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

set_seed(7_7_0)


__lowerCAmelCase       :			Optional[int]   	={
    """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""",
}


__lowerCAmelCase       :			str   	={
    """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""",
    },
}

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


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[str]   ,						lowerCAmelCase__	:int=False						)					->       int:
      '''simple docstring'''


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:Optional[int]						)					->       str:
      '''simple docstring'''


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:List[Any]=False   ,						lowerCAmelCase__	:Optional[Any]="text"						)					->       Optional[Any]:
      '''simple docstring'''


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

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

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

      lowercase								=      model_generation_config
      lowercase								=      checkpoint["""model"""]
      # fixup checkpoint
      lowercase								=      """_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
                  lowercase								=      k[len(lowerCAmelCase__						) :]
                  for old_layer_name in new_layer_name_dict:
                        lowercase								=      new_k.replace(lowerCAmelCase__   ,						new_layer_name_dict[old_layer_name]						)

                  lowercase								=      state_dict.pop(lowerCAmelCase__						)

      lowercase								=      set(state_dict.keys()						) - set(model.state_dict().keys()						)
      lowercase								=      {k for k in extra_keys if not k.endswith(""".attn.bias"""						)}
      lowercase								=      set(model.state_dict().keys()						) - set(state_dict.keys()						)
      lowercase								=      {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__						)
      lowercase								=      model.num_parameters(exclude_embeddings=lowerCAmelCase__						)
      lowercase								=      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__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:List[str]=False   ,						lowerCAmelCase__	:int="text"						)					->       Optional[int]:
      '''simple docstring'''


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

      lowercase								=      """cpu"""  # do conversion on cpu

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

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

      if model_type == "text":
            lowercase								=      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
      lowercase								=      5
      lowercase								=      1_0

      if model_type in ["text", "coarse"]:
            lowercase								=      torch.randint(2_5_6   ,						(batch_size, sequence_length)   ,						dtype=torch.int						)
            lowercase								=      bark_model(lowerCAmelCase__						)[0]

            lowercase								=      model(lowerCAmelCase__						)

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

      else:
            lowercase								=      3
            lowercase								=      8
            lowercase								=      torch.randint(2_5_6   ,						(batch_size, sequence_length, n_codes_total)   ,						dtype=torch.int						)

            lowercase								=      model(lowerCAmelCase__   ,						lowerCAmelCase__						)
            lowercase								=      bark_model(lowerCAmelCase__   ,						lowerCAmelCase__						)

            lowercase								=      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__			(							lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:Tuple   ,						)					->       str:
      '''simple docstring'''


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

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

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

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

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

      lowercase								=      BarkModel(lowerCAmelCase__						)

      lowercase								=      semantic
      lowercase								=      coarseAcoustic
      lowercase								=      fineAcoustic
      lowercase								=      codec

      lowercase								=      bark_generation_config

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


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

       __lowerCAmelCase       :			List[str]   	=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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      'philschmid/bart-large-cnn-samsum'
      snake_case__     :  int				    =      (
          'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
          'and returns a summary of the text.'
      )
      snake_case__     :  Tuple				    =      'summarizer'
      snake_case__     :  int				    =      AutoTokenizer
      snake_case__     :  Any				    =      AutoModelForSeqaSeqLM

      snake_case__     :  Optional[int]				    =      ['text']
      snake_case__     :  Optional[int]				    =      ['text']

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor(__lowerCAmelCase						,			return_tensors="""pt"""						,			truncation=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.model.generate(**__lowerCAmelCase		)[0]







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor.decode(__lowerCAmelCase						,			skip_special_tokens=__lowerCAmelCase						,			clean_up_tokenization_spaces=__lowerCAmelCase		)



