
import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
			import bitsandbytes as bnb
			import torch
			import torch.nn as nn

			from ..pytorch_utils import ConvaD

if is_accelerate_available():
			from accelerate import init_empty_weights
			from accelerate.utils import find_tied_parameters

_A     =  logging.get_logger(__name__)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Optional[int]=None					,							__lowerCAmelCase						:						Optional[Any]=None       ):




			"""simple docstring"""


			if "." in tensor_name:
						lowerCAmelCase_      			=			tensor_name.split("."       )
						for split in splits[:-1]:
									lowerCAmelCase_      			=			getattr(__lowerCAmelCase					,							__lowerCAmelCase       )
									if new_module is None:
												raise ValueError(F"""{module} has no attribute {split}."""       )
									lowerCAmelCase_      			=			new_module
						lowerCAmelCase_      			=			splits[-1]

			if tensor_name not in module._parameters and tensor_name not in module._buffers:
						raise ValueError(F"""{module} does not have a parameter or a buffer named {tensor_name}."""       )
			lowerCAmelCase_      			=			tensor_name in module._buffers
			lowerCAmelCase_      			=			getattr(__lowerCAmelCase					,							__lowerCAmelCase       )

			if old_value.device == torch.device("meta"       ) and device not in ["meta", torch.device("meta"       )] and value is None:
						raise ValueError(F"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""       )

			lowerCAmelCase_      			=			False
			lowerCAmelCase_      			=			False
			if is_buffer or not is_bitsandbytes_available():
						lowerCAmelCase_      			=			False
						lowerCAmelCase_      			=			False
			else:
						lowerCAmelCase_      			=			hasattr(bnb.nn					,							"Params4bit"       ) and isinstance(module._parameters[tensor_name]					,							bnb.nn.Paramsabit       )
						lowerCAmelCase_      			=			isinstance(module._parameters[tensor_name]					,							bnb.nn.IntaParams       )

			if is_abit or is_abit:
						lowerCAmelCase_      			=			module._parameters[tensor_name]
						if param.device.type != "cuda":
									if value is None:
												lowerCAmelCase_      			=			old_value.to(__lowerCAmelCase       )
									elif isinstance(__lowerCAmelCase					,							torch.Tensor       ):
												lowerCAmelCase_      			=			value.to("cpu"       )
												if value.dtype == torch.inta:
															lowerCAmelCase_      			=			version.parse(importlib.metadata.version("bitsandbytes"       )       ) > version.parse(
															    "0.37.2"       )
															if not is_abit_serializable:
																		raise ValueError(
																		    "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
																		    "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."       )
									else:
												lowerCAmelCase_      			=			torch.tensor(__lowerCAmelCase					,							device="cpu"       )

									# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
									# Since weights are saved in the correct "orientation", we skip transposing when loading.
									if issubclass(module.source_cls					,							__lowerCAmelCase       ) and fpaa_statistics is None:
												lowerCAmelCase_      			=			new_value.T

									lowerCAmelCase_      			=			old_value.__dict__
									if is_abit:
												lowerCAmelCase_      			=			bnb.nn.IntaParams(__lowerCAmelCase					,							requires_grad=__lowerCAmelCase					,							**__lowerCAmelCase       ).to(__lowerCAmelCase       )
									elif is_abit:
												lowerCAmelCase_      			=			bnb.nn.Paramsabit(__lowerCAmelCase					,							requires_grad=__lowerCAmelCase					,							**__lowerCAmelCase       ).to(__lowerCAmelCase       )

									lowerCAmelCase_      			=			new_value
									if fpaa_statistics is not None:
												setattr(module.weight					,							"SCB"					,							fpaa_statistics.to(__lowerCAmelCase       )       )

			else:
						if value is None:
									lowerCAmelCase_      			=			old_value.to(__lowerCAmelCase       )
						elif isinstance(__lowerCAmelCase					,							torch.Tensor       ):
									lowerCAmelCase_      			=			value.to(__lowerCAmelCase       )
						else:
									lowerCAmelCase_      			=			torch.tensor(__lowerCAmelCase					,							device=__lowerCAmelCase       )

						if is_buffer:
									lowerCAmelCase_      			=			new_value
						else:
									lowerCAmelCase_      			=			nn.Parameter(__lowerCAmelCase					,							requires_grad=old_value.requires_grad       )
									lowerCAmelCase_      			=			new_value






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						int=None					,							__lowerCAmelCase						:						Tuple=None					,							__lowerCAmelCase						:						str=None					,							__lowerCAmelCase						:						str=False       ):




			"""simple docstring"""


			for name, module in model.named_children():
						if current_key_name is None:
									lowerCAmelCase_      			=			[]
						current_key_name.append(__lowerCAmelCase       )

						if (isinstance(__lowerCAmelCase					,							nn.Linear       ) or isinstance(__lowerCAmelCase					,							__lowerCAmelCase       )) and name not in modules_to_not_convert:
									# Check if the current key is not in the `modules_to_not_convert`
									if not any(key in ".".join(__lowerCAmelCase       ) for key in modules_to_not_convert       ):
												with init_empty_weights():
															if isinstance(__lowerCAmelCase					,							__lowerCAmelCase       ):
																		lowerCAmelCase_			,						lowerCAmelCase_      			=			module.weight.shape
															else:
																		lowerCAmelCase_      			=			module.in_features
																		lowerCAmelCase_      			=			module.out_features

															if quantization_config.quantization_method() == "llm_int8":
																		lowerCAmelCase_      			=			bnb.nn.LinearabitLt(
																		    __lowerCAmelCase					,							__lowerCAmelCase					,							module.bias is not None					,							has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight					,							threshold=quantization_config.llm_inta_threshold					,							)
																		lowerCAmelCase_      			=			True
															else:
																		if (
																		    quantization_config.llm_inta_skip_modules is not None
																		    and name in quantization_config.llm_inta_skip_modules
																		):
																					pass
																		else:
																					lowerCAmelCase_      			=			bnb.nn.Linearabit(
																					    __lowerCAmelCase					,							__lowerCAmelCase					,							module.bias is not None					,							quantization_config.bnb_abit_compute_dtype					,							compress_statistics=quantization_config.bnb_abit_use_double_quant					,							quant_type=quantization_config.bnb_abit_quant_type					,							)
																					lowerCAmelCase_      			=			True
                    # Store the module class in case we need to transpose the weight later
															lowerCAmelCase_      			=			type(__lowerCAmelCase       )
															# Force requires grad to False to avoid unexpected errors
															model._modules[name].requires_grad_(__lowerCAmelCase       )
						if len(list(module.children()       )       ) > 0:
									lowerCAmelCase_			,						lowerCAmelCase_      			=			_replace_with_bnb_linear(
									    __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							has_been_replaced=__lowerCAmelCase					,							)
						# Remove the last key for recursion
						current_key_name.pop(-1       )
			return model, has_been_replaced






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Optional[int]=None					,							__lowerCAmelCase						:						Optional[int]=None					,							__lowerCAmelCase						:						List[Any]=None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
			lowerCAmelCase_			,						lowerCAmelCase_      			=			_replace_with_bnb_linear(
			    __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )

			if not has_been_replaced:
						logger.warning(
						    "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
						    " Please double check your model architecture, or submit an issue on github if you think this is"
						    " a bug."       )

			return model






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




			"""simple docstring"""


			warnings.warn(
			    "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"					,							__lowerCAmelCase					,							)
			return replace_with_bnb_linear(*__lowerCAmelCase					,							**__lowerCAmelCase       )






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




			"""simple docstring"""


			warnings.warn(
			    "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"					,							__lowerCAmelCase					,							)
			return set_module_quantized_tensor_to_device(*__lowerCAmelCase					,							**__lowerCAmelCase       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			deepcopy(__lowerCAmelCase       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
			tied_model.tie_weights()

			lowerCAmelCase_      			=			find_tied_parameters(__lowerCAmelCase       )
			# For compatibility with Accelerate < 0.18
			if isinstance(__lowerCAmelCase					,							__lowerCAmelCase       ):
						lowerCAmelCase_      			=			sum(list(tied_params.values()       )					,							[]       ) + list(tied_params.keys()       )
			else:
						lowerCAmelCase_      			=			sum(__lowerCAmelCase					,							[]       )
			lowerCAmelCase_      			=			len(__lowerCAmelCase       ) > 0

			# Check if it is a base model
			lowerCAmelCase_      			=			not hasattr(__lowerCAmelCase					,							model.base_model_prefix       )

			# Ignore this for base models (BertModel, GPT2Model, etc.)
			if (not has_tied_params) and is_base_model:
						return []

			# otherwise they have an attached head
			lowerCAmelCase_      			=			list(model.named_children()       )
			lowerCAmelCase_      			=			[list_modules[-1][0]]

			# add last module together with tied weights
			lowerCAmelCase_      			=			set(__lowerCAmelCase       ) - set(__lowerCAmelCase       )
			lowerCAmelCase_      			=			list(set(__lowerCAmelCase       )       ) + list(__lowerCAmelCase       )

			# remove ".weight" from the keys
			lowerCAmelCase_      			=			[".weight", ".bias"]
			lowerCAmelCase_      			=			[]
			for name in list_untouched:
						for name_to_remove in names_to_remove:
									if name_to_remove in name:
												lowerCAmelCase_      			=			name.replace(__lowerCAmelCase					,							""       )
						filtered_module_names.append(__lowerCAmelCase       )

			return filtered_module_names





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 			_lowerCAmelCase      :




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





class 			_lowerCAmelCase      :




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





class 			_lowerCAmelCase      (			__a ):




				@add_start_docstrings(_UpperCamelCase  )
				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   jnp.ndarray:
							for processor in self:
										lowerCAmelCase_      			=			inspect.signature(processor.__call__  ).parameters
										if len(_UpperCamelCase  ) > 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(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )
										else:
													lowerCAmelCase_      			=			processor(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )
							return scores





class 			_lowerCAmelCase      (			__a ):




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

							lowerCAmelCase_      			=			temperature




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							lowerCAmelCase_      			=			scores / self.temperature
							return scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = -float("Inf"  )       ,       _UpperCamelCase = 1  )       ->   Union[str, Any]:
							if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) 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(_UpperCamelCase       ,       _UpperCamelCase  ) 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       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							lowerCAmelCase_			,						lowerCAmelCase_      			=			lax.top_k(_UpperCamelCase       ,       scores.shape[-1]  )

							lowerCAmelCase_      			=			jnp.full_like(_UpperCamelCase       ,       self.filter_value  )
							lowerCAmelCase_      			=			jax.nn.softmax(_UpperCamelCase       ,       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(_UpperCamelCase       ,       1  )
							score_mask |= score_mask.at[:, 0].set(_UpperCamelCase  )

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

							lowerCAmelCase_      			=			jnp.where(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			jax.lax.sort_key_val(_UpperCamelCase       ,       _UpperCamelCase  )[-1]

							return next_scores





class 			_lowerCAmelCase      (			__a ):




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

							lowerCAmelCase_      			=			max(_UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			filter_value




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							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(_UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			jnp.broadcast_to((jnp.arange(_UpperCamelCase  ) * 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(_UpperCamelCase  )
							lowerCAmelCase_      			=			next_scores_flat.reshape(_UpperCamelCase       ,       _UpperCamelCase  )
							return next_scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase  )       ->   Any:
							lowerCAmelCase_      			=			bos_token_id




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							lowerCAmelCase_      			=			jnp.full(scores.shape       ,       -float("inf"  )  )

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

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

							return scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[str]:
							lowerCAmelCase_      			=			max_length
							lowerCAmelCase_      			=			eos_token_id




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							lowerCAmelCase_      			=			jnp.full(scores.shape       ,       -float("inf"  )  )

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

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

							return scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
							if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) or min_length < 0:
										raise ValueError(f"""`min_length` has to be a positive integer, but is {min_length}"""  )

							if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) 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       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							# 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(_UpperCamelCase       ,       scores.at[:, self.eos_token_id].set(-float("inf"  )  )       ,       _UpperCamelCase  )

							return scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			list(_UpperCamelCase  )
							lowerCAmelCase_      			=			begin_index




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			1 - jnp.bool_(cur_len - self.begin_index  )

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

							return scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			list(_UpperCamelCase  )




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							lowerCAmelCase_      			=			scores.at[..., self.suppress_tokens].set(-float("inf"  )  )

							return scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase  )       ->   List[Any]:
							lowerCAmelCase_      			=			dict(_UpperCamelCase  )
							# 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(_UpperCamelCase  )
							lowerCAmelCase_      			=			jnp.intaa(_UpperCamelCase  )




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
							def _force_token(_UpperCamelCase  ):
										lowerCAmelCase_      			=			scores.shape[0]
										lowerCAmelCase_      			=			self.force_token_array[generation_idx]

										lowerCAmelCase_      			=			jnp.ones_like(_UpperCamelCase       ,       dtype=scores.dtype  ) * -float("inf"  )
										lowerCAmelCase_      			=			jnp.zeros((batch_size, 1)       ,       dtype=scores.dtype  )
										lowerCAmelCase_      			=			lax.dynamic_update_slice(_UpperCamelCase       ,       _UpperCamelCase       ,       (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(_UpperCamelCase  )       ,       lambda: scores       ,       )       ,       )
							return scores





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							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(_UpperCamelCase       ,       "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       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							# suppress <|notimestamps|> which is handled by without_timestamps
							lowerCAmelCase_      			=			scores.at[:, self.no_timestamps_token_id].set(-float("inf"  )  )

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

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

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

							lowerCAmelCase_      			=			jax.vmap(_UpperCamelCase  )(_UpperCamelCase       ,       _UpperCamelCase  )

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

							lowerCAmelCase_      			=			self.timestamp_begin + self.max_initial_timestamp_index

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

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

							def handle_cumulative_probs(_UpperCamelCase       ,       _UpperCamelCase  ):
										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"  )  )       ,       _UpperCamelCase       ,       )

							lowerCAmelCase_      			=			jax.vmap(_UpperCamelCase  )(_UpperCamelCase       ,       _UpperCamelCase  )

							return scores



