                 snake_case      =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
                 if length < normed_slice.shape[0]:
                     snake_case      =					padding_value

                 normed_input_values.append(_A   )
         else:
             snake_case      =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

         return normed_input_values





     def __call__(self      :    Optional[Any]				,    _A      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,    _A      :    Union[bool, str, PaddingStrategy] = False				,    _A      :    Optional[int] = None				,    _A      :    bool = False				,    _A      :    Optional[int] = None				,    _A      :    Optional[bool] = None				,    _A      :    Optional[Union[str, TensorType]] = None				,    _A      :    Optional[int] = None				,    **_A      :    List[Any]				,    )						->						BatchFeature:

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                     f' {self.sampling_rate} and not {sampling_rate}.'   )
         else:
             logger.warning(
                 "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                 "Failing to do so can result in silent errors that might be hard to debug."   )

         snake_case      =					isinstance(_A				,    np.ndarray   ) and len(raw_speech.shape   ) > 1
         if is_batched_numpy and len(raw_speech.shape   ) > 2:
             raise ValueError(f'Only mono-channel audio is supported for input to {self}'   )
         snake_case      =					is_batched_numpy or (
             isinstance(_A				,    (list, tuple)   ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)   ))
         )

         # always return batch
         if not is_batched:
             snake_case      =					[raw_speech]

         # convert into correct format for padding
         snake_case      =					BatchFeature({"input_values": raw_speech}   )

         snake_case      =					self.pad(
             _A				,    padding=_A				,    max_length=_A				,    truncation=_A				,    pad_to_multiple_of=_A				,    return_attention_mask=_A				,    )

         # convert input values to correct format
         snake_case      =					padded_inputs["input_values"]
         if not isinstance(input_values[0]				,    np.ndarray   ):
             snake_case      =					[np.asarray(_A				,    dtype=np.floataa   ) for array in input_values]
         elif (
             not isinstance(_A				,    np.ndarray   )
             and isinstance(input_values[0]				,    np.ndarray   )
             and input_values[0].dtype is np.dtype(np.floataa   )
         ):
             snake_case      =					[array.astype(np.floataa   ) for array in input_values]
         elif isinstance(_A				,    np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
             snake_case      =					input_values.astype(np.floataa   )

         # convert attention_mask to correct format
         snake_case      =					padded_inputs.get("attention_mask"   )
         if attention_mask is not None:
             snake_case      =					[np.asarray(_A				,    dtype=np.intaa   ) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
             snake_case      =					(
                 attention_mask
                 if self._get_padding_strategies(_A				,    max_length=_A   ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             snake_case      =					self.zero_mean_unit_var_norm(
                 padded_inputs["input_values"]				,    attention_mask=_A				,    padding_value=self.padding_value   )

         if return_tensors is not None:
             snake_case      =					padded_inputs.convert_to_tensors(_A   )

         return padded_inputs

