






'''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()
__UpperCamelCase      :       Optional[Any]      			=      logging.get_logger(__name__)

set_seed(770)


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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str,					SCREAMING_SNAKE_CASE__:	int=False    )  ->			List[str]:

   """simple docstring"""
   __a				     =    model_type
   if use_small:
      key += "_small"
   return os.path.join(_lowerCamelCase,					REMOTE_MODEL_PATHS[key]['file_name']    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	str    )  ->			Tuple:

   """simple docstring"""
   os.makedirs(_lowerCamelCase,					exist_ok=_lowerCamelCase    )
   hf_hub_download(repo_id=_lowerCamelCase,					filename=_lowerCamelCase,					local_dir=_lowerCamelCase    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	Optional[Any]=False,					SCREAMING_SNAKE_CASE__:	Union[str, Any]="text"    )  ->			Optional[Any]:

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

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

   __a				     =    ConfigClass(**checkpoint['model_args']    )
   __a				     =    ModelClass(config=_lowerCamelCase    )
   __a				     =    GenerationConfigClass()

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

         __a				     =    state_dict.pop(_lowerCamelCase    )

   __a				     =    set(state_dict.keys()    ) - set(model.state_dict().keys()    )
   __a				     =    {k for k in extra_keys if not k.endswith('.attn.bias'    )}
   __a				     =    set(model.state_dict().keys()    ) - set(state_dict.keys()    )
   __a				     =    {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    )
   __a				     =    model.num_parameters(exclude_embeddings=_lowerCamelCase    )
   __a				     =    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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Optional[int]=False,					SCREAMING_SNAKE_CASE__:	List[Any]="text"    )  ->			Optional[Any]:

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

   __a				     =    'cpu'  # do conversion on cpu

   __a				     =    _get_ckpt_path(_lowerCamelCase,					use_small=_lowerCamelCase    )
   __a				     =    _load_model(_lowerCamelCase,					_lowerCamelCase,					model_type=_lowerCamelCase,					use_small=_lowerCamelCase    )

   # load bark initial model
   __a				     =    _bark_load_model(_lowerCamelCase,					'cpu',					model_type=_lowerCamelCase,					use_small=_lowerCamelCase    )

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

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

      __a				     =    model(_lowerCamelCase    )

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

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

      __a				     =    model(_lowerCamelCase,					_lowerCamelCase    )
      __a				     =    bark_model(_lowerCamelCase,					_lowerCamelCase    )

      __a				     =    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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	List[str],					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	str,					)  ->			Tuple:

   """simple docstring"""
   __a				     =    os.path.join(_lowerCamelCase,					_lowerCamelCase    )

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

   __a				     =    BarkSemanticModel.from_pretrained(_lowerCamelCase    )
   __a				     =    BarkCoarseModel.from_pretrained(_lowerCamelCase    )
   __a				     =    BarkFineModel.from_pretrained(_lowerCamelCase    )
   __a				     =    EncodecModel.from_pretrained('facebook/encodec_24khz'    )

   __a				     =    BarkConfig.from_sub_model_configs(
       _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase    )

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

   __a				     =    BarkModel(_lowerCamelCase    )

   __a				     =    semantic
   __a				     =    coarseAcoustic
   __a				     =    fineAcoustic
   __a				     =    codec

   __a				     =    bark_generation_config

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


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[int]      			=      parser.parse_args()

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







'''simple docstring'''



# 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.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
 from datasets import load_dataset
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
          "text to read (in English) and returns a waveform object containing the sound."
      )
      __a  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         if self.post_processor is None:
            __a				     =    'microsoft/speecht5_hifigan'
         super().setup()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



         '''simple docstring'''
         __a				     =    self.pre_processor(text=lowerCamelCase ,   return_tensors='pt' ,   truncation=lowerCamelCase      )

         if speaker_embeddings is None:
            if not is_datasets_available():
               raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'      )

            __a				     =    load_dataset('Matthijs/cmu-arctic-xvectors' ,   split='validation'      )
            __a				     =    torch.tensor(embeddings_dataset[7305]['xvector']      ).unsqueeze(0      )

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()