

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		:   Dict										=       logging.get_logger(__name__)

set_seed(770)


_UpperCAmelCase		:   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""",
}


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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=False   ,							UpperCamelCase__="text"      ):







							'''simple docstring'''






							if model_type == "text":
														snake_case_							=    BarkSemanticModel
														snake_case_							=    BarkSemanticConfig
														snake_case_							=    BarkSemanticGenerationConfig
							elif model_type == "coarse":
														snake_case_							=    BarkCoarseModel
														snake_case_							=    BarkCoarseConfig
														snake_case_							=    BarkCoarseGenerationConfig
							elif model_type == "fine":
														snake_case_							=    BarkFineModel
														snake_case_							=    BarkFineConfig
														snake_case_							=    BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							snake_case_							=    F'''{model_type}_small''' if use_small else model_type
							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']      )
							snake_case_							=    torch.load(UpperCamelCase__   ,							map_location=UpperCamelCase__      )
							# this is a hack
							snake_case_							=    checkpoint['model_args']
							if "input_vocab_size" not in model_args:
														snake_case_							=    model_args['vocab_size']
														snake_case_							=    model_args['vocab_size']
														del model_args["vocab_size"]

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

							snake_case_							=    ConfigClass(**checkpoint['model_args']      )
							snake_case_							=    ModelClass(config=UpperCamelCase__      )
							snake_case_							=    GenerationConfigClass()

							snake_case_							=    model_generation_config
							snake_case_							=    checkpoint['model']
							# fixup checkpoint
							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
																					snake_case_							=    k[len(UpperCamelCase__      ) :]
																					for old_layer_name in new_layer_name_dict:
																												snake_case_							=    new_k.replace(UpperCamelCase__   ,							new_layer_name_dict[old_layer_name]      )

																					snake_case_							=    state_dict.pop(UpperCamelCase__      )

							snake_case_							=    set(state_dict.keys()      ) - set(model.state_dict().keys()      )
							snake_case_							=    {k for k in extra_keys if not k.endswith('.attn.bias'      )}
							snake_case_							=    set(model.state_dict().keys()      ) - set(state_dict.keys()      )
							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__      )
							snake_case_							=    model.num_parameters(exclude_embeddings=UpperCamelCase__      )
							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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=False   ,							UpperCamelCase__="text"      ):







							'''simple docstring'''






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

							snake_case_							=    'cpu'  # do conversion on cpu

							snake_case_							=    _get_ckpt_path(UpperCamelCase__   ,							use_small=UpperCamelCase__      )
							snake_case_							=    _load_model(UpperCamelCase__   ,							UpperCamelCase__   ,							model_type=UpperCamelCase__   ,							use_small=UpperCamelCase__      )

							# load bark initial model
							snake_case_							=    _bark_load_model(UpperCamelCase__   ,							'cpu'   ,							model_type=UpperCamelCase__   ,							use_small=UpperCamelCase__      )

							if model_type == "text":
														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
							snake_case_							=    5
							snake_case_							=    10

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

														snake_case_							=    model(UpperCamelCase__      )

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

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

														snake_case_							=    model(UpperCamelCase__   ,							UpperCamelCase__      )
														snake_case_							=    bark_model(UpperCamelCase__   ,							UpperCamelCase__      )

														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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							):







							'''simple docstring'''






							snake_case_							=    os.path.join(UpperCamelCase__   ,							UpperCamelCase__      )

							snake_case_							=    BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase__   ,							'config.json'      )      )
							snake_case_							=    BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase__   ,							'config.json'      )      )
							snake_case_							=    BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase__   ,							'config.json'      )      )
							snake_case_							=    EncodecConfig.from_pretrained('facebook/encodec_24khz'      )

							snake_case_							=    BarkSemanticModel.from_pretrained(UpperCamelCase__      )
							snake_case_							=    BarkCoarseModel.from_pretrained(UpperCamelCase__      )
							snake_case_							=    BarkFineModel.from_pretrained(UpperCamelCase__      )
							snake_case_							=    EncodecModel.from_pretrained('facebook/encodec_24khz'      )

							snake_case_							=    BarkConfig.from_sub_model_configs(
							    UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

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

							snake_case_							=    BarkModel(UpperCamelCase__      )

							snake_case_							=    semantic
							snake_case_							=    coarseAcoustic
							snake_case_							=    fineAcoustic
							snake_case_							=    codec

							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__":
					_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		:   Optional[Any]										=       parser.parse_args()

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







import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

											snake_case_							=    images[0, 253:256, 253:256, -1]

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




