






"""simple docstring"""







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
			from .features import FeatureType


a ,		a ,		a			:    int				 =		False, False, False


@dataclass
class    __UpperCAmelCase:




						"""simple docstring"""




						__lowerCamelCase    	= None
						__lowerCamelCase    	= True
						__lowerCamelCase    	= True
						__lowerCamelCase    	= None
						# Automatically constructed
						__lowerCamelCase    	= "dict"
						__lowerCamelCase    	= pa.struct({"bytes": pa.binary(), "path": pa.string()}							)
						__lowerCamelCase    	= field(default="Audio"  ,  init=SCREAMING_SNAKE_CASE__  ,  repr=SCREAMING_SNAKE_CASE__							)




						def __call__(       self      ):
												'''simple docstring'''
												return self.pa_type




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												try:
																		import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
												except ImportError as err:
																		raise ImportError("To support encoding audio data, please install 'soundfile'."      ) from err
												if isinstance(snake_case__	,  snake_case__      ):
																		return {"bytes": None, "path": value}
												elif isinstance(snake_case__	,  snake_case__      ):
																		return {"bytes": value, "path": None}
												elif "array" in value:
																		# convert the audio array to wav bytes
																		lowercase__    :				Tuple=	BytesIO()
																		sf.write(snake_case__	,  value["array"]	,  value["sampling_rate"]	,  format="wav"      )
																		return {"bytes": buffer.getvalue(), "path": None}
												elif value.get("path"      ) is not None and os.path.isfile(value["path"]      ):
																		# we set "bytes": None to not duplicate the data if they're already available locally
																		if value["path"].endswith("pcm"      ):
																								# "PCM" only has raw audio bytes
																								if value.get("sampling_rate"      ) is None:
																														# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																														raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object"      )
																								if value.get("bytes"      ):
																														# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																														lowercase__    :				Optional[Any]=	np.frombuffer(value["bytes"]	,  dtype=np.intaa      ).astype(np.floataa      ) / 32767
																								else:
																														lowercase__    :				Any=	np.memmap(value["path"]	,  dtype="h"	,  mode="r"      ).astype(np.floataa      ) / 32767

																								lowercase__    :				List[str]=	BytesIO(bytes()      )
																								sf.write(snake_case__	,  snake_case__	,  value["sampling_rate"]	,  format="wav"      )
																								return {"bytes": buffer.getvalue(), "path": None}
																		else:
																								return {"bytes": None, "path": value.get("path"      )}
												elif value.get("bytes"      ) is not None or value.get("path"      ) is not None:
																		# store the audio bytes, and path is used to infer the audio format using the file extension
																		return {"bytes": value.get("bytes"      ), "path": value.get("path"      )}
												else:
																		raise ValueError(
																		    F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												if not self.decode:
																		raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."      )

												lowercase__,     lowercase__    :				int=	(value["path"], BytesIO(value["bytes"]      )) if value["bytes"] is not None else (value["path"], None)
												if path is None and file is None:
																		raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''      )

												try:
																		import librosa
																		import soundfile as sf
												except ImportError as err:
																		raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'."      ) from err

												lowercase__    :				Union[str, Any]=	xsplitext(snake_case__      )[1][1:].lower() if path is not None else None
												if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
																		raise RuntimeError(
																		    "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
																		    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "      )
												elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
																		raise RuntimeError(
																		    "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
																		    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "      )

												if file is None:
																		lowercase__    :				Union[str, Any]=	token_per_repo_id or {}
																		lowercase__    :				Union[str, Any]=	path.split("::"      )[-1]
																		try:
																								lowercase__    :				Optional[Any]=	string_to_dict(snake_case__	,  config.HUB_DATASETS_URL      )["repo_id"]
																								lowercase__    :				str=	token_per_repo_id[repo_id]
																		except (ValueError, KeyError):
																								lowercase__    :				int=	None

																		with xopen(snake_case__	,  "rb"	,  use_auth_token=snake_case__      ) as f:
																								lowercase__,     lowercase__    :				Optional[int]=	sf.read(snake_case__      )

												else:
																		lowercase__,     lowercase__    :				Tuple=	sf.read(snake_case__      )

												lowercase__    :				int=	array.T
												if self.mono:
																		lowercase__    :				str=	librosa.to_mono(snake_case__      )
												if self.sampling_rate and self.sampling_rate != sampling_rate:
																		lowercase__    :				Dict=	librosa.resample(snake_case__	,  orig_sr=snake_case__	,  target_sr=self.sampling_rate      )
																		lowercase__    :				int=	self.sampling_rate

												return {"path": path, "array": array, "sampling_rate": sampling_rate}




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												from .features import Value

												if self.decode:
																		raise ValueError("Cannot flatten a decoded Audio feature."      )
												return {
												    "bytes": Value("binary"      ),
												    "path": Value("string"      ),
												}




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												if pa.types.is_string(storage.type      ):
																		lowercase__    :				str=	pa.array([None] * len(snake_case__      )	,  type=pa.binary()      )
																		lowercase__    :				Optional[int]=	pa.StructArray.from_arrays([bytes_array, storage]	,  ["bytes", "path"]	,  mask=storage.is_null()      )
												elif pa.types.is_binary(storage.type      ):
																		lowercase__    :				List[Any]=	pa.array([None] * len(snake_case__      )	,  type=pa.string()      )
																		lowercase__    :				Any=	pa.StructArray.from_arrays([storage, path_array]	,  ["bytes", "path"]	,  mask=storage.is_null()      )
												elif pa.types.is_struct(storage.type      ) and storage.type.get_all_field_indices("array"      ):
																		lowercase__    :				int=	pa.array([Audio().encode_example(snake_case__      ) if x is not None else None for x in storage.to_pylist()]      )
												elif pa.types.is_struct(storage.type      ):
																		if storage.type.get_field_index("bytes"      ) >= 0:
																								lowercase__    :				Any=	storage.field("bytes"      )
																		else:
																								lowercase__    :				Optional[int]=	pa.array([None] * len(snake_case__      )	,  type=pa.binary()      )
																		if storage.type.get_field_index("path"      ) >= 0:
																								lowercase__    :				int=	storage.field("path"      )
																		else:
																								lowercase__    :				int=	pa.array([None] * len(snake_case__      )	,  type=pa.string()      )
																		lowercase__    :				Optional[Any]=	pa.StructArray.from_arrays([bytes_array, path_array]	,  ["bytes", "path"]	,  mask=storage.is_null()      )
												return array_cast(snake_case__	,  self.pa_type      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''

												@no_op_if_value_is_null
												def path_to_bytes(snake_case__      ):
																		with xopen(snake_case__	,  "rb"      ) as f:
																								lowercase__    :				Any=	f.read()
																		return bytes_

												lowercase__    :				Tuple=	pa.array(
												    [
												        (path_to_bytes(x["path"]      ) if x["bytes"] is None else x["bytes"]) if x is not None else None
												        for x in storage.to_pylist()
												    ]	,  type=pa.binary()	,  )
												lowercase__    :				Optional[Any]=	pa.array(
												    [os.path.basename(snake_case__      ) if path is not None else None for path in storage.field("path"      ).to_pylist()]	,  type=pa.string()	,  )
												lowercase__    :				Optional[int]=	pa.StructArray.from_arrays([bytes_array, path_array]	,  ["bytes", "path"]	,  mask=bytes_array.is_null()      )
												return array_cast(snake_case__	,  self.pa_type      )














"""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			:    Any				 =		get_logger(__name__)


a			:    Any				 =		r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""


class    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												for processor in self:
																		lowercase__    :				Optional[Any]=	inspect.signature(processor.__call__      ).parameters
																		if len(snake_case__      ) > 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.'''      )
																								lowercase__    :				Union[str, Any]=	processor(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      )
																		else:
																								lowercase__    :				Dict=	processor(snake_case__	,  snake_case__	,  snake_case__      )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or not (temperature > 0):
																		raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''      )

												lowercase__    :				Any=	temperature




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	scores / self.temperature
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) 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(snake_case__	,  snake_case__      ) 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}'''      )

												lowercase__    :				int=	top_p
												lowercase__    :				Optional[int]=	filter_value
												lowercase__    :				Tuple=	min_tokens_to_keep




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Dict=	lax.top_k(snake_case__	,  scores.shape[-1]      )

												lowercase__    :				Optional[int]=	jnp.full_like(snake_case__	,  self.filter_value      )
												lowercase__    :				Union[str, Any]=	jax.nn.softmax(snake_case__	,  axis=-1      ).cumsum(axis=-1      )
												lowercase__    :				str=	cumulative_probs < self.top_p

												# include the token that is higher than top_p as well
												lowercase__    :				str=	jnp.roll(snake_case__	,  1      )
												score_mask |= score_mask.at[:, 0].set(snake_case__      )

												# min tokens to keep
												lowercase__    :				Optional[int]=	score_mask.at[:, : self.min_tokens_to_keep].set(snake_case__      )

												lowercase__    :				str=	jnp.where(snake_case__	,  snake_case__	,  snake_case__      )
												lowercase__    :				str=	jax.lax.sort_key_val(snake_case__	,  snake_case__      )[-1]

												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or top_k <= 0:
																		raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''      )

												lowercase__    :				List[Any]=	max(snake_case__	,  snake_case__      )
												lowercase__    :				Dict=	filter_value




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Optional[Any]=	scores.shape
												lowercase__    :				int=	jnp.full(batch_size * vocab_size	,  self.filter_value      )

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

												lowercase__    :				Optional[Any]=	next_scores_flat.at[topk_indices_flat].set(snake_case__      )
												lowercase__    :				str=	next_scores_flat.reshape(snake_case__	,  snake_case__      )
												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	bos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				int=	1 - jnp.bool_(cur_len - 1      )

												lowercase__    :				int=	jnp.where(snake_case__	,  new_scores.at[:, self.bos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	max_length
												lowercase__    :				str=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				Any=	1 - jnp.bool_(cur_len - self.max_length + 1      )

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

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or min_length < 0:
																		raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''      )

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

												lowercase__    :				List[str]=	min_length
												lowercase__    :				Dict=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# create boolean flag to decide if min length penalty should be applied
												lowercase__    :				Tuple=	1 - jnp.clip(cur_len - self.min_length	,  0	,  1      )

												lowercase__    :				Dict=	jnp.where(snake_case__	,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	list(snake_case__      )
												lowercase__    :				List[Any]=	begin_index




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	1 - jnp.bool_(cur_len - self.begin_index      )

												lowercase__    :				str=	jnp.where(snake_case__	,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	list(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	dict(snake_case__      )
												# 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.
												lowercase__    :				List[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:
																								lowercase__    :				List[Any]=	force_token_array.at[index].set(snake_case__      )
												lowercase__    :				int=	jnp.intaa(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												def _force_token(snake_case__      ):
																		lowercase__    :				Dict=	scores.shape[0]
																		lowercase__    :				Any=	self.force_token_array[generation_idx]

																		lowercase__    :				List[Any]=	jnp.ones_like(snake_case__	,  dtype=scores.dtype      ) * -float("inf"      )
																		lowercase__    :				List[Any]=	jnp.zeros((batch_size, 1)	,  dtype=scores.dtype      )
																		lowercase__    :				List[str]=	lax.dynamic_update_slice(snake_case__	,  snake_case__	,  (0, current_token)      )
																		return new_scores

												lowercase__    :				Dict=	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(snake_case__      )	,  lambda: scores	,  )	,  )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	generate_config.eos_token_id
												lowercase__    :				Optional[int]=	generate_config.no_timestamps_token_id
												lowercase__    :				Dict=	generate_config.no_timestamps_token_id + 1

												lowercase__    :				List[Any]=	decoder_input_length + 1

												if generate_config.is_multilingual:
																		# room for language token and task token
																		self.begin_index += 2
												if hasattr(snake_case__	,  "max_initial_timestamp_index"      ):
																		lowercase__    :				int=	generate_config.max_initial_timestamp_index
												else:
																		lowercase__    :				Dict=	model_config.vocab_size
												if self.max_initial_timestamp_index is None:
																		lowercase__    :				str=	model_config.vocab_size




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# suppress <|notimestamps|> which is handled by without_timestamps
												lowercase__    :				int=	scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

												def handle_pairs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Union[str, Any]=	jnp.where((cur_len - self.begin_index) >= 1	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Tuple=	jnp.where(
																		    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  snake_case__	,  )

																		lowercase__    :				int=	jnp.where((cur_len - self.begin_index) < 2	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Optional[int]=	jnp.where(
																		    input_ids_k[cur_len - 2] >= self.timestamp_begin	,  snake_case__	,  snake_case__	,  )

																		return jnp.where(
																		    snake_case__	,  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"      )      )	,  )	,  snake_case__	,  )

												lowercase__    :				List[str]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												lowercase__    :				str=	jnp.where(cur_len == self.begin_index	,  snake_case__	,  snake_case__      )
												lowercase__    :				List[Any]=	jnp.where(
												    self.max_initial_timestamp_index is not None	,  True and apply_max_initial_timestamp	,  snake_case__	,  )

												lowercase__    :				Any=	self.timestamp_begin + self.max_initial_timestamp_index

												lowercase__    :				str=	jnp.where(
												    snake_case__	,  scores.at[:, last_allowed + 1 :].set(-float("inf"      )      )	,  snake_case__	,  )

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

												def handle_cumulative_probs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Dict=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1      )
																		lowercase__    :				Union[str, 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"      )      )	,  snake_case__	,  )

												lowercase__    :				Optional[int]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												return scores






