



"""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()
__A   :							Any										=   logging.get_logger(__name__)

set_seed(770)


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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Dict=False				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Dict=False					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]="text"				):






				'''simple docstring'''



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

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

				_UpperCAmelCase					=    ConfigClass(**checkpoint['''model_args''']				)
				_UpperCAmelCase					=    ModelClass(config=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    GenerationConfigClass()

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

												_UpperCAmelCase					=    state_dict.pop(_SCREAMING_SNAKE_CASE				)

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

				return model







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Optional[int]=False					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]="text"				):






				'''simple docstring'''



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

				_UpperCAmelCase					=    '''cpu'''  # do conversion on cpu

				_UpperCAmelCase					=    _get_ckpt_path(_SCREAMING_SNAKE_CASE					,     use_small=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    _load_model(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     model_type=_SCREAMING_SNAKE_CASE					,     use_small=_SCREAMING_SNAKE_CASE				)

				# load bark initial model
				_UpperCAmelCase					=    _bark_load_model(_SCREAMING_SNAKE_CASE					,     '''cpu'''					,     model_type=_SCREAMING_SNAKE_CASE					,     use_small=_SCREAMING_SNAKE_CASE				)

				if model_type == "text":
								_UpperCAmelCase					=    bark_model['''model''']

				if model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE				) != 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
				_UpperCAmelCase					=    5
				_UpperCAmelCase					=    10

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

								_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE				)

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

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

								_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    bark_model(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

								_UpperCAmelCase					=    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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
				model.save_pretrained(_SCREAMING_SNAKE_CASE				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						int					,     ):






				'''simple docstring'''



				_UpperCAmelCase					=    os.path.join(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

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

				_UpperCAmelCase					=    BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    EncodecModel.from_pretrained('''facebook/encodec_24khz'''				)

				_UpperCAmelCase					=    BarkConfig.from_sub_model_configs(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

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

				_UpperCAmelCase					=    BarkModel(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    semantic
				_UpperCAmelCase					=    coarseAcoustic
				_UpperCAmelCase					=    fineAcoustic
				_UpperCAmelCase					=    codec

				_UpperCAmelCase					=    bark_generation_config

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


if __name__ == "__main__":
						__A   :							List[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.")

						__A   :							Tuple										=   parser.parse_args()

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








"""simple docstring"""


from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


__A   :							List[str]										=   logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    Union[str, Any]				,   **__UpperCamelCase     :    Optional[Any]   )->List[Any]:
							super().__init__(**__UpperCamelCase   )

							if self.framework != "pt":
											raise ValueError(F'The {self.__class__} is only available in PyTorch.'   )
							# No specific FOR_XXX available yet



			def __call__(		self     :    Optional[int]				,   __UpperCamelCase     :    Union[np.ndarray, bytes, str]				,   **__UpperCamelCase     :    Tuple   )->List[str]:
							return super().__call__(__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Any				,   **__UpperCamelCase     :    Any   )->Union[str, Any]:
							_UpperCAmelCase					=    {}
							if "candidate_labels" in kwargs:
											_UpperCAmelCase					=    kwargs['''candidate_labels''']
							if "hypothesis_template" in kwargs:
											_UpperCAmelCase					=    kwargs['''hypothesis_template''']

							return preprocess_params, {}, {}



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    List[Any]=None				,   __UpperCamelCase     :    Tuple="This is a sound of {}."   )->int:
							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											if audio.startswith('''http://'''   ) or audio.startswith('''https://'''   ):
															# We need to actually check for a real protocol, otherwise it's impossible to use a local file
															# like http_huggingface_co.png
															_UpperCAmelCase					=    requests.get(__UpperCamelCase   ).content
											else:
															with open(__UpperCamelCase				,   '''rb'''   ) as f:
																			_UpperCAmelCase					=    f.read()

							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    ffmpeg_read(__UpperCamelCase				,   self.feature_extractor.sampling_rate   )

							if not isinstance(__UpperCamelCase				,   np.ndarray   ):
											raise ValueError('''We expect a numpy ndarray as input'''   )
							if len(audio.shape   ) != 1:
											raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline'''   )

							_UpperCAmelCase					=    self.feature_extractor(
							    [audio]				,   sampling_rate=self.feature_extractor.sampling_rate				,   return_tensors='''pt'''   )
							_UpperCAmelCase					=    candidate_labels
							_UpperCAmelCase					=    [hypothesis_template.format(__UpperCamelCase   ) for x in candidate_labels]
							_UpperCAmelCase					=    self.tokenizer(__UpperCamelCase				,   return_tensors=self.framework				,   padding=__UpperCamelCase   )
							_UpperCAmelCase					=    [text_inputs]
							return inputs



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Optional[int]   )->Any:
							_UpperCAmelCase					=    model_inputs.pop('''candidate_labels'''   )
							_UpperCAmelCase					=    model_inputs.pop('''text_inputs'''   )
							if isinstance(text_inputs[0]				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    text_inputs[0]
							else:
											# Batching case.
											_UpperCAmelCase					=    text_inputs[0][0]

							_UpperCAmelCase					=    self.model(**__UpperCamelCase				,   **__UpperCamelCase   )

							_UpperCAmelCase					=    {
							    '''candidate_labels''': candidate_labels,
							    '''logits''': outputs.logits_per_audio,
							}
							return model_outputs



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_UpperCAmelCase					=    model_outputs.pop('''candidate_labels'''   )
							_UpperCAmelCase					=    model_outputs['''logits'''][0]

							if self.framework == "pt":
											_UpperCAmelCase					=    logits.softmax(dim=0   )
											_UpperCAmelCase					=    probs.tolist()
							else:
											raise ValueError('''`tf` framework not supported.'''   )

							_UpperCAmelCase					=    [
							    {'''score''': score, '''label''': candidate_label}
							    for score, candidate_label in sorted(zip(__UpperCamelCase				,   __UpperCamelCase   )				,   key=lambda __UpperCamelCase   : -x[0]   )
							]
							return result



