






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
							import torch


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__				     =			{'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE__				     =			{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

SCREAMING_SNAKE_CASE__				     =			{
    '''AI-Sweden/gpt-sw3-126m''': 2048,
    '''AI-Sweden/gpt-sw3-350m''': 2048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2048,
    '''AI-Sweden/gpt-sw3-20b''': 2048,
}






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Any							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       Tuple							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       Any							=							['''input_ids''', '''attention_mask''']

		def __init__(					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=False    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=False    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=False    ,       SCREAMING_SNAKE_CASE__		:    str=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=None    ,       SCREAMING_SNAKE_CASE__		:    List[str]=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Dict[str, Any]] = None    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ,       ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     {} if sp_model_kwargs is None else sp_model_kwargs

					__a						:  List[Any]									=     kwargs.get('name_or_path'    )
					if name_or_path is None:
								logger.warning(
								    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
								    ' you are testing the model, this can safely be ignored'    )
								__a						:  str									=     'None'

					# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
					__a						:  List[Any]									=     '<|endoftext|>' if eos_token is None else eos_token
					__a						:  List[str]									=     '<unk>' if unk_token is None else unk_token
					if "gpt-sw3-7b" in name_or_path:
								__a						:  str									=     unk_token if pad_token is None else pad_token
								__a						:  List[Any]									=     eos_token if bos_token is None else bos_token
					else:
								__a						:  Optional[Any]									=     '<pad>' if pad_token is None else pad_token
								__a						:  Tuple									=     '<s>' if bos_token is None else bos_token

					super().__init__(
					    do_lower_case=SCREAMING_SNAKE_CASE__    ,       remove_space=SCREAMING_SNAKE_CASE__    ,       keep_accents=SCREAMING_SNAKE_CASE__    ,       bos_token=SCREAMING_SNAKE_CASE__    ,       eos_token=SCREAMING_SNAKE_CASE__    ,       unk_token=SCREAMING_SNAKE_CASE__    ,       pad_token=SCREAMING_SNAKE_CASE__    ,       sp_model_kwargs=self.sp_model_kwargs    ,       **SCREAMING_SNAKE_CASE__    ,       )

					__a						:  str									=     do_lower_case
					__a						:  Dict									=     remove_space
					__a						:  Any									=     keep_accents
					__a						:  Optional[Any]									=     vocab_file

					__a						:  List[Any]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(SCREAMING_SNAKE_CASE__    )

					# Used for whitespace normalization in input texts
					# fmt : off
					__a						:  int									=     {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
					# fmt : on

					# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
					__a						:  List[Any]									=     re.compile(
					    f'''[{"".join(map(SCREAMING_SNAKE_CASE__    ,       list(range(0    ,       9    )    ) + list(range(1_1    ,       3_2    )    ) + list(range(1_2_7    ,       1_6_0    )    ) + [1_6_0, 1_7_3, 8_2_0_3]    )    )}]'''    )

		def __getstate__(					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  List[Any]									=     self.__dict__.copy()
					__a						:  Union[str, Any]									=     None
					return state

		def __setstate__(					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     d

					# for backward compatibility
					if not hasattr(self    ,       'sp_model_kwargs'    ):
								__a						:  Dict									=     {}

					__a						:  Union[str, Any]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )

		@property
		# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					return len(self.sp_model    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     self.non_printing_characters_re.sub(''    ,       SCREAMING_SNAKE_CASE__    )

					# Normalize whitespaces
					__a						:  int									=     ''.join([char if char not in self.whitespaces else ' ' for char in text]    )

					# NFC Unicode normalization
					__a						:  Union[str, Any]									=     unicodedata.normalize('NFC'    ,       SCREAMING_SNAKE_CASE__    )
					return text

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       **SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Tuple									=     self.preprocess_text(SCREAMING_SNAKE_CASE__    )
					return self.sp_model.encode(SCREAMING_SNAKE_CASE__    ,       out_type=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__    )

		@staticmethod
		def    __lowerCAmelCase  (					SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					return out_string

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					__a						:  Any									=     []
					__a						:  List[Any]									=     ''
					__a						:  Optional[int]									=     False
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
											if not prev_is_special:
														out_string += " "

											out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__    ) + token
											__a						:  int									=     True
											__a						:  Tuple									=     []
								else:
											current_sub_tokens.append(SCREAMING_SNAKE_CASE__    )
											__a						:  str									=     False
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__    )

					return out_string

		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ):



					'''simple docstring'''



					if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
								return
					__a						:  Tuple									=     os.path.join(
					    SCREAMING_SNAKE_CASE__    ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

					if os.path.abspath(self.vocab_file    ) != os.path.abspath(SCREAMING_SNAKE_CASE__    ) and os.path.isfile(self.vocab_file    ):
								copyfile(self.vocab_file    ,       SCREAMING_SNAKE_CASE__    )
					elif not os.path.isfile(self.vocab_file    ):
								with open(SCREAMING_SNAKE_CASE__    ,       'wb'    ) as fi:
											__a						:  Any									=     self.sp_model.serialized_model_proto()
											fi.write(SCREAMING_SNAKE_CASE__    )

					return (out_vocab_file,)

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, List[str]]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, bool] = False    ):



					'''simple docstring'''



					if isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ):
								__a						:  Tuple									=     self.preprocess_text(SCREAMING_SNAKE_CASE__    )
								__a						:  Dict									=     self.sp_model.encode(SCREAMING_SNAKE_CASE__    )
					else:
								__a						:  Optional[Any]									=     [self.preprocess_text(SCREAMING_SNAKE_CASE__    ) for t in text]
								__a						:  List[Any]									=     self.sp_model.encode(SCREAMING_SNAKE_CASE__    )

					if return_tensors is True or return_tensors == "pt":
								__a						:  Tuple									=     torch.tensor(SCREAMING_SNAKE_CASE__    )

					return token_ids

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Union[int, List[int]]    ):



					'''simple docstring'''



					return self.sp_model.decode(SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    "Conversation"    ):



					'''simple docstring'''



					__a						:  int									=     [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
					__a						:  int									=     (
					    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(SCREAMING_SNAKE_CASE__    ) + f'''{self.bos_token}Bot:'''
					)
					return self.encode(text=SCREAMING_SNAKE_CASE__    )









"""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	:  Dict            =     get_logger(__name__)


A	:  Dict            =     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     _UpperCamelCase    :
							'''simple docstring'''







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







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







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a       ,  __a       ,  **__a      ):
										for processor in self:
													__lowerCAmelCase							       =     inspect.signature(processor.__call__      ).parameters
													if len(__a      ) > 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."      )
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a       ,  **__a      )
													else:
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a      )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or not (temperature > 0):
													raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}"      )

										__lowerCAmelCase							       =     temperature



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores / self.temperature
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) 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(__a       ,  __a      ) 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}"      )

										__lowerCAmelCase							       =     top_p
										__lowerCAmelCase							       =     filter_value
										__lowerCAmelCase							       =     min_tokens_to_keep



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  scores.shape[-1]      )

										__lowerCAmelCase							       =     jnp.full_like(__a       ,  self.filter_value      )
										__lowerCAmelCase							       =     jax.nn.softmax(__a       ,  axis=-1      ).cumsum(axis=-1      )
										__lowerCAmelCase							       =     cumulative_probs < self.top_p

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

										# min tokens to keep
										__lowerCAmelCase							       =     score_mask.at[:, : self.min_tokens_to_keep].set(__a      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jax.lax.sort_key_val(__a       ,  __a      )[-1]

										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

										__lowerCAmelCase							       =     max(__a       ,  __a      )
										__lowerCAmelCase							       =     filter_value



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     scores.shape
										__lowerCAmelCase							       =     jnp.full(batch_size * vocab_size       ,  self.filter_value      )

										__lowerCAmelCase							       =     min(self.top_k       ,  scores.shape[-1]      )  # Safety check
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  __a      )
										__lowerCAmelCase							       =     jnp.broadcast_to((jnp.arange(__a      ) * vocab_size)[:, None]       ,  (batch_size, topk)      ).flatten()
										__lowerCAmelCase							       =     topk_scores.flatten()
										__lowerCAmelCase							       =     topk_indices.flatten() + shift

										__lowerCAmelCase							       =     next_scores_flat.at[topk_indices_flat].set(__a      )
										__lowerCAmelCase							       =     next_scores_flat.reshape(__a       ,  __a      )
										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     bos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.bos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     max_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.max_length + 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.eos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or min_length < 0:
													raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}"      )

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

										__lowerCAmelCase							       =     min_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# create boolean flag to decide if min length penalty should be applied
										__lowerCAmelCase							       =     1 - jnp.clip(cur_len - self.min_length       ,  0       ,  1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )
										__lowerCAmelCase							       =     begin_index



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.begin_index      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     dict(__a      )
										# 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.
										__lowerCAmelCase							       =     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:
																__lowerCAmelCase							       =     force_token_array.at[index].set(__a      )
										__lowerCAmelCase							       =     jnp.intaa(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										def _force_token(__a      ):
													__lowerCAmelCase							       =     scores.shape[0]
													__lowerCAmelCase							       =     self.force_token_array[generation_idx]

													__lowerCAmelCase							       =     jnp.ones_like(__a       ,  dtype=scores.dtype      ) * -float("inf"      )
													__lowerCAmelCase							       =     jnp.zeros((batch_size, 1)       ,  dtype=scores.dtype      )
													__lowerCAmelCase							       =     lax.dynamic_update_slice(__a       ,  __a       ,  (0, current_token)      )
													return new_scores

										__lowerCAmelCase							       =     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(__a      )       ,  lambda: scores       ,  )       ,  )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     generate_config.eos_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id + 1

										__lowerCAmelCase							       =     decoder_input_length + 1

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



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										__lowerCAmelCase							       =     scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

										def handle_pairs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) >= 1       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 1] >= self.timestamp_begin       ,  True and last_was_timestamp       ,  __a       ,  )

													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) < 2       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 2] >= self.timestamp_begin       ,  __a       ,  __a       ,  )

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

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										__lowerCAmelCase							       =     jnp.where(cur_len == self.begin_index       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jnp.where(
										    self.max_initial_timestamp_index is not None       ,  True and apply_max_initial_timestamp       ,  __a       ,  )

										__lowerCAmelCase							       =     self.timestamp_begin + self.max_initial_timestamp_index

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

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

										def handle_cumulative_probs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,  axis=-1      )
													__lowerCAmelCase							       =     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"      )      )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										return scores




