
"""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__          =	logging.get_logger(__name__)

set_seed(7_70)


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 					lowerCamelCase   (     _snake_case    ,_snake_case=False ):
    UpperCAmelCase__	:      Optional[int]     							=					model_type
    if use_small:
        key += "_small"
    return os.path.join(_snake_case    ,REMOTE_MODEL_PATHS[key]['file_name'] )
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    os.makedirs(_snake_case    ,exist_ok=_snake_case )
    hf_hub_download(repo_id=_snake_case    ,filename=_snake_case    ,local_dir=_snake_case )
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case=False    ,_snake_case="text" ):
    if model_type == "text":
        UpperCAmelCase__	:      List[str]     							=					BarkSemanticModel
        UpperCAmelCase__	:      Union[str, Any]     							=					BarkSemanticConfig
        UpperCAmelCase__	:      Any     							=					BarkSemanticGenerationConfig
    elif model_type == "coarse":
        UpperCAmelCase__	:      str     							=					BarkCoarseModel
        UpperCAmelCase__	:      Dict     							=					BarkCoarseConfig
        UpperCAmelCase__	:      List[str]     							=					BarkCoarseGenerationConfig
    elif model_type == "fine":
        UpperCAmelCase__	:      Any     							=					BarkFineModel
        UpperCAmelCase__	:      str     							=					BarkFineConfig
        UpperCAmelCase__	:      str     							=					BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    UpperCAmelCase__	:      List[str]     							=					F'''{model_type}_small''' if use_small else model_type
    UpperCAmelCase__	:      List[str]     							=					REMOTE_MODEL_PATHS[model_key]
    if not os.path.exists(_snake_case ):
        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(_snake_case    ,map_location=_snake_case )
    # this is a hack
    UpperCAmelCase__	:      Optional[int]     							=					checkpoint['model_args']
    if "input_vocab_size" not in model_args:
        UpperCAmelCase__	:      int     							=					model_args['vocab_size']
        UpperCAmelCase__	:      Tuple     							=					model_args['vocab_size']
        del model_args["vocab_size"]

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

    UpperCAmelCase__	:      str     							=					ConfigClass(**checkpoint['model_args'] )
    UpperCAmelCase__	:      Union[str, Any]     							=					ModelClass(config=_snake_case )
    UpperCAmelCase__	:      Any     							=					GenerationConfigClass()

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

            UpperCAmelCase__	:      Optional[int]     							=					state_dict.pop(_snake_case )

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

    return model
def 					lowerCamelCase   (     _snake_case    ,_snake_case=False    ,_snake_case="text" ):
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    UpperCAmelCase__	:      Dict     							=					'cpu'  # do conversion on cpu

    UpperCAmelCase__	:      List[Any]     							=					_get_ckpt_path(_snake_case    ,use_small=_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					_load_model(_snake_case    ,_snake_case    ,model_type=_snake_case    ,use_small=_snake_case )

    # load bark initial model
    UpperCAmelCase__	:      Optional[Any]     							=					_bark_load_model(_snake_case    ,'cpu'    ,model_type=_snake_case    ,use_small=_snake_case )

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

    if model.num_parameters(exclude_embeddings=_snake_case ) != 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__	:      Tuple     							=					5
    UpperCAmelCase__	:      Dict     							=					10

    if model_type in ["text", "coarse"]:
        UpperCAmelCase__	:      str     							=					torch.randint(256    ,(batch_size, sequence_length)    ,dtype=torch.int )
        UpperCAmelCase__	:      Dict     							=					bark_model(_snake_case )[0]

        UpperCAmelCase__	:      Union[str, Any]     							=					model(_snake_case )

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

    else:
        UpperCAmelCase__	:      Tuple     							=					3
        UpperCAmelCase__	:      int     							=					8
        UpperCAmelCase__	:      Union[str, Any]     							=					torch.randint(256    ,(batch_size, sequence_length, n_codes_total)    ,dtype=torch.int )

        UpperCAmelCase__	:      int     							=					model(_snake_case    ,_snake_case )
        UpperCAmelCase__	:      Dict     							=					bark_model(_snake_case    ,_snake_case )

        UpperCAmelCase__	:      Dict     							=					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(_snake_case ).mkdir(exist_ok=_snake_case )
    model.save_pretrained(_snake_case )
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,):
    UpperCAmelCase__	:      Union[str, Any]     							=					os.path.join(_snake_case    ,_snake_case )

    UpperCAmelCase__	:      Optional[Any]     							=					BarkSemanticConfig.from_pretrained(os.path.join(_snake_case    ,'config.json' ) )
    UpperCAmelCase__	:      Dict     							=					BarkCoarseConfig.from_pretrained(os.path.join(_snake_case    ,'config.json' ) )
    UpperCAmelCase__	:      List[str]     							=					BarkFineConfig.from_pretrained(os.path.join(_snake_case    ,'config.json' ) )
    UpperCAmelCase__	:      Optional[Any]     							=					EncodecConfig.from_pretrained('facebook/encodec_24khz' )

    UpperCAmelCase__	:      Dict     							=					BarkSemanticModel.from_pretrained(_snake_case )
    UpperCAmelCase__	:      Tuple     							=					BarkCoarseModel.from_pretrained(_snake_case )
    UpperCAmelCase__	:      List[Any]     							=					BarkFineModel.from_pretrained(_snake_case )
    UpperCAmelCase__	:      List[str]     							=					EncodecModel.from_pretrained('facebook/encodec_24khz' )

    UpperCAmelCase__	:      Dict     							=					BarkConfig.from_sub_model_configs(
        _snake_case    ,_snake_case    ,_snake_case    ,_snake_case )

    UpperCAmelCase__	:      Tuple     							=					BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config    ,coarseAcoustic.generation_config    ,fineAcoustic.generation_config )

    UpperCAmelCase__	:      int     							=					BarkModel(_snake_case )

    UpperCAmelCase__	:      Any     							=					semantic
    UpperCAmelCase__	:      Any     							=					coarseAcoustic
    UpperCAmelCase__	:      int     							=					fineAcoustic
    UpperCAmelCase__	:      Tuple     							=					codec

    UpperCAmelCase__	:      List[Any]     							=					bark_generation_config

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


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)



"""simple docstring"""



from __future__ import annotations

UpperCamelCase__          =	tuple[int, int, int]
UpperCamelCase__          =	tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__          =	'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
UpperCamelCase__          =	'EGZWVONAHDCLFQMSIPJBYUKXTR'
UpperCamelCase__          =	'FOBHMDKEXQNRAULPGSJVTYICZW'
UpperCamelCase__          =	'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
UpperCamelCase__          =	{
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
UpperCamelCase__          =	'RMDJXFUWGISLHVTCQNKYPBEZOA'
UpperCamelCase__          =	'SGLCPQWZHKXAREONTFBVIYJUDM'
UpperCamelCase__          =	'HVSICLTYKQUBXDWAJZOMFGPREN'
UpperCamelCase__          =	'RZWQHFMVDBKICJLNTUXAGYPSOE'
UpperCamelCase__          =	'LFKIJODBEGAMQPXVUHYSTCZRWN'
UpperCamelCase__          =	'KOAEGVDHXPQZMLFTYWJNBRCIUS'
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    # Checks if there are 3 unique rotors

    if (unique_rotsel := len(set(_snake_case ) )) < 3:
        UpperCAmelCase__	:      int     							=					F'''Please use 3 unique rotors (not {unique_rotsel})'''
        raise Exception(_snake_case )

    # Checks if rotor positions are valid
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					rotpos
    if not 0 < rotorposa <= len(_snake_case ):
        UpperCAmelCase__	:      List[Any]     							=					F'''First rotor position is not within range of 1..26 ({rotorposa}'''
        raise ValueError(_snake_case )
    if not 0 < rotorposa <= len(_snake_case ):
        UpperCAmelCase__	:      Tuple     							=					F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(_snake_case )
    if not 0 < rotorposa <= len(_snake_case ):
        UpperCAmelCase__	:      Tuple     							=					F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(_snake_case )

    # Validates string and returns dict
    UpperCAmelCase__	:      Optional[int]     							=					_plugboard(_snake_case )

    return rotpos, rotsel, pbdict
def 					lowerCamelCase   (     _snake_case ):

    # tests the input string if it
    # a) is type string
    # b) has even length (so pairs can be made)
    if not isinstance(_snake_case    ,_snake_case ):
        UpperCAmelCase__	:      Union[str, Any]     							=					F'''Plugboard setting isn\'t type string ({type(_snake_case )})'''
        raise TypeError(_snake_case )
    elif len(_snake_case ) % 2 != 0:
        UpperCAmelCase__	:      Dict     							=					F'''Odd number of symbols ({len(_snake_case )})'''
        raise Exception(_snake_case )
    elif pbstring == "":
        return {}

    pbstring.replace(' '    ,'' )

    # Checks if all characters are unique
    UpperCAmelCase__	:      List[Any]     							=					set()
    for i in pbstring:
        if i not in abc:
            UpperCAmelCase__	:      List[Any]     							=					F'''\'{i}\' not in list of symbols'''
            raise Exception(_snake_case )
        elif i in tmppbl:
            UpperCAmelCase__	:      Optional[Any]     							=					F'''Duplicate symbol ({i})'''
            raise Exception(_snake_case )
        else:
            tmppbl.add(_snake_case )
    del tmppbl

    # Created the dictionary
    UpperCAmelCase__	:      str     							=					{}
    for j in range(0    ,len(_snake_case ) - 1    ,2 ):
        UpperCAmelCase__	:      Dict     							=					pbstring[j + 1]
        UpperCAmelCase__	:      int     							=					pbstring[j]

    return pb
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case = (rotora, rotora, rotora)    ,_snake_case = ""    ,):
    UpperCAmelCase__	:      str     							=					text.upper()
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					_validator(
        _snake_case    ,_snake_case    ,plugb.upper() )

    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					rotor_position
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    UpperCAmelCase__	:      Any     							=					[]

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                UpperCAmelCase__	:      Dict     							=					plugboard[symbol]

            # rotor ra --------------------------
            UpperCAmelCase__	:      List[str]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      Optional[Any]     							=					rotora[index % len(_snake_case )]

            # rotor rb --------------------------
            UpperCAmelCase__	:      Optional[int]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      List[Any]     							=					rotora[index % len(_snake_case )]

            # rotor rc --------------------------
            UpperCAmelCase__	:      Optional[Any]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      Any     							=					rotora[index % len(_snake_case )]

            # reflector --------------------------
            # this is the reason you don't need another machine to decipher

            UpperCAmelCase__	:      Union[str, Any]     							=					reflector[symbol]

            # 2nd rotors
            UpperCAmelCase__	:      List[str]     							=					abc[rotora.index(_snake_case ) - rotorposa]
            UpperCAmelCase__	:      Optional[Any]     							=					abc[rotora.index(_snake_case ) - rotorposa]
            UpperCAmelCase__	:      List[Any]     							=					abc[rotora.index(_snake_case ) - rotorposa]

            # 2nd plugboard
            if symbol in plugboard:
                UpperCAmelCase__	:      int     							=					plugboard[symbol]

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      Dict     							=					0
                rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      Any     							=					0
                rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      int     							=					0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
        result.append(_snake_case )

    return "".join(_snake_case )


if __name__ == "__main__":
    UpperCamelCase__          =	'This is my Python script that emulates the Enigma machine from WWII.'
    UpperCamelCase__          =	(1, 1, 1)
    UpperCamelCase__          =	'pictures'
    UpperCamelCase__          =	(rotora, rotora, rotora)
    UpperCamelCase__          =	enigma(message, rotor_pos, rotor_sel, pb)

    print('Encrypted message:', en)
    print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))

