






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

set_seed(7_7_0)


a_					=	{
    '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',
}


a_					=	{
    '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',
    },
}

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





def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  List[str]=False							):




						'''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  Dict							):




						'''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  List[Any]=False,		UpperCamelCase__			:  List[str]="text"							):




						'''simple docstring'''






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

						# convert Bark model arguments to HF Bark model arguments
						SCREAMING_SNAKE_CASE__       :			Tuple													=model_args.pop('''n_head'''							)
						SCREAMING_SNAKE_CASE__       :			List[str]													=model_args.pop('''n_embd'''							)
						SCREAMING_SNAKE_CASE__       :			str													=model_args.pop('''n_layer'''							)

						SCREAMING_SNAKE_CASE__       :			int													=ConfigClass(**checkpoint['''model_args''']							)
						SCREAMING_SNAKE_CASE__       :			int													=ModelClass(config=__SCREAMING_SNAKE_CASE							)
						SCREAMING_SNAKE_CASE__       :			List[str]													=GenerationConfigClass()

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

																		SCREAMING_SNAKE_CASE__       :			Tuple													=state_dict.pop(__SCREAMING_SNAKE_CASE							)

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

						return model





def 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  List[str]=False,		UpperCamelCase__			:  Optional[int]="text"							):




						'''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__       :			Union[str, Any]													=_get_ckpt_path(__SCREAMING_SNAKE_CASE,		use_small=__SCREAMING_SNAKE_CASE							)
						SCREAMING_SNAKE_CASE__       :			Any													=_load_model(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		model_type=__SCREAMING_SNAKE_CASE,		use_small=__SCREAMING_SNAKE_CASE							)

						# load bark initial model
						SCREAMING_SNAKE_CASE__       :			Optional[int]													=_bark_load_model(__SCREAMING_SNAKE_CASE,		'''cpu''',		model_type=__SCREAMING_SNAKE_CASE,		use_small=__SCREAMING_SNAKE_CASE							)

						if model_type == "text":
												SCREAMING_SNAKE_CASE__       :			Tuple													=bark_model["""model"""]

						if model.num_parameters(exclude_embeddings=__SCREAMING_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
						SCREAMING_SNAKE_CASE__       :			Dict													=5
						SCREAMING_SNAKE_CASE__       :			str													=1_0

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

												SCREAMING_SNAKE_CASE__       :			Dict													=model(__SCREAMING_SNAKE_CASE							)

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

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

												SCREAMING_SNAKE_CASE__       :			List[str]													=model(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
												SCREAMING_SNAKE_CASE__       :			Dict													=bark_model(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

												SCREAMING_SNAKE_CASE__       :			Union[str, 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(__SCREAMING_SNAKE_CASE							).mkdir(exist_ok=__SCREAMING_SNAKE_CASE							)
						model.save_pretrained(__SCREAMING_SNAKE_CASE							)





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Any,		UpperCamelCase__			:  int,		UpperCamelCase__			:  str,		UpperCamelCase__			:  str,		UpperCamelCase__			:  Dict,		):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__       :			List[Any]													=os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

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

						SCREAMING_SNAKE_CASE__       :			int													=BarkSemanticModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
						SCREAMING_SNAKE_CASE__       :			Dict													=BarkCoarseModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
						SCREAMING_SNAKE_CASE__       :			Dict													=BarkFineModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
						SCREAMING_SNAKE_CASE__       :			str													=EncodecModel.from_pretrained('''facebook/encodec_24khz'''							)

						SCREAMING_SNAKE_CASE__       :			List[Any]													=BarkConfig.from_sub_model_configs(
						    __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

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

						SCREAMING_SNAKE_CASE__       :			int													=BarkModel(__SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__       :			List[str]													=semantic
						SCREAMING_SNAKE_CASE__       :			Any													=coarseAcoustic
						SCREAMING_SNAKE_CASE__       :			Optional[Any]													=fineAcoustic
						SCREAMING_SNAKE_CASE__       :			Optional[int]													=codec

						SCREAMING_SNAKE_CASE__       :			str													=bark_generation_config

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


if __name__ == "__main__":
		a_					=	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.')

		a_					=	parser.parse_args()

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







'''simple docstring'''
import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


a_					=	get_logger(__name__)


a_					=	R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'






class        __SCREAMING_SNAKE_CASE :




 @add_start_docstrings(__lowercase )
 def __call__( self		:							Any   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray )		->  jnp.ndarray:
       raise NotImplementedError(
           F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )






class        __SCREAMING_SNAKE_CASE :




 @add_start_docstrings(__lowercase )
 def __call__( self		:							List[str]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray )		->  jnp.ndarray:
       raise NotImplementedError(
           F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 @add_start_docstrings(__lowercase )
 def __call__( self		:							Any   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int   , **__lowercase		:							List[str] )		->  jnp.ndarray:
       for processor in self:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=inspect.signature(processor.__call__ ).parameters
             if len(__lowercase ) > 3:
                   if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
                         raise ValueError(
                             F"Make sure that all the required parameters: {list(function_args.keys() )} for "
                             F"{processor.__class__} are passed to the logits processor." )
                   SCREAMING_SNAKE_CASE__       :			List[Any]													=processor(__lowercase   , __lowercase   , __lowercase   , **__lowercase )
             else:
                   SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=processor(__lowercase   , __lowercase   , __lowercase )
       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[str]   , __lowercase		:							float )		->  Tuple:
       if not isinstance(__lowercase   , __lowercase ) or not (temperature > 0):
             raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}" )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=temperature




 def __call__( self		:							Optional[int]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			int													=scores / self.temperature
       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Tuple   , __lowercase		:							float   , __lowercase		:							float = -float('''Inf''' )   , __lowercase		:							int = 1 )		->  List[str]:
       if not isinstance(__lowercase   , __lowercase ) or (top_p < 0 or top_p > 1.0):
             raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}" )
       if not isinstance(__lowercase   , __lowercase ) or (min_tokens_to_keep < 1):
             raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}" )

       SCREAMING_SNAKE_CASE__       :			Tuple													=top_p
       SCREAMING_SNAKE_CASE__       :			List[str]													=filter_value
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=min_tokens_to_keep




 def __call__( self		:							int   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=lax.top_k(__lowercase   , scores.shape[-1] )

       SCREAMING_SNAKE_CASE__       :			List[str]													=jnp.full_like(__lowercase   , self.filter_value )
       SCREAMING_SNAKE_CASE__       :			Tuple													=jax.nn.softmax(__lowercase   , axis=-1 ).cumsum(axis=-1 )
       SCREAMING_SNAKE_CASE__       :			Dict													=cumulative_probs < self.top_p

       # include the token that is higher than top_p as well
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=jnp.roll(__lowercase   , 1 )
       score_mask |= score_mask.at[:, 0].set(__lowercase )

       # min tokens to keep
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=score_mask.at[:, : self.min_tokens_to_keep].set(__lowercase )

       SCREAMING_SNAKE_CASE__       :			Dict													=jnp.where(__lowercase   , __lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=jax.lax.sort_key_val(__lowercase   , __lowercase )[-1]

       return next_scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[str]   , __lowercase		:							int   , __lowercase		:							float = -float('''Inf''' )   , __lowercase		:							int = 1 )		->  List[Any]:
       if not isinstance(__lowercase   , __lowercase ) or top_k <= 0:
             raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}" )

       SCREAMING_SNAKE_CASE__       :			str													=max(__lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=filter_value




 def __call__( self		:							List[Any]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=scores.shape
       SCREAMING_SNAKE_CASE__       :			str													=jnp.full(batch_size * vocab_size   , self.filter_value )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=min(self.top_k   , scores.shape[-1] )  # Safety check
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=lax.top_k(__lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=jnp.broadcast_to((jnp.arange(__lowercase ) * vocab_size)[:, None]   , (batch_size, topk) ).flatten()
       SCREAMING_SNAKE_CASE__       :			str													=topk_scores.flatten()
       SCREAMING_SNAKE_CASE__       :			int													=topk_indices.flatten() + shift

       SCREAMING_SNAKE_CASE__       :			str													=next_scores_flat.at[topk_indices_flat].set(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=next_scores_flat.reshape(__lowercase   , __lowercase )
       return next_scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Any   , __lowercase		:							int )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=bos_token_id




 def __call__( self		:							Tuple   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			Tuple													=jnp.full(scores.shape   , -float('''inf''' ) )

       SCREAMING_SNAKE_CASE__       :			str													=1 - jnp.bool_(cur_len - 1 )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where(__lowercase   , new_scores.at[:, self.bos_token_id].set(0 )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Union[str, Any]   , __lowercase		:							int   , __lowercase		:							int )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=max_length
       SCREAMING_SNAKE_CASE__       :			int													=eos_token_id




 def __call__( self		:							Any   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.full(scores.shape   , -float('''inf''' ) )

       SCREAMING_SNAKE_CASE__       :			str													=1 - jnp.bool_(cur_len - self.max_length + 1 )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where(__lowercase   , new_scores.at[:, self.eos_token_id].set(0 )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Union[str, Any]   , __lowercase		:							int   , __lowercase		:							int )		->  Tuple:
       if not isinstance(__lowercase   , __lowercase ) or min_length < 0:
             raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}" )

       if not isinstance(__lowercase   , __lowercase ) or eos_token_id < 0:
             raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}" )

       SCREAMING_SNAKE_CASE__       :			str													=min_length
       SCREAMING_SNAKE_CASE__       :			Tuple													=eos_token_id




 def __call__( self		:							Tuple   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       # create boolean flag to decide if min length penalty should be applied
       SCREAMING_SNAKE_CASE__       :			List[str]													=1 - jnp.clip(cur_len - self.min_length   , 0   , 1 )

       SCREAMING_SNAKE_CASE__       :			Tuple													=jnp.where(__lowercase   , scores.at[:, self.eos_token_id].set(-float('''inf''' ) )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							int   , __lowercase		:							Optional[Any]   , __lowercase		:							Tuple )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Dict													=list(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=begin_index




 def __call__( self		:							Optional[Any]   , __lowercase		:							Optional[Any]   , __lowercase		:							Dict   , __lowercase		:							int )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Any													=1 - jnp.bool_(cur_len - self.begin_index )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where(__lowercase   , scores.at[:, self.begin_suppress_tokens].set(-float('''inf''' ) )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Optional[Any]   , __lowercase		:							list )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			int													=list(__lowercase )




 def __call__( self		:							str   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=scores.at[..., self.suppress_tokens].set(-float('''inf''' ) )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[Any]   , __lowercase		:							List[str] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=dict(__lowercase )
       # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
       # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
       # Indexes without forced tokens will have a negative value.
       SCREAMING_SNAKE_CASE__       :			Any													=jnp.ones((max(force_token_map.keys() ) + 1)   , dtype=jnp.intaa ) * -1
       for index, token in force_token_map.items():
             if token is not None:
                   SCREAMING_SNAKE_CASE__       :			Tuple													=force_token_array.at[index].set(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=jnp.intaa(__lowercase )




 def __call__( self		:							Optional[Any]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       def _force_token(__lowercase		:							Union[str, Any] ):
             SCREAMING_SNAKE_CASE__       :			Dict													=scores.shape[0]
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.force_token_array[generation_idx]

             SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.ones_like(__lowercase   , dtype=scores.dtype ) * -float('''inf''' )
             SCREAMING_SNAKE_CASE__       :			Tuple													=jnp.zeros((batch_size, 1)   , dtype=scores.dtype )
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=lax.dynamic_update_slice(__lowercase   , __lowercase   , (0, current_token) )
             return new_scores

       SCREAMING_SNAKE_CASE__       :			Tuple													=lax.cond(
           cur_len >= self.force_token_array.shape[0]   , lambda: scores   , lambda: lax.cond(
               self.force_token_array[cur_len] >= 0   , lambda: _force_token(__lowercase )   , lambda: scores   , )   , )
       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[str]   , __lowercase		:							List[Any]   , __lowercase		:							List[str]   , __lowercase		:							Any )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=generate_config.eos_token_id
       SCREAMING_SNAKE_CASE__       :			List[Any]													=generate_config.no_timestamps_token_id
       SCREAMING_SNAKE_CASE__       :			List[Any]													=generate_config.no_timestamps_token_id + 1

       SCREAMING_SNAKE_CASE__       :			Dict													=decoder_input_length + 1

       if generate_config.is_multilingual:
             # room for language token and task token
             self.begin_index += 2
       if hasattr(__lowercase   , '''max_initial_timestamp_index''' ):
             SCREAMING_SNAKE_CASE__       :			int													=generate_config.max_initial_timestamp_index
       else:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=model_config.vocab_size
       if self.max_initial_timestamp_index is None:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_config.vocab_size




 def __call__( self		:							List[Any]   , __lowercase		:							Union[str, Any]   , __lowercase		:							Any   , __lowercase		:							str )		->  Optional[Any]:
       # suppress <|notimestamps|> which is handled by without_timestamps
       SCREAMING_SNAKE_CASE__       :			Any													=scores.at[:, self.no_timestamps_token_id].set(-float('''inf''' ) )

       def handle_pairs(__lowercase		:							Any   , __lowercase		:							Optional[int] ):
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where((cur_len - self.begin_index) >= 1   , __lowercase   , __lowercase )
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=jnp.where(
                 input_ids_k[cur_len - 1] >= self.timestamp_begin   , True and last_was_timestamp   , __lowercase   , )

             SCREAMING_SNAKE_CASE__       :			List[str]													=jnp.where((cur_len - self.begin_index) < 2   , __lowercase   , __lowercase )
             SCREAMING_SNAKE_CASE__       :			Any													=jnp.where(
                 input_ids_k[cur_len - 2] >= self.timestamp_begin   , __lowercase   , __lowercase   , )

             return jnp.where(
                 __lowercase   , jnp.where(
                     penultimate_was_timestamp > 0   , scores_k.at[self.timestamp_begin :].set(-float('''inf''' ) )   , scores_k.at[: self.eos_token_id].set(-float('''inf''' ) )   , )   , __lowercase   , )

       SCREAMING_SNAKE_CASE__       :			List[str]													=jax.vmap(__lowercase )(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=jnp.where(cur_len == self.begin_index   , __lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=jnp.where(
           self.max_initial_timestamp_index is not None   , True and apply_max_initial_timestamp   , __lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.timestamp_begin + self.max_initial_timestamp_index

       SCREAMING_SNAKE_CASE__       :			int													=jnp.where(
           __lowercase   , scores.at[:, last_allowed + 1 :].set(-float('''inf''' ) )   , __lowercase   , )

       # if sum of probability over timestamps is above any other token, sample timestamp
       SCREAMING_SNAKE_CASE__       :			Any													=jax.nn.log_softmax(__lowercase   , axis=-1 )

       def handle_cumulative_probs(__lowercase		:							Optional[int]   , __lowercase		:							str ):
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   , axis=-1 )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=jnp.max(logprobs_k[: self.timestamp_begin] )
             return jnp.where(
                 timestamp_logprob > max_text_token_logprob   , scores_k.at[: self.timestamp_begin].set(-float('''inf''' ) )   , __lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Any													=jax.vmap(__lowercase )(__lowercase   , __lowercase )

       return scores