




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								=	logging.get_logger(__name__)

set_seed(770)


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



def 				_A  (		lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   Tuple=False     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   int  ,							lowerCAmelCase_	:   List[Any]     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   Any  ,							lowerCAmelCase_	:   List[str]=False  ,							lowerCAmelCase_	:   Optional[Any]="text"     ):
     """simple docstring"""

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

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

     lowerCAmelCase__            =					ConfigClass(**checkpoint["model_args"]     )
     lowerCAmelCase__            =					ModelClass(config=lowerCAmelCase_     )
     lowerCAmelCase__            =					GenerationConfigClass()

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

               lowerCAmelCase__            =					state_dict.pop(lowerCAmelCase_     )

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

     return model



def 				_A  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   str=False  ,							lowerCAmelCase_	:   int="text"     ):
     """simple docstring"""

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

     lowerCAmelCase__            =					"cpu"  # do conversion on cpu

     lowerCAmelCase__            =					_get_ckpt_path(lowerCAmelCase_  ,							use_small=lowerCAmelCase_     )
     lowerCAmelCase__            =					_load_model(lowerCAmelCase_  ,							lowerCAmelCase_  ,							model_type=lowerCAmelCase_  ,							use_small=lowerCAmelCase_     )

     # load bark initial model
     lowerCAmelCase__            =					_bark_load_model(lowerCAmelCase_  ,							"cpu"  ,							model_type=lowerCAmelCase_  ,							use_small=lowerCAmelCase_     )

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

     if model.num_parameters(exclude_embeddings=lowerCAmelCase_     ) != 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
     lowerCAmelCase__            =					5
     lowerCAmelCase__            =					10

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

          lowerCAmelCase__            =					model(lowerCAmelCase_     )

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

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

          lowerCAmelCase__            =					model(lowerCAmelCase_  ,							lowerCAmelCase_     )
          lowerCAmelCase__            =					bark_model(lowerCAmelCase_  ,							lowerCAmelCase_     )

          lowerCAmelCase__            =					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(lowerCAmelCase_     ).mkdir(exist_ok=lowerCAmelCase_     )
     model.save_pretrained(lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   List[Any]  ,							):
     """simple docstring"""

     lowerCAmelCase__            =					os.path.join(lowerCAmelCase_  ,							lowerCAmelCase_     )

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

     lowerCAmelCase__            =					BarkSemanticModel.from_pretrained(lowerCAmelCase_     )
     lowerCAmelCase__            =					BarkCoarseModel.from_pretrained(lowerCAmelCase_     )
     lowerCAmelCase__            =					BarkFineModel.from_pretrained(lowerCAmelCase_     )
     lowerCAmelCase__            =					EncodecModel.from_pretrained("facebook/encodec_24khz"     )

     lowerCAmelCase__            =					BarkConfig.from_sub_model_configs(
         lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )

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

     lowerCAmelCase__            =					BarkModel(lowerCAmelCase_     )

     lowerCAmelCase__            =					semantic
     lowerCAmelCase__            =					coarseAcoustic
     lowerCAmelCase__            =					fineAcoustic
     lowerCAmelCase__            =					codec

     lowerCAmelCase__            =					bark_generation_config

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


if __name__ == "__main__":
     UpperCamelCase								=	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								=	parser.parse_args()

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


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						="openai/whisper-base"
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(
     "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
     "transcribed text."
 )
 SCREAMING_SNAKE_CASE_				:					int       						="transcriber"
 SCREAMING_SNAKE_CASE_				:					Tuple       						=WhisperProcessor
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=WhisperForConditionalGeneration

 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=["audio"]
 SCREAMING_SNAKE_CASE_				:					Any       						=["text"]

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[Any]   ):






      """simple docstring"""



      return self.pre_processor(SCREAMING_SNAKE_CASE__					, return_tensors='pt'   ).input_features

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      return self.model.generate(inputs=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      return self.pre_processor.batch_decode(SCREAMING_SNAKE_CASE__					, skip_special_tokens=SCREAMING_SNAKE_CASE__   )[0]


