


'''simple docstring'''


import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__			=      logging.get_logger(__name__)

set_seed(770)


__magic_name__			=      {
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


__magic_name__			=      {
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

__magic_name__			=      os.path.dirname(os.path.abspath(__file__))
__magic_name__			=      os.path.join(os.path.expanduser('~'), '.cache')
__magic_name__			=      os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')




def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      str=False):
      A_						:  Union[str, Any]          =				model_type
      if use_small:
            key += "_small"
      return os.path.join(lowerCamelCase							,						REMOTE_MODEL_PATHS[key]["""file_name"""])




def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Tuple):
      os.makedirs(lowerCamelCase							,						exist_ok=lowerCamelCase)
      hf_hub_download(repo_id=lowerCamelCase							,						filename=lowerCamelCase							,						local_dir=lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Tuple=False							,						lowerCamelCase  :      Tuple="text"):
      if model_type == "text":
            A_						:  Dict          =				BarkSemanticModel
            A_						:  Dict          =				BarkSemanticConfig
            A_						:  List[Any]          =				BarkSemanticGenerationConfig
      elif model_type == "coarse":
            A_						:  str          =				BarkCoarseModel
            A_						:  Any          =				BarkCoarseConfig
            A_						:  Union[str, Any]          =				BarkCoarseGenerationConfig
      elif model_type == "fine":
            A_						:  int          =				BarkFineModel
            A_						:  Optional[Any]          =				BarkFineConfig
            A_						:  List[str]          =				BarkFineGenerationConfig
      else:
            raise NotImplementedError()
      A_						:  Any          =				F'{model_type}_small' if use_small else model_type
      A_						:  Optional[Any]          =				REMOTE_MODEL_PATHS[model_key]
      if not os.path.exists(lowerCamelCase):
            logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.')
            _download(model_info["""repo_id"""]							,						model_info["""file_name"""])
      A_						:  int          =				torch.load(lowerCamelCase							,						map_location=lowerCamelCase)
      # this is a hack
      A_						:  Any          =				checkpoint["""model_args"""]
      if "input_vocab_size" not in model_args:
            A_						:  List[str]          =				model_args["""vocab_size"""]
            A_						:  List[Any]          =				model_args["""vocab_size"""]
            del model_args["vocab_size"]

      # convert Bark model arguments to HF Bark model arguments
      A_						:  str          =				model_args.pop("""n_head""")
      A_						:  List[str]          =				model_args.pop("""n_embd""")
      A_						:  List[str]          =				model_args.pop("""n_layer""")

      A_						:  Optional[int]          =				ConfigClass(**checkpoint["""model_args"""])
      A_						:  List[str]          =				ModelClass(config=lowerCamelCase)
      A_						:  int          =				GenerationConfigClass()

      A_						:  List[Any]          =				model_generation_config
      A_						:  Any          =				checkpoint["""model"""]
      # fixup checkpoint
      A_						:  List[str]          =				"""_orig_mod."""
      for k, v in list(state_dict.items()):
            if k.startswith(lowerCamelCase):
                  # replace part of the key with corresponding layer name in HF implementation
                  A_						:  Union[str, Any]          =				k[len(lowerCamelCase) :]
                  for old_layer_name in new_layer_name_dict:
                        A_						:  List[str]          =				new_k.replace(lowerCamelCase							,						new_layer_name_dict[old_layer_name])

                  A_						:  Union[str, Any]          =				state_dict.pop(lowerCamelCase)

      A_						:  Any          =				set(state_dict.keys()) - set(model.state_dict().keys())
      A_						:  Dict          =				{k for k in extra_keys if not k.endswith(""".attn.bias""")}
      A_						:  str          =				set(model.state_dict().keys()) - set(state_dict.keys())
      A_						:  Optional[Any]          =				{k for k in missing_keys if not k.endswith(""".attn.bias""")}
      if len(lowerCamelCase) != 0:
            raise ValueError(F'extra keys found: {extra_keys}')
      if len(lowerCamelCase) != 0:
            raise ValueError(F'missing keys: {missing_keys}')
      model.load_state_dict(lowerCamelCase							,						strict=lowerCamelCase)
      A_						:  int          =				model.num_parameters(exclude_embeddings=lowerCamelCase)
      A_						:  Optional[Any]          =				checkpoint["""best_val_loss"""].item()
      logger.info(F'model loaded: {round(n_params/1E6							,						1)}M params, {round(lowerCamelCase							,						3)} loss')
      model.eval()
      model.to(lowerCamelCase)
      del checkpoint, state_dict

      return model




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Dict=False							,						lowerCamelCase  :      str="text"):
      if model_type not in ("text", "coarse", "fine"):
            raise NotImplementedError()

      A_						:  Tuple          =				"""cpu"""  # do conversion on cpu

      A_						:  str          =				_get_ckpt_path(lowerCamelCase							,						use_small=lowerCamelCase)
      A_						:  Optional[Any]          =				_load_model(lowerCamelCase							,						lowerCamelCase							,						model_type=lowerCamelCase							,						use_small=lowerCamelCase)

      # load bark initial model
      A_						:  Optional[Any]          =				_bark_load_model(lowerCamelCase							,						"""cpu"""							,						model_type=lowerCamelCase							,						use_small=lowerCamelCase)

      if model_type == "text":
            A_						:  Optional[int]          =				bark_model["""model"""]

      if model.num_parameters(exclude_embeddings=lowerCamelCase) != bark_model.get_num_params():
            raise ValueError("""initial and new models don't have the same number of parameters""")

      # check if same output as the bark model
      A_						:  Dict          =				5
      A_						:  Dict          =				10

      if model_type in ["text", "coarse"]:
            A_						:  Optional[int]          =				torch.randint(256							,						(batch_size, sequence_length)							,						dtype=torch.int)
            A_						:  Optional[int]          =				bark_model(lowerCamelCase)[0]

            A_						:  str          =				model(lowerCamelCase)

            # take last logits
            A_						:  List[str]          =				output_new_model_total.logits[:, [-1], :]

      else:
            A_						:  Tuple          =				3
            A_						:  Tuple          =				8
            A_						:  Tuple          =				torch.randint(256							,						(batch_size, sequence_length, n_codes_total)							,						dtype=torch.int)

            A_						:  int          =				model(lowerCamelCase							,						lowerCamelCase)
            A_						:  Any          =				bark_model(lowerCamelCase							,						lowerCamelCase)

            A_						:  Dict          =				output_new_model_total.logits

      # output difference should come from the difference of self-attention implementation design
      if output_new_model.shape != output_old_model.shape:
            raise ValueError("""initial and new outputs don't have the same shape""")
      if (output_new_model - output_old_model).abs().max().item() > 1E-3:
            raise ValueError("""initial and new outputs are not equal""")

      Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
      model.save_pretrained(lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Dict							,						):
      A_						:  List[str]          =				os.path.join(lowerCamelCase							,						lowerCamelCase)

      A_						:  Optional[int]          =				BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase							,						"""config.json"""))
      A_						:  int          =				BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase							,						"""config.json"""))
      A_						:  Any          =				BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase							,						"""config.json"""))
      A_						:  Optional[Any]          =				EncodecConfig.from_pretrained("""facebook/encodec_24khz""")

      A_						:  Any          =				BarkSemanticModel.from_pretrained(lowerCamelCase)
      A_						:  Optional[Any]          =				BarkCoarseModel.from_pretrained(lowerCamelCase)
      A_						:  Dict          =				BarkFineModel.from_pretrained(lowerCamelCase)
      A_						:  str          =				EncodecModel.from_pretrained("""facebook/encodec_24khz""")

      A_						:  Any          =				BarkConfig.from_sub_model_configs(
          lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

      A_						:  List[Any]          =				BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config							,						coarseAcoustic.generation_config							,						fineAcoustic.generation_config)

      A_						:  str          =				BarkModel(lowerCamelCase)

      A_						:  int          =				semantic
      A_						:  Tuple          =				coarseAcoustic
      A_						:  Any          =				fineAcoustic
      A_						:  Tuple          =				codec

      A_						:  List[Any]          =				bark_generation_config

      Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
      bark.save_pretrained(lowerCamelCase							,						repo_id=lowerCamelCase							,						push_to_hub=lowerCamelCase)


if __name__ == "__main__":
     __magic_name__			=      argparse.ArgumentParser()
     # Required parameters

     parser.add_argument('model_type', type=str, help='text, coarse or fine.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

     __magic_name__			=      parser.parse_args()

     load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							KandinskyVaaControlnetPipeline
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a_  					=							False






  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return self.time_input_dim






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return self.time_input_dim * 4






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 100






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  List[Any]          =				{
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        A_						:  Tuple          =				UNetaDConditionModel(**_a )
        return model






  @property
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  int          =				VQModel(**self.dummy_movq_kwargs )
        return model






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.dummy_unet
        A_						:  int          =				self.dummy_movq

        A_						:  Tuple          =				DDIMScheduler(
            num_train_timesteps=1000	,beta_schedule="""linear"""	,beta_start=0.00085	,beta_end=0.012	,clip_sample=_a	,set_alpha_to_one=_a	,steps_offset=1	,prediction_type="""epsilon"""	,thresholding=_a	,)

        A_						:  int          =				{
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






  def        _a							(							self :       Dict	,_a :       str	,_a :       Union[str, Any]=0 ):

        '''simple docstring'''




        A_						:  Dict          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(_a ) ).to(_a )
        A_						:  int          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1 ) ).to(
            _a )

        # create hint
        A_						:  List[Any]          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_a ) ).to(_a )

        if str(_a ).startswith("""mps""" ):
              A_						:  Optional[Any]          =				torch.manual_seed(_a )
        else:
              A_						:  str          =				torch.Generator(device=_a ).manual_seed(_a )
        A_						:  List[Any]          =				{
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        A_						:  List[Any]          =				"""cpu"""

        A_						:  List[str]          =				self.get_dummy_components()

        A_						:  Tuple          =				self.pipeline_class(**_a )
        A_						:  Dict          =				pipe.to(_a )

        pipe.set_progress_bar_config(disable=_a )

        A_						:  Tuple          =				pipe(**self.get_dummy_inputs(_a ) )
        A_						:  Tuple          =				output.images

        A_						:  Optional[Any]          =				pipe(
            **self.get_dummy_inputs(_a )	,return_dict=_a	,)[0]

        A_						:  Tuple          =				image[0, -3:, -3:, -1]
        A_						:  Any          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A_						:  List[Any]          =				np.array(
            [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )

        assert (
            np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Tuple          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )

        A_						:  Optional[int]          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png""" )
        A_						:  Optional[int]          =				torch.from_numpy(np.array(_a ) ).float() / 255.0
        A_						:  List[Any]          =				hint.permute(2	,0	,1 ).unsqueeze(0 )

        A_						:  List[Any]          =				KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""	,torch_dtype=torch.floataa )
        pipe_prior.to(_a )

        A_						:  Union[str, Any]          =				KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,torch_dtype=torch.floataa )
        A_						:  Union[str, Any]          =				pipeline.to(_a )
        pipeline.set_progress_bar_config(disable=_a )

        A_						:  Optional[Any]          =				"""A robot, 4k photo"""

        A_						:  Any          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_   ,					A_						:  List[str]          =				pipe_prior(
            _a	,generator=_a	,num_inference_steps=5	,negative_prompt=""""""	,).to_tuple()

        A_						:  int          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_						:  List[Any]          =				pipeline(
            image_embeds=_a	,negative_image_embeds=_a	,hint=_a	,generator=_a	,num_inference_steps=100	,output_type="""np"""	,)

        A_						:  Dict          =				output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(_a	,_a )
