






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

set_seed(770)


__lowerCAmelCase       		=      {
    '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',
}


__lowerCAmelCase       		=      {
    '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',
    },
}

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





def 		_UpperCAmelCase   (							__A							:   Dict						,		__A							:   List[Any]=False      ):
				a_		:  List[Any]											=      model_type
				if use_small:
								key += "_small"
				return os.path.join(__A						,		REMOTE_MODEL_PATHS[key]['''file_name''']      )





def 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   str      ):
				os.makedirs(__A						,		exist_ok=__A      )
				hf_hub_download(repo_id=__A						,		filename=__A						,		local_dir=__A      )





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

				a_		:  Optional[int]											=      ConfigClass(**checkpoint['''model_args''']      )
				a_		:  List[Any]											=      ModelClass(config=__A      )
				a_		:  int											=      GenerationConfigClass()

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

												a_		:  Union[str, Any]											=      state_dict.pop(__A      )

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

				return model





def 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   Tuple=False						,		__A							:   Optional[Any]="text"      ):
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				a_		:  Optional[int]											=      '''cpu'''  # do conversion on cpu

				a_		:  Tuple											=      _get_ckpt_path(__A						,		use_small=__A      )
				a_		:  Any											=      _load_model(__A						,		__A						,		model_type=__A						,		use_small=__A      )

				# load bark initial model
				a_		:  int											=      _bark_load_model(__A						,		'''cpu'''						,		model_type=__A						,		use_small=__A      )

				if model_type == "text":
								a_		:  Dict											=      bark_model['''model''']

				if model.num_parameters(exclude_embeddings=__A      ) != 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[Any]											=      10

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

								a_		:  List[str]											=      model(__A      )

								# take last logits
								a_		:  Optional[Any]											=      output_new_model_total.logits[:, [-1], :]

				else:
								a_		:  Tuple											=      3
								a_		:  Tuple											=      8
								a_		:  Any											=      torch.randint(2_56						,		(batch_size, sequence_length, n_codes_total)						,		dtype=torch.int      )

								a_		:  int											=      model(__A						,		__A      )
								a_		:  List[Any]											=      bark_model(__A						,		__A      )

								a_		:  Optional[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(__A      ).mkdir(exist_ok=__A      )
				model.save_pretrained(__A      )





def 		_UpperCAmelCase   (							__A							:   Dict						,		__A							:   Optional[Any]						,		__A							:   Tuple						,		__A							:   Dict						,		__A							:   Tuple						,		__A							:   int						,		):
				a_		:  Union[str, Any]											=      os.path.join(__A						,		__A      )

				a_		:  Tuple											=      BarkSemanticConfig.from_pretrained(os.path.join(__A						,		'''config.json'''      )      )
				a_		:  str											=      BarkCoarseConfig.from_pretrained(os.path.join(__A						,		'''config.json'''      )      )
				a_		:  Dict											=      BarkFineConfig.from_pretrained(os.path.join(__A						,		'''config.json'''      )      )
				a_		:  Union[str, Any]											=      EncodecConfig.from_pretrained('''facebook/encodec_24khz'''      )

				a_		:  List[str]											=      BarkSemanticModel.from_pretrained(__A      )
				a_		:  List[str]											=      BarkCoarseModel.from_pretrained(__A      )
				a_		:  str											=      BarkFineModel.from_pretrained(__A      )
				a_		:  Optional[int]											=      EncodecModel.from_pretrained('''facebook/encodec_24khz'''      )

				a_		:  Tuple											=      BarkConfig.from_sub_model_configs(
				    __A						,		__A						,		__A						,		__A      )

				a_		:  Dict											=      BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config						,		coarseAcoustic.generation_config						,		fineAcoustic.generation_config      )

				a_		:  List[Any]											=      BarkModel(__A      )

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

				a_		:  Tuple											=      bark_generation_config

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


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

		__lowerCAmelCase       		=      parser.parse_args()

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












'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				IFInpaintingSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=0					)			->			List[Any]:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Optional[int]											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''original_image''': original_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}

							return inputs






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()  ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''  ,						)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda'''  ,						reason='''float16 requires CUDA'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[int]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Optional[Any]:
							self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[Any]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2  ,						)




