

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_	:			Union[str, Any]  	=      logging.get_logger(__name__)

set_seed(770)


A_	:			Tuple  	=      {
    '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_	:			Tuple  	=      {
    '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_	:			Any  	=      os.path.dirname(os.path.abspath(__file__))
A_	:			int  	=      os.path.join(os.path.expanduser('~'), '.cache')
A_	:			int  	=      os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			Optional[int]=False )					->				List[str]:
	A__							:   Dict					       =		model_type
	if use_small:
		key += "_small"
	return os.path.join(lowerCamelCase__   ,				REMOTE_MODEL_PATHS[key]["""file_name"""] )

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			str )					->				Union[str, Any]:
	os.makedirs(lowerCamelCase__   ,				exist_ok=lowerCamelCase__ )
	hf_hub_download(repo_id=lowerCamelCase__   ,				filename=lowerCamelCase__   ,				local_dir=lowerCamelCase__ )

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

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

	A__							:   Optional[int]					       =		ConfigClass(**checkpoint["""model_args"""] )
	A__							:   Union[str, Any]					       =		ModelClass(config=lowerCamelCase__ )
	A__							:   int					       =		GenerationConfigClass()

	A__							:   Dict					       =		model_generation_config
	A__							:   List[str]					       =		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__							:   Optional[int]					       =		k[len(lowerCamelCase__ ) :]
			for old_layer_name in new_layer_name_dict:
				A__							:   int					       =		new_k.replace(lowerCamelCase__   ,				new_layer_name_dict[old_layer_name] )

			A__							:   Any					       =		state_dict.pop(lowerCamelCase__ )

	A__							:   Any					       =		set(state_dict.keys() ) - set(model.state_dict().keys() )
	A__							:   List[Any]					       =		{k for k in extra_keys if not k.endswith(""".attn.bias""" )}
	A__							:   Tuple					       =		set(model.state_dict().keys() ) - set(state_dict.keys() )
	A__							:   Tuple					       =		{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__							:   Optional[int]					       =		model.num_parameters(exclude_embeddings=lowerCamelCase__ )
	A__							:   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 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			Union[str, Any]=False   ,				lowercase_:			List[str]="text" )					->				Optional[Any]:
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	A__							:   Any					       =		'''cpu'''  # do conversion on cpu

	A__							:   Dict					       =		_get_ckpt_path(lowerCamelCase__   ,				use_small=lowerCamelCase__ )
	A__							:   Dict					       =		_load_model(lowerCamelCase__   ,				lowerCamelCase__   ,				model_type=lowerCamelCase__   ,				use_small=lowerCamelCase__ )

	# load bark initial model
	A__							:   Tuple					       =		_bark_load_model(lowerCamelCase__   ,				"""cpu"""   ,				model_type=lowerCamelCase__   ,				use_small=lowerCamelCase__ )

	if model_type == "text":
		A__							:   Any					       =		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__							:   List[str]					       =		10

	if model_type in ["text", "coarse"]:
		A__							:   Union[str, Any]					       =		torch.randint(256   ,				(batch_size, sequence_length)   ,				dtype=torch.int )
		A__							:   List[Any]					       =		bark_model(lowerCamelCase__ )[0]

		A__							:   Union[str, Any]					       =		model(lowerCamelCase__ )

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

	else:
		A__							:   Union[str, Any]					       =		3
		A__							:   Tuple					       =		8
		A__							:   Union[str, Any]					       =		torch.randint(256   ,				(batch_size, sequence_length, n_codes_total)   ,				dtype=torch.int )

		A__							:   Any					       =		model(lowerCamelCase__   ,				lowerCamelCase__ )
		A__							:   Dict					       =		bark_model(lowerCamelCase__   ,				lowerCamelCase__ )

		A__							:   int					       =		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 						UpperCamelCase (lowercase_:			List[Any]   ,				lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			Optional[int]   ,				lowercase_:			List[Any]   ,				lowercase_:			List[Any]   ,				)					->				Optional[Any]:
	A__							:   Dict					       =		os.path.join(lowerCamelCase__   ,				lowerCamelCase__ )

	A__							:   Optional[int]					       =		BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase__   ,				"""config.json""" ) )
	A__							:   Any					       =		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__							:   Optional[Any]					       =		BarkSemanticModel.from_pretrained(lowerCamelCase__ )
	A__							:   int					       =		BarkCoarseModel.from_pretrained(lowerCamelCase__ )
	A__							:   Union[str, Any]					       =		BarkFineModel.from_pretrained(lowerCamelCase__ )
	A__							:   str					       =		EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

	A__							:   Union[str, 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__							:   int					       =		BarkModel(lowerCamelCase__ )

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

	A__							:   Union[str, 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_	:			Optional[int]  	=      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_	:			Any  	=      parser.parse_args()

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









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	KandinskyVaaImgaImgPipeline
					UpperCAmelCase__:       Optional[Any]  							=	['''image_embeds''', '''negative_image_embeds''', '''image''']
					UpperCAmelCase__:       str  							=	[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					UpperCAmelCase__:       int  							=	[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__:       Union[str, Any]  							=	False






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return self.time_input_dim






					@property
					def   __A      (			self      ):
						return self.time_input_dim * 4






					@property
					def   __A      (			self      ):
						return 100






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )

						A__							:   Dict					       =		{
						    """in_channels""": 4,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """image""",
						    """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__							:   List[str]					       =		UNetaDConditionModel(**A__      )
						return model






					@property
					def   __A      (			self      ):
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["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",
						    ],
						    "vq_embed_dim": 4,
						}






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   Tuple					       =		VQModel(**self.dummy_movq_kwargs      )
						return model






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.dummy_unet
						A__							:   Dict					       =		self.dummy_movq

						A__							:   List[Any]					       =		{
						    """num_train_timesteps""": 1000,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.0_0_0_8_5,
						    """beta_end""": 0.0_1_2,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						A__							:   List[str]					       =		DDIMScheduler(**A__      )

						A__							:   List[str]					       =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components






					def   __A      (			self   ,	A__   ,	A__=0      ):
						A__							:   List[str]					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Any					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(seed + 1      )      ).to(
						    A__      )
						# create init_image
						A__							:   Dict					       =		floats_tensor((1, 3, 64, 64)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Optional[int]					       =		image.cpu().permute(0   ,	2   ,	3   ,	1      )[0]
						A__							:   Dict					       =		Image.fromarray(np.uinta(A__      )      ).convert("""RGB"""      ).resize((256, 256)      )

						if str(A__      ).startswith("""mps"""      ):
							A__							:   Any					       =		torch.manual_seed(A__      )
						else:
							A__							:   List[Any]					       =		torch.Generator(device=A__      ).manual_seed(A__      )
						A__							:   Optional[int]					       =		{
						    """image""": init_image,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """num_inference_steps""": 10,
						    """guidance_scale""": 7.0,
						    """strength""": 0.2,
						    """output_type""": """np""",
						}
						return inputs






					def   __A      (			self      ):
						A__							:   str					       =		"""cpu"""

						A__							:   Any					       =		self.get_dummy_components()

						A__							:   Union[str, Any]					       =		self.pipeline_class(**A__      )
						A__							:   List[str]					       =		pipe.to(A__      )

						pipe.set_progress_bar_config(disable=A__      )

						A__							:   Dict					       =		pipe(**self.get_dummy_inputs(A__      )      )
						A__							:   Any					       =		output.images

						A__							:   List[str]					       =		pipe(
						    **self.get_dummy_inputs(A__      )   ,	return_dict=A__   ,	)[0]

						A__							:   Optional[int]					       =		image[0, -3:, -3:, -1]
						A__							:   str					       =		image_from_tuple[0, -3:, -3:, -1]

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

						A__							:   str					       =		np.array(
						    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]      )
						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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

						A__							:   List[Any]					       =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
						A__							:   str					       =		"""A red cartoon frog, 4k"""

						A__							:   int					       =		KandinskyVaaPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""   ,	torch_dtype=torch.floataa      )
						pipe_prior.to(A__      )

						A__							:   List[Any]					       =		KandinskyVaaImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-decoder"""   ,	torch_dtype=torch.floataa      )
						A__							:   List[str]					       =		pipeline.to(A__      )

						pipeline.set_progress_bar_config(disable=A__      )

						A__							:   Tuple					       =		torch.Generator(device="""cpu"""      ).manual_seed(0      )
						A__       ,       A__							:   Optional[Any]					       =		pipe_prior(
						    A__   ,	generator=A__   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

						A__							:   str					       =		pipeline(
						    image=A__   ,	image_embeds=A__   ,	negative_image_embeds=A__   ,	generator=A__   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	strength=0.2   ,	output_type="""np"""   ,	)

						A__							:   Optional[int]					       =		output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(A__   ,	A__      )



