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

set_seed(770)


_lowerCAmelCase  :Dict  				=    {
    """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  :Union[str, 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""",
    },
}

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


def     __lowerCAmelCase						(       a_       ,      a_=False		)      ->	Any:





     '''simple docstring'''





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


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





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


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=False       ,      a_="text"		)      ->	List[str]:





     '''simple docstring'''





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

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

     SCREAMING_SNAKE_CASE					:			Dict    	=       ConfigClass(**checkpoint['model_args']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ModelClass(config=a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       GenerationConfigClass()

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

               SCREAMING_SNAKE_CASE					:			Tuple    	=       state_dict.pop(a_		)

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       set(state_dict.keys()		) - set(model.state_dict().keys()		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       {k for k in extra_keys if not k.endswith('.attn.bias'		)}
     SCREAMING_SNAKE_CASE					:			Tuple    	=       set(model.state_dict().keys()		) - set(state_dict.keys()		)
     SCREAMING_SNAKE_CASE					:			str    	=       {k for k in missing_keys if not k.endswith('.attn.bias'		)}
     if len(a_		) != 0:
          raise ValueError(f"""extra keys found: {extra_keys}"""		)
     if len(a_		) != 0:
          raise ValueError(f"""missing keys: {missing_keys}"""		)
     model.load_state_dict(a_       ,      strict=a_		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model.num_parameters(exclude_embeddings=a_		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       checkpoint['best_val_loss'].item()
     logger.info(f"""model loaded: {round(n_params/1e6       ,      1		)}M params, {round(a_       ,      3		)} loss"""		)
     model.eval()
     model.to(a_		)
     del checkpoint, state_dict

     return model


def     __lowerCAmelCase						(       a_       ,      a_=False       ,      a_="text"		)      ->	Optional[int]:





     '''simple docstring'''





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

     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'cpu'  # do conversion on cpu

     SCREAMING_SNAKE_CASE					:			Tuple    	=       _get_ckpt_path(a_       ,      use_small=a_		)
     SCREAMING_SNAKE_CASE					:			str    	=       _load_model(a_       ,      a_       ,      model_type=a_       ,      use_small=a_		)

     # load bark initial model
     SCREAMING_SNAKE_CASE					:			Any    	=       _bark_load_model(a_       ,      'cpu'       ,      model_type=a_       ,      use_small=a_		)

     if model_type == "text":
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       bark_model['model']

     if model.num_parameters(exclude_embeddings=a_		) != 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
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       5
     SCREAMING_SNAKE_CASE					:			List[str]    	=       10

     if model_type in ["text", "coarse"]:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.randint(256       ,      (batch_size, sequence_length)       ,      dtype=torch.int		)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       bark_model(a_		)[0]

          SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(a_		)

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

     else:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       3
          SCREAMING_SNAKE_CASE					:			List[str]    	=       8
          SCREAMING_SNAKE_CASE					:			str    	=       torch.randint(256       ,      (batch_size, sequence_length, n_codes_total)       ,      dtype=torch.int		)

          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(a_       ,      a_		)
          SCREAMING_SNAKE_CASE					:			Any    	=       bark_model(a_       ,      a_		)

          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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(a_		).mkdir(exist_ok=a_		)
     model.save_pretrained(a_		)


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_       ,      a_       ,      a_       ,      )      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       os.path.join(a_       ,      a_		)

     SCREAMING_SNAKE_CASE					:			Tuple    	=       BarkSemanticConfig.from_pretrained(os.path.join(a_       ,      'config.json'		)		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       BarkCoarseConfig.from_pretrained(os.path.join(a_       ,      'config.json'		)		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       BarkFineConfig.from_pretrained(os.path.join(a_       ,      'config.json'		)		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       EncodecConfig.from_pretrained('facebook/encodec_24khz'		)

     SCREAMING_SNAKE_CASE					:			List[str]    	=       BarkSemanticModel.from_pretrained(a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       BarkCoarseModel.from_pretrained(a_		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BarkFineModel.from_pretrained(a_		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       EncodecModel.from_pretrained('facebook/encodec_24khz'		)

     SCREAMING_SNAKE_CASE					:			List[str]    	=       BarkConfig.from_sub_model_configs(
         a_       ,      a_       ,      a_       ,      a_		)

     SCREAMING_SNAKE_CASE					:			str    	=       BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config       ,      coarseAcoustic.generation_config       ,      fineAcoustic.generation_config		)

     SCREAMING_SNAKE_CASE					:			Dict    	=       BarkModel(a_		)

     SCREAMING_SNAKE_CASE					:			List[str]    	=       semantic
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       coarseAcoustic
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       fineAcoustic
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       codec

     SCREAMING_SNAKE_CASE					:			Tuple    	=       bark_generation_config

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


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

 _lowerCAmelCase  :Optional[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

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0] * no_of_processes
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0

     # When processes are not completed,
     # A process whose arrival time has passed \
     # and has remaining execution time is put into the ready_process.
     # The shortest process in the ready_process, target_process is executed.

     while completed != no_of_processes:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
          SCREAMING_SNAKE_CASE					:			Dict    	=       -1

          for i in range(a_		):
               if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                    ready_process.append(a_		)

          if len(a_		) > 0:
               SCREAMING_SNAKE_CASE					:			Dict    	=       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       i
               total_time += burst_time[target_process]
               completed += 1
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       0
               SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


if __name__ == "__main__":
 print("""[TEST CASE 01]""")

 _lowerCAmelCase  :Optional[int]  				=    4
 _lowerCAmelCase  :Optional[int]  				=    [2, 5, 3, 7]
 _lowerCAmelCase  :List[str]  				=    [0, 0, 0, 0]
 _lowerCAmelCase  :Union[str, Any]  				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 _lowerCAmelCase  :Any  				=    calculate_turnaroundtime(
     burst_time, no_of_processes, waiting_time
 )

 # Printing the Result
 print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
 for i, process_id in enumerate(list(range(1, 5))):
  print(
      f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
      f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
  )
 print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
 print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")



