'''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()
_lowercase		:				Dict	       =		logging.get_logger(__name__)

set_seed(770)


_lowercase		:				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",
}


_lowercase		:				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",
    },
}

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







def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Any=False				)    ->   int:
							lowercase_      :    Tuple       		= model_type
							if use_small:
														key += "_small"
							return os.path.join(UpperCAmelCase__   ,   REMOTE_MODEL_PATHS[key]["""file_name"""]				)







def 							lowerCamelCase			(   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   int:
							os.makedirs(UpperCAmelCase__   ,   exist_ok=UpperCAmelCase__				)
							hf_hub_download(repo_id=UpperCAmelCase__   ,   filename=UpperCAmelCase__   ,   local_dir=UpperCAmelCase__				)







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

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

							lowercase_      :    Union[str, Any]       		= ConfigClass(**checkpoint["""model_args"""]				)
							lowercase_      :    List[str]       		= ModelClass(config=UpperCAmelCase__				)
							lowercase_      :    int       		= GenerationConfigClass()

							lowercase_      :    List[Any]       		= model_generation_config
							lowercase_      :    str       		= checkpoint["""model"""]
							# fixup checkpoint
							lowercase_      :    Any       		= """_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
																					lowercase_      :    str       		= k[len(UpperCAmelCase__				) :]
																					for old_layer_name in new_layer_name_dict:
																												lowercase_      :    str       		= new_k.replace(UpperCAmelCase__   ,   new_layer_name_dict[old_layer_name]				)

																					lowercase_      :    Dict       		= state_dict.pop(UpperCAmelCase__				)

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

							lowercase_      :    str       		= """cpu"""  # do conversion on cpu

							lowercase_      :    List[str]       		= _get_ckpt_path(UpperCAmelCase__   ,   use_small=UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= _load_model(UpperCAmelCase__   ,   UpperCAmelCase__   ,   model_type=UpperCAmelCase__   ,   use_small=UpperCAmelCase__				)

							# load bark initial model
							lowercase_      :    Union[str, Any]       		= _bark_load_model(UpperCAmelCase__   ,   """cpu"""   ,   model_type=UpperCAmelCase__   ,   use_small=UpperCAmelCase__				)

							if model_type == "text":
														lowercase_      :    List[Any]       		= 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
							lowercase_      :    str       		= 5
							lowercase_      :    Any       		= 10

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

														lowercase_      :    List[Any]       		= model(UpperCAmelCase__				)

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

							else:
														lowercase_      :    Dict       		= 3
														lowercase_      :    Dict       		= 8
														lowercase_      :    Optional[Any]       		= torch.randint(256   ,   (batch_size, sequence_length, n_codes_total)   ,   dtype=torch.int				)

														lowercase_      :    int       		= model(UpperCAmelCase__   ,   UpperCAmelCase__				)
														lowercase_      :    Tuple       		= bark_model(UpperCAmelCase__   ,   UpperCAmelCase__				)

														lowercase_      :    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(UpperCAmelCase__				).mkdir(exist_ok=UpperCAmelCase__				)
							model.save_pretrained(UpperCAmelCase__				)







def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      Tuple   ,   )    ->   List[str]:
							lowercase_      :    Dict       		= os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__				)

							lowercase_      :    str       		= BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase__   ,   """config.json"""				)				)
							lowercase_      :    Optional[Any]       		= BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase__   ,   """config.json"""				)				)
							lowercase_      :    List[Any]       		= BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase__   ,   """config.json"""				)				)
							lowercase_      :    Any       		= EncodecConfig.from_pretrained("""facebook/encodec_24khz"""				)

							lowercase_      :    List[str]       		= BarkSemanticModel.from_pretrained(UpperCAmelCase__				)
							lowercase_      :    str       		= BarkCoarseModel.from_pretrained(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= BarkFineModel.from_pretrained(UpperCAmelCase__				)
							lowercase_      :    Tuple       		= EncodecModel.from_pretrained("""facebook/encodec_24khz"""				)

							lowercase_      :    Union[str, Any]       		= BarkConfig.from_sub_model_configs(
							    UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)

							lowercase_      :    List[Any]       		= BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config   ,   coarseAcoustic.generation_config   ,   fineAcoustic.generation_config				)

							lowercase_      :    Union[str, Any]       		= BarkModel(UpperCAmelCase__				)

							lowercase_      :    str       		= semantic
							lowercase_      :    Optional[int]       		= coarseAcoustic
							lowercase_      :    Tuple       		= fineAcoustic
							lowercase_      :    Union[str, Any]       		= codec

							lowercase_      :    Tuple       		= bark_generation_config

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


if __name__ == "__main__":
						_lowercase		:				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.")

						_lowercase		:				int	       =		parser.parse_args()

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






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





