




import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__SCREAMING_SNAKE_CASE     : Optional[Any]      =logging.get_logger(__name__)

if is_vision_available():
      import PIL.Image
      from PIL import Image
      from PIL.Image import Image as ImageType
else:
      __SCREAMING_SNAKE_CASE     : Dict      =object

if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf






class       A      :
     """simple docstring"""

     def __init__(     self	: Optional[int]						,	A__	: int      )					->							int:
           lowerCamelCase__   	=					value




     def __str__(     self	: List[Any]      )					->							Any:
           return self.to_string()




     def 			__snake_case (     self	: int      )					->							Tuple:
           logger.error(
               'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'      )
           return self._value




     def 			__snake_case (     self	: Optional[int]      )					->							Dict:
           logger.error(
               'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'      )
           return str(self._value      )






class       A      (		_a      ,    _a		):
     """simple docstring"""

     def 			__snake_case (     self	: int      )					->							Tuple:
           return self._value




     def 			__snake_case (     self	: List[str]      )					->							Optional[int]:
           return self._value






class       A      (		_a      ,    _a		):
     """simple docstring"""

     def __init__(     self	: int						,	A__	: Optional[int]      )					->							List[Any]:
           super().__init__(A__      )

           if not is_vision_available():
                 raise ImportError('PIL must be installed in order to handle images.'      )

           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None

           if isinstance(A__						,	A__      ):
                 lowerCamelCase__   	=					value
           elif isinstance(A__						,	(str, pathlib.Path)      ):
                 lowerCamelCase__   	=					value
           elif isinstance(A__						,	torch.Tensor      ):
                 lowerCamelCase__   	=					value
           else:
                 raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(A__      )}"""      )




     def 			__snake_case (     self	: Tuple						,	A__	: Union[str, Any]=None						,	A__	: int=None      )					->							Optional[int]:
           from IPython.display import Image, display

           display(Image(self.to_string()      )      )




     def 			__snake_case (     self	: Optional[Any]      )					->							List[str]:





           if self._raw is not None:
                 return self._raw

           if self._path is not None:
                 lowerCamelCase__   	=					Image.open(self._path      )
                 return self._raw




     def 			__snake_case (     self	: List[Any]      )					->							Optional[int]:





           if self._path is not None:
                 return self._path

           if self._raw is not None:
                 lowerCamelCase__   	=					tempfile.mkdtemp()
                 lowerCamelCase__   	=					os.path.join(A__						,	str(uuid.uuida()      ) + '.png'      )
                 self._raw.save(self._path      )

                 return self._path

           if self._tensor is not None:
                 lowerCamelCase__   	=					self._tensor.cpu().detach().numpy()

                 # There is likely simpler than load into image into save
                 lowerCamelCase__   	=					Image.fromarray((array * 2_5_5).astype(np.uinta      )      )

                 lowerCamelCase__   	=					tempfile.mkdtemp()
                 lowerCamelCase__   	=					os.path.join(A__						,	str(uuid.uuida()      ) + '.png'      )

                 img.save(self._path      )

                 return self._path






class       A      (		_a		):
     """simple docstring"""

     def __init__(     self	: List[Any]						,	A__	: int						,	A__	: Any=1_6_0_0_0      )					->							Union[str, Any]:
           super().__init__(A__      )

           if not is_soundfile_availble():
                 raise ImportError('soundfile must be installed in order to handle audio.'      )

           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None

           lowerCamelCase__   	=					samplerate

           if isinstance(A__						,	(str, pathlib.Path)      ):
                 lowerCamelCase__   	=					value
           elif isinstance(A__						,	torch.Tensor      ):
                 lowerCamelCase__   	=					value
           else:
                 raise ValueError(F"""Unsupported audio type: {type(A__      )}"""      )




     def 			__snake_case (     self	: Optional[int]						,	A__	: Tuple=None						,	A__	: Tuple=None      )					->							Optional[int]:
           from IPython.display import Audio, display

           display(Audio(self.to_string()						,	rate=self.samplerate      )      )




     def 			__snake_case (     self	: Optional[int]      )					->							int:





           if self._tensor is not None:
                 return self._tensor

           if self._path is not None:
                 lowerCamelCase__	,		lowerCamelCase__   	=					sf.read(self._path      )
                 lowerCamelCase__   	=					torch.tensor(A__      )
                 return self._tensor




     def 			__snake_case (     self	: Optional[Any]      )					->							Optional[Any]:





           if self._path is not None:
                 return self._path

           if self._tensor is not None:
                 lowerCamelCase__   	=					tempfile.mkdtemp()
                 lowerCamelCase__   	=					os.path.join(A__						,	str(uuid.uuida()      ) + '.wav'      )
                 sf.write(self._path						,	self._tensor						,	samplerate=self.samplerate      )
                 return self._path
__SCREAMING_SNAKE_CASE     : Any      ={"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
__SCREAMING_SNAKE_CASE     : Optional[int]      ={str: AgentText}

if is_vision_available():
      __SCREAMING_SNAKE_CASE     : List[str]      =AgentImage

def       lowerCAmelCase_      (	*lowerCamelCase:							List[Any]  ,					**lowerCamelCase:							Tuple    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					[(arg.to_raw() if isinstance(__lowercase  ,					__lowercase    ) else arg) for arg in args]
      lowerCamelCase__   	=					{k: (v.to_raw() if isinstance(__lowercase  ,					__lowercase    ) else v) for k, v in kwargs.items()}
      return args, kwargs


def       lowerCAmelCase_      (	lowerCamelCase:							List[Any]  ,					lowerCamelCase:							List[Any]=None    )       ->      List[Any]:


      '''simple docstring'''




      if isinstance(__lowercase  ,					__lowercase    ):
            lowerCamelCase__   	=					{}
            for i, (k, v) in enumerate(outputs.items()    ):
                  if output_types is not None:
                        # If the class has defined outputs, we can map directly according to the class definition
                        if output_types[i] in AGENT_TYPE_MAPPING:
                              lowerCamelCase__   	=					AGENT_TYPE_MAPPING[output_types[i]](__lowercase    )
                        else:
                              lowerCamelCase__   	=					AgentType(__lowercase    )

                  else:
                        # If the class does not have defined output, then we map according to the type
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(__lowercase  ,					_k    ):
                                    lowerCamelCase__   	=					_v(__lowercase    )
                        if k not in decoded_outputs:
                              lowerCamelCase__   	=					AgentType[v]

      elif isinstance(__lowercase  ,					(list, tuple)    ):
            lowerCamelCase__   	=					type(__lowercase    )()
            for i, v in enumerate(__lowercase    ):
                  if output_types is not None:
                        # If the class has defined outputs, we can map directly according to the class definition
                        if output_types[i] in AGENT_TYPE_MAPPING:
                              decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__lowercase    )    )
                        else:
                              decoded_outputs.append(AgentType(__lowercase    )    )
                  else:
                        # If the class does not have defined output, then we map according to the type
                        lowerCamelCase__   	=					False
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(__lowercase  ,					_k    ):
                                    decoded_outputs.append(_v(__lowercase    )    )
                                    lowerCamelCase__   	=					True

                        if not found:
                              decoded_outputs.append(AgentType(__lowercase    )    )

      else:
            if output_types[0] in AGENT_TYPE_MAPPING:
                  # If the class has defined outputs, we can map directly according to the class definition
                  lowerCamelCase__   	=					AGENT_TYPE_MAPPING[output_types[0]](__lowercase    )

            else:
                  # If the class does not have defined output, then we map according to the type
                  for _k, _v in INSTANCE_TYPE_MAPPING.items():
                        if isinstance(__lowercase  ,					_k    ):
                              return _v(__lowercase    )
                  return AgentType(__lowercase    )

      return decoded_outputs






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowercase				    =      {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

  __lowerCAmelCase								=					state_dict['''x_embedder.proj.weight''']
  __lowerCAmelCase								=					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):
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''y_embedder.embedding_table.weight'''
    ]

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.bias'''
    ]

    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']  ,				3  ,				dim=0)
    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']  ,				3  ,				dim=0)

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.attn.proj.weight'''
    ]
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.attn.proj.bias''']

    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
    __lowerCAmelCase								=					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''')

  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					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
  __lowerCAmelCase								=					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=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

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


if __name__ == "__main__":
      __lowercase				    =      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."""
      )

      __lowercase				    =      parser.parse_args()
      main(args)
