



'''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()
a							   =				logging.get_logger(__name__)

set_seed(770)


a							   =				{
    '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',
}


a							   =				{
    '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',
    },
}

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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=False					)				->					int:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=model_type
			if use_small:
						key += "_small"
			return os.path.join(__lowerCAmelCase				,							REMOTE_MODEL_PATHS[key]['file_name']					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					List[Any]:





			"""simple docstring"""







			os.makedirs(__lowerCAmelCase				,							exist_ok=__lowerCAmelCase					)
			hf_hub_download(repo_id=__lowerCAmelCase				,							filename=__lowerCAmelCase				,							local_dir=__lowerCAmelCase					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase="text"					)				->					Optional[Any]:





			"""simple docstring"""







			if model_type == "text":
						snake_case:    List[str]      							=BarkSemanticModel
						snake_case:    Dict      							=BarkSemanticConfig
						snake_case:    str      							=BarkSemanticGenerationConfig
			elif model_type == "coarse":
						snake_case:    List[str]      							=BarkCoarseModel
						snake_case:    int      							=BarkCoarseConfig
						snake_case:    int      							=BarkCoarseGenerationConfig
			elif model_type == "fine":
						snake_case:    List[Any]      							=BarkFineModel
						snake_case:    Dict      							=BarkFineConfig
						snake_case:    int      							=BarkFineGenerationConfig
			else:
						raise NotImplementedError()
			snake_case:    List[str]      							=f'''{model_type}_small''' if use_small else model_type
			snake_case:    Dict      							=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']					)
			snake_case:    Union[str, Any]      							=torch.load(__lowerCAmelCase				,							map_location=__lowerCAmelCase					)
			# this is a hack
			snake_case:    Any      							=checkpoint["""model_args"""]
			if "input_vocab_size" not in model_args:
						snake_case:    Tuple      							=model_args["""vocab_size"""]
						snake_case:    List[str]      							=model_args["""vocab_size"""]
						del model_args["vocab_size"]

			# convert Bark model arguments to HF Bark model arguments
			snake_case:    Tuple      							=model_args.pop('n_head'					)
			snake_case:    Tuple      							=model_args.pop('n_embd'					)
			snake_case:    Union[str, Any]      							=model_args.pop('n_layer'					)

			snake_case:    List[Any]      							=ConfigClass(**checkpoint['model_args']					)
			snake_case:    Optional[int]      							=ModelClass(config=__lowerCAmelCase					)
			snake_case:    Dict      							=GenerationConfigClass()

			snake_case:    Any      							=model_generation_config
			snake_case:    str      							=checkpoint["""model"""]
			# fixup checkpoint
			snake_case:    Dict      							="""_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
									snake_case:    List[str]      							=k[len(__lowerCAmelCase					) :]
									for old_layer_name in new_layer_name_dict:
												snake_case:    List[Any]      							=new_k.replace(__lowerCAmelCase				,							new_layer_name_dict[old_layer_name]					)

									snake_case:    Optional[Any]      							=state_dict.pop(__lowerCAmelCase					)

			snake_case:    int      							=set(state_dict.keys()					) - set(model.state_dict().keys()					)
			snake_case:    List[Any]      							={k for k in extra_keys if not k.endswith('.attn.bias'					)}
			snake_case:    Dict      							=set(model.state_dict().keys()					) - set(state_dict.keys()					)
			snake_case:    Optional[int]      							={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					)
			snake_case:    Tuple      							=model.num_parameters(exclude_embeddings=__lowerCAmelCase					)
			snake_case:    Optional[int]      							=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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase="text"					)				->					Tuple:





			"""simple docstring"""







			if model_type not in ("text", "coarse", "fine"):
						raise NotImplementedError()

			snake_case:    str      							="""cpu"""  # do conversion on cpu

			snake_case:    str      							=_get_ckpt_path(__lowerCAmelCase				,							use_small=__lowerCAmelCase					)
			snake_case:    Optional[int]      							=_load_model(__lowerCAmelCase				,							__lowerCAmelCase				,							model_type=__lowerCAmelCase				,							use_small=__lowerCAmelCase					)

			# load bark initial model
			snake_case:    Dict      							=_bark_load_model(__lowerCAmelCase				,							'cpu'				,							model_type=__lowerCAmelCase				,							use_small=__lowerCAmelCase					)

			if model_type == "text":
						snake_case:    Dict      							=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
			snake_case:    List[str]      							=5
			snake_case:    List[Any]      							=10

			if model_type in ["text", "coarse"]:
						snake_case:    List[Any]      							=torch.randint(2_56				,							(batch_size, sequence_length)				,							dtype=torch.int					)
						snake_case:    List[str]      							=bark_model(__lowerCAmelCase					)[0]

						snake_case:    Dict      							=model(__lowerCAmelCase					)

						# take last logits
						snake_case:    Union[str, Any]      							=output_new_model_total.logits[:, [-1], :]

			else:
						snake_case:    Optional[int]      							=3
						snake_case:    Tuple      							=8
						snake_case:    int      							=torch.randint(2_56				,							(batch_size, sequence_length, n_codes_total)				,							dtype=torch.int					)

						snake_case:    Union[str, Any]      							=model(__lowerCAmelCase				,							__lowerCAmelCase					)
						snake_case:    Tuple      							=bark_model(__lowerCAmelCase				,							__lowerCAmelCase					)

						snake_case:    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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							)				->					int:





			"""simple docstring"""







			snake_case:    Optional[int]      							=os.path.join(__lowerCAmelCase				,							__lowerCAmelCase					)

			snake_case:    str      							=BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase				,							'config.json'					)					)
			snake_case:    List[str]      							=BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase				,							'config.json'					)					)
			snake_case:    List[Any]      							=BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase				,							'config.json'					)					)
			snake_case:    List[str]      							=EncodecConfig.from_pretrained('facebook/encodec_24khz'					)

			snake_case:    Optional[int]      							=BarkSemanticModel.from_pretrained(__lowerCAmelCase					)
			snake_case:    List[Any]      							=BarkCoarseModel.from_pretrained(__lowerCAmelCase					)
			snake_case:    Union[str, Any]      							=BarkFineModel.from_pretrained(__lowerCAmelCase					)
			snake_case:    Union[str, Any]      							=EncodecModel.from_pretrained('facebook/encodec_24khz'					)

			snake_case:    Dict      							=BarkConfig.from_sub_model_configs(
			    __lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase					)

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

			snake_case:    Optional[int]      							=BarkModel(__lowerCAmelCase					)

			snake_case:    List[Any]      							=semantic
			snake_case:    Any      							=coarseAcoustic
			snake_case:    Dict      							=fineAcoustic
			snake_case:    List[str]      							=codec

			snake_case:    Optional[Any]      							=bark_generation_config

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


if __name__ == "__main__":
	a							   =				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.')

	a							   =				parser.parse_args()

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












import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		_A						( UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			Union[str, Any]						=					LDMTextToImagePipeline
  lowercase_						:			Tuple						=					TEXT_TO_IMAGE_PARAMS - {
      '''negative_prompt''',
      '''negative_prompt_embeds''',
      '''cross_attention_kwargs''',
      '''prompt_embeds''',
  }
  lowercase_						:			Optional[Any]						=					PipelineTesterMixin.required_optional_params - {
      '''num_images_per_prompt''',
      '''callback''',
      '''callback_steps''',
  }
  lowercase_						:			Dict						=					TEXT_TO_IMAGE_BATCH_PARAMS
  lowercase_						:			Tuple						=					False

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			Any					=					UNetaDConditionModel(
         block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,				cross_attention_dim=32   ,				)
     __UpperCamelCase	:			Union[str, Any]					=					DDIMScheduler(
         beta_start=0.0_0085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_one=lowerCamelCase__   ,				)
     torch.manual_seed(0			)
     __UpperCamelCase	:			Any					=					AutoencoderKL(
         block_out_channels=(32, 64)   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")   ,				up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")   ,				latent_channels=4   ,				)
     torch.manual_seed(0			)
     __UpperCamelCase	:			Union[str, Any]					=					CLIPTextConfig(
         bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=10_00   ,				)
     __UpperCamelCase	:			List[str]					=					CLIPTextModel(lowerCamelCase__			)
     __UpperCamelCase	:			List[str]					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

     __UpperCamelCase	:			Any					=					{
         """unet""": unet,
         """scheduler""": scheduler,
         """vqvae""": vae,
         """bert""": text_encoder,
         """tokenizer""": tokenizer,
     }
     return components

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	List[str]=0			):
     """simple docstring"""

     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			str					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			Optional[Any]					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					{
         """prompt""": """A painting of a squirrel eating a burger""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

     __UpperCamelCase	:			List[str]					=					self.get_dummy_components()
     __UpperCamelCase	:			Optional[int]					=					LDMTextToImagePipeline(**lowerCamelCase__			)
     pipe.to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			List[Any]					=					self.get_dummy_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					pipe(**lowerCamelCase__			).images
     __UpperCamelCase	:			Tuple					=					image[0, -3:, -3:, -1]

     assert image.shape == (1, 16, 16, 3)
     __UpperCamelCase	:			int					=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]			)

     assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







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

  def    a					(    self						:	Any			):
     """simple docstring"""

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

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Tuple=torch.floataa   ,				lowerCamelCase__						:	List[Any]=0			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					torch.manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					np.random.RandomState(lowerCamelCase__			).standard_normal((1, 4, 32, 32)			)
     __UpperCamelCase	:			Any					=					torch.from_numpy(lowerCamelCase__			).to(device=lowerCamelCase__   ,				dtype=lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					{
         """prompt""": """A painting of a squirrel eating a burger""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""			).to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Any					=					self.get_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					pipe(**lowerCamelCase__			).images
     __UpperCamelCase	:			Optional[int]					=					image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 2_56, 2_56, 3)
     __UpperCamelCase	:			Optional[Any]					=					np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]			)
     __UpperCamelCase	:			Union[str, Any]					=					np.abs(expected_slice - image_slice			).max()
     assert max_diff < 1e-3







@nightly
@require_torch_gpu
class 		_A						( unittest.TestCase ):

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

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

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]=torch.floataa   ,				lowerCamelCase__						:	Tuple=0			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					torch.manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[Any]					=					np.random.RandomState(lowerCamelCase__			).standard_normal((1, 4, 32, 32)			)
     __UpperCamelCase	:			List[Any]					=					torch.from_numpy(lowerCamelCase__			).to(device=lowerCamelCase__   ,				dtype=lowerCamelCase__			)
     __UpperCamelCase	:			Union[str, Any]					=					{
         """prompt""": """A painting of a squirrel eating a burger""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 50,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""			).to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Dict					=					self.get_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					pipe(**lowerCamelCase__			).images[0]

     __UpperCamelCase	:			Optional[Any]					=					load_numpy(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""			)
     __UpperCamelCase	:			int					=					np.abs(expected_image - image			).max()
     assert max_diff < 1e-3



