






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       Optional[int]     =   KandinskyInpaintPipeline
		__snake_case      :       Dict     =   ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
		__snake_case      :       int     =   [
		    "prompt",
		    "negative_prompt",
		    "image_embeds",
		    "negative_image_embeds",
		    "image",
		    "mask_image",
		]
		__snake_case      :       List[str]     =   [
		    "generator",
		    "height",
		    "width",
		    "latents",
		    "guidance_scale",
		    "negative_prompt",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__snake_case      :       str     =   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		):
						_A	:				Dict   =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
						return tokenizer







		@property
		def  A__      (					self		):
						torch.manual_seed(0		)
						_A	:				Tuple   =      MCLIPConfig(
						    numDims=self.cross_attention_dim       ,transformerDimensions=self.text_embedder_hidden_size       ,hidden_size=self.text_embedder_hidden_size       ,intermediate_size=37       ,num_attention_heads=4       ,num_hidden_layers=5       ,vocab_size=1005       ,)

						_A	:				Dict   =      MultilingualCLIP(A__		)
						_A	:				Optional[int]   =      text_encoder.eval()

						return text_encoder







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

						_A	:				Tuple   =      {
						    '''in_channels''': 9,
						    # Out channels is double in channels because predicts mean and variance
						    '''out_channels''': 8,
						    '''addition_embed_type''': '''text_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''': '''text_image_proj''',
						    '''cross_attention_dim''': self.cross_attention_dim,
						    '''attention_head_dim''': 4,
						    '''resnet_time_scale_shift''': '''scale_shift''',
						    '''class_embed_type''': None,
						}

						_A	:				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	:				Dict   =      VQModel(**self.dummy_movq_kwargs		)
						return model







		def  A__      (					self		):
						_A	:				List[str]   =      self.dummy_text_encoder
						_A	:				Tuple   =      self.dummy_tokenizer
						_A	:				Any   =      self.dummy_unet
						_A	:				List[str]   =      self.dummy_movq

						_A	:				Any   =      DDIMScheduler(
						    num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_00_85       ,beta_end=0.0_12       ,clip_sample=A__       ,set_alpha_to_one=A__       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=A__       ,)

						_A	:				Union[str, Any]   =      {
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''movq''': movq,
						}

						return components







		def  A__      (					self       ,A__       ,A__=0		):
						_A	:				Union[str, Any]   =      floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(A__		)		).to(A__		)
						_A	:				Optional[int]   =      floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(seed + 1		)		).to(A__		)
						# create init_image
						_A	:				List[str]   =      floats_tensor((1, 3, 64, 64)       ,rng=random.Random(A__		)		).to(A__		)
						_A	:				Optional[int]   =      image.cpu().permute(0       ,2       ,3       ,1		)[0]
						_A	:				int   =      Image.fromarray(np.uinta(A__		)		).convert('''RGB'''		).resize((256, 256)		)
						# create mask
						_A	:				Optional[int]   =      np.ones((64, 64)       ,dtype=np.floataa		)
						_A	:				Optional[int]   =      0

						if str(A__		).startswith('''mps'''		):
										_A	:				List[Any]   =      torch.manual_seed(A__		)
						else:
										_A	:				Tuple   =      torch.Generator(device=A__		).manual_seed(A__		)
						_A	:				Dict   =      {
						    '''prompt''': '''horse''',
						    '''image''': init_image,
						    '''mask_image''': mask,
						    '''image_embeds''': image_embeds,
						    '''negative_image_embeds''': negative_image_embeds,
						    '''generator''': generator,
						    '''height''': 64,
						    '''width''': 64,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 4.0,
						    '''output_type''': '''np''',
						}
						return inputs







		def  A__      (					self		):
						_A	:				List[str]   =      '''cpu'''

						_A	:				Optional[int]   =      self.get_dummy_components()

						_A	:				List[Any]   =      self.pipeline_class(**A__		)
						_A	:				List[Any]   =      pipe.to(A__		)

						pipe.set_progress_bar_config(disable=A__		)

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

						_A	:				int   =      pipe(
						    **self.get_dummy_inputs(A__		)       ,return_dict=A__       ,)[0]

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

						print(f"""image.shape {image.shape}"""		)

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

						_A	:				Dict   =      np.array(
						    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]		)

						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()}"""







		def  A__      (					self		):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






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







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







		def  A__      (					self		):
						_A	:				List[str]   =      load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''		)

						_A	:				Optional[Any]   =      load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
						_A	:				str   =      np.ones((768, 768)       ,dtype=np.floataa		)
						_A	:				Optional[Any]   =      0

						_A	:				List[str]   =      '''a hat'''

						_A	:				List[Any]   =      KandinskyPriorPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-1-prior'''       ,torch_dtype=torch.floataa		)
						pipe_prior.to(A__		)

						_A	:				Optional[Any]   =      KandinskyInpaintPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-1-inpaint'''       ,torch_dtype=torch.floataa		)
						_A	:				Any   =      pipeline.to(A__		)
						pipeline.set_progress_bar_config(disable=A__		)

						_A	:				List[str]   =      torch.Generator(device='''cpu'''		).manual_seed(0		)
						_A  ,				_A	:				Any   =      pipe_prior(
						    A__       ,generator=A__       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

						_A	:				Tuple   =      pipeline(
						    A__       ,image=A__       ,mask_image=A__       ,image_embeds=A__       ,negative_image_embeds=A__       ,generator=A__       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

						_A	:				Tuple   =      output.images[0]

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

						assert_mean_pixel_difference(A__       ,A__		)











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()
_UpperCamelCase			:	Any								=logging.get_logger(__name__)

set_seed(770)


_UpperCamelCase			:	List[Any]								={
    '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',
}


_UpperCamelCase			:	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',
    },
}

_UpperCamelCase			:	Any								=os.path.dirname(os.path.abspath(__file__))
_UpperCamelCase			:	Tuple								=os.path.join(os.path.expanduser('~'), '.cache')
_UpperCamelCase			:	Dict								=os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')



def  a__ (__lowercase							:int					,  __lowercase							:Any=False   )		-> Union[str, Any]:
				_A	:				str   =      model_type
				if use_small:
								key += "_small"
				return os.path.join(__lowercase					,  REMOTE_MODEL_PATHS[key]['''file_name''']   )



def  a__ (__lowercase							:str					,  __lowercase							:Optional[Any]   )		-> Dict:
				os.makedirs(__lowercase					,  exist_ok=__lowercase   )
				hf_hub_download(repo_id=__lowercase					,  filename=__lowercase					,  local_dir=__lowercase   )



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

				# convert Bark model arguments to HF Bark model arguments
				_A	:				List[str]   =      model_args.pop('''n_head'''   )
				_A	:				int   =      model_args.pop('''n_embd'''   )
				_A	:				Tuple   =      model_args.pop('''n_layer'''   )

				_A	:				Dict   =      ConfigClass(**checkpoint['''model_args''']   )
				_A	:				Optional[Any]   =      ModelClass(config=__lowercase   )
				_A	:				Optional[Any]   =      GenerationConfigClass()

				_A	:				Dict   =      model_generation_config
				_A	:				int   =      checkpoint['''model''']
				# fixup checkpoint
				_A	:				str   =      '''_orig_mod.'''
				for k, v in list(state_dict.items()   ):
								if k.startswith(__lowercase   ):
												# replace part of the key with corresponding layer name in HF implementation
												_A	:				Optional[Any]   =      k[len(__lowercase   ) :]
												for old_layer_name in new_layer_name_dict:
																_A	:				Any   =      new_k.replace(__lowercase					,  new_layer_name_dict[old_layer_name]   )

												_A	:				str   =      state_dict.pop(__lowercase   )

				_A	:				List[str]   =      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	:				Union[str, Any]   =      set(model.state_dict().keys()   ) - set(state_dict.keys()   )
				_A	:				Any   =      {k for k in missing_keys if not k.endswith('''.attn.bias'''   )}
				if len(__lowercase   ) != 0:
								raise ValueError(f"""extra keys found: {extra_keys}"""   )
				if len(__lowercase   ) != 0:
								raise ValueError(f"""missing keys: {missing_keys}"""   )
				model.load_state_dict(__lowercase					,  strict=__lowercase   )
				_A	:				int   =      model.num_parameters(exclude_embeddings=__lowercase   )
				_A	:				Tuple   =      checkpoint['''best_val_loss'''].item()
				logger.info(f"""model loaded: {round(n_params/1e6					,  1   )}M params, {round(__lowercase					,  3   )} loss"""   )
				model.eval()
				model.to(__lowercase   )
				del checkpoint, state_dict

				return model



def  a__ (__lowercase							:Union[str, Any]					,  __lowercase							:Any=False					,  __lowercase							:Optional[int]="text"   )		-> Dict:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				_A	:				List[str]   =      '''cpu'''  # do conversion on cpu

				_A	:				Tuple   =      _get_ckpt_path(__lowercase					,  use_small=__lowercase   )
				_A	:				Any   =      _load_model(__lowercase					,  __lowercase					,  model_type=__lowercase					,  use_small=__lowercase   )

				# load bark initial model
				_A	:				Union[str, Any]   =      _bark_load_model(__lowercase					,  '''cpu'''					,  model_type=__lowercase					,  use_small=__lowercase   )

				if model_type == "text":
								_A	:				Any   =      bark_model['''model''']

				if model.num_parameters(exclude_embeddings=__lowercase   ) != 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	:				List[str]   =      5
				_A	:				List[str]   =      10

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

								_A	:				Optional[Any]   =      model(__lowercase   )

								# take last logits
								_A	:				Dict   =      output_new_model_total.logits[:, [-1], :]

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

								_A	:				List[str]   =      model(__lowercase					,  __lowercase   )
								_A	:				Dict   =      bark_model(__lowercase					,  __lowercase   )

								_A	:				Optional[Any]   =      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(__lowercase   ).mkdir(exist_ok=__lowercase   )
				model.save_pretrained(__lowercase   )



def  a__ (__lowercase							:Dict					,  __lowercase							:List[str]					,  __lowercase							:Union[str, Any]					,  __lowercase							:Dict					,  __lowercase							:List[Any]					,  __lowercase							:Optional[int]					,  )		-> Union[str, Any]:
				_A	:				List[Any]   =      os.path.join(__lowercase					,  __lowercase   )

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

				_A	:				str   =      BarkSemanticModel.from_pretrained(__lowercase   )
				_A	:				Tuple   =      BarkCoarseModel.from_pretrained(__lowercase   )
				_A	:				List[str]   =      BarkFineModel.from_pretrained(__lowercase   )
				_A	:				Union[str, Any]   =      EncodecModel.from_pretrained('''facebook/encodec_24khz'''   )

				_A	:				Tuple   =      BarkConfig.from_sub_model_configs(
				    __lowercase					,  __lowercase					,  __lowercase					,  __lowercase   )

				_A	:				Tuple   =      BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config					,  coarseAcoustic.generation_config					,  fineAcoustic.generation_config   )

				_A	:				Tuple   =      BarkModel(__lowercase   )

				_A	:				Optional[Any]   =      semantic
				_A	:				List[Any]   =      coarseAcoustic
				_A	:				int   =      fineAcoustic
				_A	:				List[Any]   =      codec

				_A	:				Optional[Any]   =      bark_generation_config

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


if __name__ == "__main__":
	_UpperCamelCase			:	str								=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.')

	_UpperCamelCase			:	Dict								=parser.parse_args()

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



