


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__       :							Dict        =  logging.get_logger(__name__)

set_seed(7_7_0)


a__       :							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",
}


a__       :							Dict        =  {
    "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__       :							Union[str, Any]        =  os.path.dirname(os.path.abspath(__file__))
a__       :							int        =  os.path.join(os.path.expanduser('''~'''), '''.cache''')
a__       :							List[str]        =  os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       model_type
					if use_small:
										key += "_small"
					return os.path.join(UpperCAmelCase__						,		REMOTE_MODEL_PATHS[key]["file_name"]     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=False						,		lowerCAmelCase_="text"     ):




					'''simple docstring'''


					if model_type == "text":
										__SCREAMING_SNAKE_CASE		     =       BarkSemanticModel
										__SCREAMING_SNAKE_CASE		     =       BarkSemanticConfig
										__SCREAMING_SNAKE_CASE		     =       BarkSemanticGenerationConfig
					elif model_type == "coarse":
										__SCREAMING_SNAKE_CASE		     =       BarkCoarseModel
										__SCREAMING_SNAKE_CASE		     =       BarkCoarseConfig
										__SCREAMING_SNAKE_CASE		     =       BarkCoarseGenerationConfig
					elif model_type == "fine":
										__SCREAMING_SNAKE_CASE		     =       BarkFineModel
										__SCREAMING_SNAKE_CASE		     =       BarkFineConfig
										__SCREAMING_SNAKE_CASE		     =       BarkFineGenerationConfig
					else:
										raise NotImplementedError()
					__SCREAMING_SNAKE_CASE		     =       f"""{model_type}_small""" if use_small else model_type
					__SCREAMING_SNAKE_CASE		     =       REMOTE_MODEL_PATHS[model_key]
					if not os.path.exists(UpperCAmelCase__     ):
										logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""     )
										_download(model_info["repo_id"]						,		model_info["file_name"]     )
					__SCREAMING_SNAKE_CASE		     =       torch.load(UpperCAmelCase__						,		map_location=UpperCAmelCase__     )
					# this is a hack
					__SCREAMING_SNAKE_CASE		     =       checkpoint["""model_args"""]
					if "input_vocab_size" not in model_args:
										__SCREAMING_SNAKE_CASE		     =       model_args["""vocab_size"""]
										__SCREAMING_SNAKE_CASE		     =       model_args["""vocab_size"""]
										del model_args["vocab_size"]

					# convert Bark model arguments to HF Bark model arguments
					__SCREAMING_SNAKE_CASE		     =       model_args.pop("n_head"     )
					__SCREAMING_SNAKE_CASE		     =       model_args.pop("n_embd"     )
					__SCREAMING_SNAKE_CASE		     =       model_args.pop("n_layer"     )

					__SCREAMING_SNAKE_CASE		     =       ConfigClass(**checkpoint["model_args"]     )
					__SCREAMING_SNAKE_CASE		     =       ModelClass(config=UpperCAmelCase__     )
					__SCREAMING_SNAKE_CASE		     =       GenerationConfigClass()

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

															__SCREAMING_SNAKE_CASE		     =       state_dict.pop(UpperCAmelCase__     )

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

					return model




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=False						,		lowerCAmelCase_="text"     ):




					'''simple docstring'''


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

					__SCREAMING_SNAKE_CASE		     =       """cpu"""  # do conversion on cpu

					__SCREAMING_SNAKE_CASE		     =       _get_ckpt_path(UpperCAmelCase__						,		use_small=UpperCAmelCase__     )
					__SCREAMING_SNAKE_CASE		     =       _load_model(UpperCAmelCase__						,		UpperCAmelCase__						,		model_type=UpperCAmelCase__						,		use_small=UpperCAmelCase__     )

					# load bark initial model
					__SCREAMING_SNAKE_CASE		     =       _bark_load_model(UpperCAmelCase__						,		"cpu"						,		model_type=UpperCAmelCase__						,		use_small=UpperCAmelCase__     )

					if model_type == "text":
										__SCREAMING_SNAKE_CASE		     =       bark_model["""model"""]

					if model.num_parameters(exclude_embeddings=UpperCAmelCase__     ) != 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
					__SCREAMING_SNAKE_CASE		     =       5
					__SCREAMING_SNAKE_CASE		     =       10

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

										__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__     )

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

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

										__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__						,		UpperCAmelCase__     )
										__SCREAMING_SNAKE_CASE		     =       bark_model(UpperCAmelCase__						,		UpperCAmelCase__     )

										__SCREAMING_SNAKE_CASE		     =       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(UpperCAmelCase__     ).mkdir(exist_ok=UpperCAmelCase__     )
					model.save_pretrained(UpperCAmelCase__     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       os.path.join(UpperCAmelCase__						,		UpperCAmelCase__     )

					__SCREAMING_SNAKE_CASE		     =       BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase__						,		"config.json"     )     )
					__SCREAMING_SNAKE_CASE		     =       BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase__						,		"config.json"     )     )
					__SCREAMING_SNAKE_CASE		     =       BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase__						,		"config.json"     )     )
					__SCREAMING_SNAKE_CASE		     =       EncodecConfig.from_pretrained("facebook/encodec_24khz"     )

					__SCREAMING_SNAKE_CASE		     =       BarkSemanticModel.from_pretrained(UpperCAmelCase__     )
					__SCREAMING_SNAKE_CASE		     =       BarkCoarseModel.from_pretrained(UpperCAmelCase__     )
					__SCREAMING_SNAKE_CASE		     =       BarkFineModel.from_pretrained(UpperCAmelCase__     )
					__SCREAMING_SNAKE_CASE		     =       EncodecModel.from_pretrained("facebook/encodec_24khz"     )

					__SCREAMING_SNAKE_CASE		     =       BarkConfig.from_sub_model_configs(
					    UpperCAmelCase__						,		UpperCAmelCase__						,		UpperCAmelCase__						,		UpperCAmelCase__     )

					__SCREAMING_SNAKE_CASE		     =       BarkGenerationConfig.from_sub_model_configs(
					    semantic.generation_config						,		coarseAcoustic.generation_config						,		fineAcoustic.generation_config     )

					__SCREAMING_SNAKE_CASE		     =       BarkModel(UpperCAmelCase__     )

					__SCREAMING_SNAKE_CASE		     =       semantic
					__SCREAMING_SNAKE_CASE		     =       coarseAcoustic
					__SCREAMING_SNAKE_CASE		     =       fineAcoustic
					__SCREAMING_SNAKE_CASE		     =       codec

					__SCREAMING_SNAKE_CASE		     =       bark_generation_config

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


if __name__ == "__main__":
		a__       :							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__       :							int        =  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 unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Union[str, Any]					      =    DiTPipeline
	snake_case__       :						Union[str, Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	snake_case__       :						Any					      =    PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	snake_case__       :						Optional[Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	snake_case__       :						Optional[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Dict:
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       TransformeraDModel(
						    sample_size=1_6    ,    num_layers=2    ,    patch_size=4    ,    attention_head_dim=8    ,    num_attention_heads=2    ,    in_channels=4    ,    out_channels=8    ,    attention_bias=UpperCAmelCase__    ,    activation_fn="gelu-approximate"    ,    num_embeds_ada_norm=1_0_0_0    ,    norm_type="ada_norm_zero"    ,    norm_elementwise_affine=UpperCAmelCase__    ,    )
						__SCREAMING_SNAKE_CASE		     =       AutoencoderKL()
						__SCREAMING_SNAKE_CASE		     =       DDIMScheduler()
						__SCREAMING_SNAKE_CASE		     =       {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
						return components




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       str=0     ) ->			Optional[int]:
						if str(UpperCAmelCase__     ).startswith("mps"     ):
											__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       torch.Generator(device=UpperCAmelCase__     ).manual_seed(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       {
						    "class_labels": [1],
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}
						return inputs




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       "cpu"

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_components()
						__SCREAMING_SNAKE_CASE		     =       self.pipeline_class(**UpperCAmelCase__     )
						pipe.to(UpperCAmelCase__     )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_inputs(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       pipe(**UpperCAmelCase__     ).images
						__SCREAMING_SNAKE_CASE		     =       image[0, -3:, -3:, -1]

						self.assertEqual(image.shape    ,    (1, 1_6, 1_6, 3)     )
						__SCREAMING_SNAKE_CASE		     =       np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]     )
						__SCREAMING_SNAKE_CASE		     =       np.abs(image_slice.flatten() - expected_slice     ).max()
						self.assertLessEqual(UpperCAmelCase__    ,    1E-3     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase__    ,    expected_max_diff=1E-3     )




	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()    ,    reason="XFormers attention is only available with CUDA and `xformers` installed"    ,    )
	def 						UpperCAmelCase_   (				self    :       int     ) ->			Dict:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )




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

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

						__SCREAMING_SNAKE_CASE		     =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"     )
						pipe.to("cuda"     )

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella", "white shark", "white wolf"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=4_0    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       load_numpy(
											    F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""     )
											assert np.abs((expected_image - image).max()     ) < 1E-2




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"     )
						__SCREAMING_SNAKE_CASE		     =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
						pipe.to("cuda"     )

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=2_5    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    F"""/dit/{word}_512.npy"""     )

											assert np.abs((expected_image - image).max()     ) < 1E-1
