

'''simple docstring'''

from __future__ import annotations




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					str	)						->     Any:
      UpperCAmelCase ,			UpperCAmelCase	:     List[Any]						 =			text, pattern
      UpperCAmelCase ,			UpperCAmelCase	:     Any						 =			len(lowercase_	), len(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					str	)						->     int:

      for i in range(self.patLen - 1					,      -1					,      -1	):
       if char == self.pattern[i]:
        return i
      return -1


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					int	)						->     int:

      for i in range(self.patLen - 1					,      -1					,      -1	):
       if self.pattern[i] != self.text[current_pos + i]:
        return current_pos + i
      return -1




     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     list[int]:
      # searches pattern in text and returns index positions
      UpperCAmelCase	:     int						 =			[]
      for i in range(self.textLen - self.patLen + 1	):
       UpperCAmelCase	:     int						 =			self.mismatch_in_text(lowercase_	)
       if mismatch_index == -1:
        positions.append(lowercase_	)
       else:
        UpperCAmelCase	:     Dict						 =			self.match_in_pattern(self.text[mismatch_index]	)
        UpperCAmelCase	:     Tuple						 =			(
            mismatch_index - match_index
        )  # shifting index lgtm [py/multiple-definition]
      return positions



lowercase__         =      "ABAABA"
lowercase__         =      "AB"
lowercase__         =      BoyerMooreSearch(text, pattern)
lowercase__         =      bms.bad_character_heuristic()

if len(positions) == 0:
 print("No match found")
else:
 print("Pattern found in following positions: ")
 print(positions)







'''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()
lowercase__         =      logging.get_logger(__name__)

set_seed(770)


lowercase__         =      {
    "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",
}


lowercase__         =      {
    "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",
    },
}

lowercase__         =      os.path.dirname(os.path.abspath(__file__))
lowercase__         =      os.path.join(os.path.expanduser("~"), ".cache")
lowercase__         =      os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
 UpperCAmelCase	:     List[str]						 =			model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCAmelCase_  ,	REMOTE_MODEL_PATHS[key]['file_name']     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 os.makedirs(UpperCAmelCase_  ,	exist_ok=UpperCAmelCase_     )
 hf_hub_download(repo_id=UpperCAmelCase_  ,	filename=UpperCAmelCase_  ,	local_dir=UpperCAmelCase_     )




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

 # convert Bark model arguments to HF Bark model arguments
 UpperCAmelCase	:     Any						 =			model_args.pop('n_head'     )
 UpperCAmelCase	:     Optional[Any]						 =			model_args.pop('n_embd'     )
 UpperCAmelCase	:     Union[str, Any]						 =			model_args.pop('n_layer'     )

 UpperCAmelCase	:     List[Any]						 =			ConfigClass(**checkpoint['model_args']     )
 UpperCAmelCase	:     List[str]						 =			ModelClass(config=UpperCAmelCase_     )
 UpperCAmelCase	:     List[str]						 =			GenerationConfigClass()

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

   UpperCAmelCase	:     List[Any]						 =			state_dict.pop(UpperCAmelCase_     )

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

 return model




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False  ,	UpperCAmelCase_="text"     ):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 UpperCAmelCase	:     List[str]						 =			'cpu'  # do conversion on cpu

 UpperCAmelCase	:     List[str]						 =			_get_ckpt_path(UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			_load_model(UpperCAmelCase_  ,	UpperCAmelCase_  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

 # load bark initial model
 UpperCAmelCase	:     List[str]						 =			_bark_load_model(UpperCAmelCase_  ,	'cpu'  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

 if model_type == "text":
  UpperCAmelCase	:     Tuple						 =			bark_model['model']

 if model.num_parameters(exclude_embeddings=UpperCAmelCase_     ) != 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	:     Optional[int]						 =			5
 UpperCAmelCase	:     Optional[int]						 =			10

 if model_type in ["text", "coarse"]:
  UpperCAmelCase	:     List[Any]						 =			torch.randint(2_56  ,	(batch_size, sequence_length)  ,	dtype=torch.int     )
  UpperCAmelCase	:     Optional[Any]						 =			bark_model(UpperCAmelCase_     )[0]

  UpperCAmelCase	:     List[str]						 =			model(UpperCAmelCase_     )

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

 else:
  UpperCAmelCase	:     Optional[int]						 =			3
  UpperCAmelCase	:     List[Any]						 =			8
  UpperCAmelCase	:     Dict						 =			torch.randint(2_56  ,	(batch_size, sequence_length, n_codes_total)  ,	dtype=torch.int     )

  UpperCAmelCase	:     str						 =			model(UpperCAmelCase_  ,	UpperCAmelCase_     )
  UpperCAmelCase	:     Dict						 =			bark_model(UpperCAmelCase_  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     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(UpperCAmelCase_     ).mkdir(exist_ok=UpperCAmelCase_     )
 model.save_pretrained(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	):
 UpperCAmelCase	:     int						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Dict						 =			BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Any						 =			BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Union[str, Any]						 =			BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Any						 =			EncodecConfig.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Dict						 =			BarkSemanticModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			BarkCoarseModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			BarkFineModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			EncodecModel.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Optional[Any]						 =			BarkConfig.from_sub_model_configs(
     UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Optional[int]						 =			BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config  ,	coarseAcoustic.generation_config  ,	fineAcoustic.generation_config     )

 UpperCAmelCase	:     str						 =			BarkModel(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			semantic
 UpperCAmelCase	:     Tuple						 =			coarseAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			fineAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			codec

 UpperCAmelCase	:     Optional[int]						 =			bark_generation_config

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


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

 lowercase__         =      parser.parse_args()

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




