
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_A  					= {5_12: "DiT-XL-2-512x512.pt", 2_56: "DiT-XL-2-256x256.pt"}



def 						lowercase_			(   A__		)							->							Any:
    """simple docstring"""

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



def 						lowercase_			(   A__		)							->							List[str]:
    """simple docstring"""

    snake_case      =					download_model(pretrained_models[args.image_size]		)

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

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

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

        snake_case      =					q
        snake_case      =					q_bias
        snake_case      =					k
        snake_case      =					k_bias
        snake_case      =					v
        snake_case      =					v_bias

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

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

    snake_case      =					state_dict["final_layer.adaLN_modulation.1.weight"]
    snake_case      =					state_dict["final_layer.adaLN_modulation.1.bias"]
    snake_case      =					state_dict["final_layer.linear.weight"]
    snake_case      =					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
    snake_case      =					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=1000 ,		norm_type="ada_norm_zero" ,		norm_elementwise_affine=A__ ,		)
    transformer.load_state_dict(A__ ,		strict=A__		)

    snake_case      =					DDIMScheduler(
        num_train_timesteps=1000 ,		beta_schedule="linear" ,		prediction_type="epsilon" ,		clip_sample=A__ ,		)

    snake_case      =					AutoencoderKL.from_pretrained(args.vae_model		)

    snake_case      =					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=2_56,
       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)



import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





     def __init__(self      :    Dict				,    _A      :    Optional[Any]=1_0_0_0_0				,    _A      :    List[Any]=True				,    _A      :    Any=7_6_8				,    _A      :    Union[str, Any]=False				,    _A      :    List[Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    str=0.7				,    _A      :    Optional[int]=True				,    _A      :    Any=1_0_0_4_8				,    _A      :    Optional[int]=1_2_9_5_9_5				,    _A      :    Union[str, Any]=1_2_9_5_9_9				,    _A      :    Any=1_0_0_0_0				,    _A      :    str=2_5_6				,    _A      :    int=49.9				,    **_A      :    Union[str, Any]				,    )						->						Any:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    eos_token_id=_A				,    renormalize_logits=_A				,    max_new_tokens=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         snake_case      =					text_encoding_offset
         snake_case      =					text_pad_token
         snake_case      =					eos_token_id
         snake_case      =					semantic_infer_token
         snake_case      =					semantic_vocab_size
         snake_case      =					max_input_semantic_length
         snake_case      =					semantic_rate_hz







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"coarse_acoustics"





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						Union[str, Any]:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    renormalize_logits=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         snake_case      =					coarse_semantic_pad_token
         snake_case      =					coarse_rate_hz
         snake_case      =					n_coarse_codebooks
         snake_case      =					coarse_infer_token
         snake_case      =					max_coarse_input_length
         snake_case      =					max_coarse_history
         snake_case      =					sliding_window_len







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





     def __init__(self      :    Tuple				,    _A      :    Optional[int]=0.5				,    _A      :    List[str]=5_1_2				,    _A      :    Union[str, Any]=1_0_2_4				,    _A      :    Tuple=8				,    **_A      :    Tuple				,    )						->						Tuple:
         super().__init__(temperature=_A   )

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         if semantic_config is None:
             snake_case      =					{}
             logger.info("semantic_config is None. initializing the semantic model with default values."   )

         if coarse_acoustics_config is None:
             snake_case      =					{}
             logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."   )

         if fine_acoustics_config is None:
             snake_case      =					{}
             logger.info("fine_acoustics_config is None. initializing the fine model with default values."   )

         snake_case      =					BarkSemanticGenerationConfig(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, Any]:
         return cls(
             semantic_config=semantic_config.to_dict()				,    coarse_acoustics_config=coarse_acoustics_config.to_dict()				,    fine_acoustics_config=fine_acoustics_config.to_dict()				,    **_A				,    )





     def 		UpperCAmelCase(self      :    List[str]   )						->						int:
         snake_case      =					copy.deepcopy(self.__dict__   )

         snake_case      =					self.semantic_config.to_dict()
         snake_case      =					self.coarse_acoustics_config.to_dict()
         snake_case      =					self.fine_acoustics_config.to_dict()

         snake_case      =					self.__class__.model_type
         return output

