






"""simple docstring"""







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
a			:    str				 =		logging.get_logger()


@dataclass
class    __UpperCAmelCase:




						"""simple docstring"""




						__lowerCamelCase    	= 42
						__lowerCamelCase    	= field(default_factory=lowerCamelCase_							)
						__lowerCamelCase    	= field(default_factory=lowerCamelCase_							)




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	len(list(m.modules()      )      ) == 1 or isinstance(lowerCAmelCase__	,  nn.Convad      ) or isinstance(lowerCAmelCase__	,  nn.BatchNormad      )
												if has_not_submodules:
																		self.traced.append(lowerCAmelCase__      )




						def __call__(       self	,  snake_case__      ):
												'''simple docstring'''
												for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook      )      )
												self.module(lowerCAmelCase__      )
												[x.remove() for x in self.handles]
												return self




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# check the len of the state_dict keys to see if we have learnable params
												return list(filter(lambda snake_case__      : len(list(x.state_dict().keys()      )      ) > 0	,  self.traced      )      )


@dataclass
class    __UpperCAmelCase:




						"""simple docstring"""




						__lowerCamelCase    	= 42
						__lowerCamelCase    	= 42
						__lowerCamelCase    	= 0
						__lowerCamelCase    	= field(default_factory=lowerCamelCase_							)
						__lowerCamelCase    	= field(default_factory=lowerCamelCase_							)




						def __call__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	Tracker(self.dest      )(lowerCAmelCase__      ).parametrized
												lowercase__    :				Union[str, Any]=	Tracker(self.src      )(lowerCAmelCase__      ).parametrized

												lowercase__    :				Optional[int]=	list(filter(lambda snake_case__      : type(lowerCAmelCase__      ) not in self.src_skip	,  lowerCAmelCase__      )      )
												lowercase__    :				List[str]=	list(filter(lambda snake_case__      : type(lowerCAmelCase__      ) not in self.dest_skip	,  lowerCAmelCase__      )      )

												if len(lowerCAmelCase__      ) != len(lowerCAmelCase__      ):
																		raise Exception(
																		    F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__      )} operations while'''
																		    F''' destination module has {len(lowerCAmelCase__      )}.'''      )

												for dest_m, src_m in zip(lowerCAmelCase__	,  lowerCAmelCase__      ):
																		dest_m.load_state_dict(src_m.state_dict()      )
																		if self.verbose == 1:
																								print(F'''Transfered from={src_m} to={dest_m}'''      )





def 						lowercase__(A ,		A ,		A ,		A = True						)    ->List[Any]:




						"""simple docstring"""



						print(f'''Converting {name}...'''						)
						with torch.no_grad():
												lowercase__    :				Optional[Any]=	timm.create_model(SCREAMING_SNAKE_CASE_ ,		pretrained=SCREAMING_SNAKE_CASE_						).eval()
												lowercase__    :				Dict=	ResNetForImageClassification(SCREAMING_SNAKE_CASE_						).eval()
												lowercase__    :				int=	ModuleTransfer(src=SCREAMING_SNAKE_CASE_ ,		dest=SCREAMING_SNAKE_CASE_						)
												lowercase__    :				List[str]=	torch.randn((1, 3, 224, 224)						)
												module_transfer(SCREAMING_SNAKE_CASE_						)

						assert torch.allclose(from_model(SCREAMING_SNAKE_CASE_						) ,		our_model(SCREAMING_SNAKE_CASE_						).logits						), "The model logits don't match the original one."

						lowercase__    :				str=	f'''resnet{'-'.join(name.split('resnet'						)						)}'''
						print(SCREAMING_SNAKE_CASE_						)

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name ,		commit_message="Add model" ,		use_temp_dir=SCREAMING_SNAKE_CASE_ ,		)

												# we can use the convnext one
												lowercase__    :				int=	AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name ,		commit_message="Add image processor" ,		use_temp_dir=SCREAMING_SNAKE_CASE_ ,		)

												print(f'''Pushed {checkpoint_name}'''						)





def 						lowercase__(A ,		A = None ,		A = True						)    ->List[str]:




						"""simple docstring"""



						lowercase__    :				Tuple=	"imagenet-1k-id2label.json"
						lowercase__    :				Union[str, Any]=	1_000
						lowercase__    :				Optional[Any]=	(1, num_labels)

						lowercase__    :				Any=	"huggingface/label-files"
						lowercase__    :				Optional[Any]=	num_labels
						lowercase__    :				Optional[int]=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		repo_type="dataset"						) ,		"r"						)						)
						lowercase__    :				Union[str, Any]=	{int(SCREAMING_SNAKE_CASE_						): v for k, v in idalabel.items()}

						lowercase__    :				List[str]=	idalabel
						lowercase__    :				Optional[int]=	{v: k for k, v in idalabel.items()}

						lowercase__    :				Optional[int]=	partial(SCREAMING_SNAKE_CASE_ ,		num_labels=SCREAMING_SNAKE_CASE_ ,		idalabel=SCREAMING_SNAKE_CASE_ ,		labelaid=SCREAMING_SNAKE_CASE_						)

						lowercase__    :				Dict=	{
						    "resnet18": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2] ,		hidden_sizes=[64, 128, 256, 512] ,		layer_type="basic"						),
						    "resnet26": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2] ,		hidden_sizes=[256, 512, 1_024, 2_048] ,		layer_type="bottleneck"						),
						    "resnet34": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3] ,		hidden_sizes=[64, 128, 256, 512] ,		layer_type="basic"						),
						    "resnet50": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3] ,		hidden_sizes=[256, 512, 1_024, 2_048] ,		layer_type="bottleneck"						),
						    "resnet101": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3] ,		hidden_sizes=[256, 512, 1_024, 2_048] ,		layer_type="bottleneck"						),
						    "resnet152": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3] ,		hidden_sizes=[256, 512, 1_024, 2_048] ,		layer_type="bottleneck"						),
						}

						if model_name:
												convert_weight_and_push(SCREAMING_SNAKE_CASE_ ,		names_to_config[model_name] ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_						)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_						)
						return config, expected_shape


if __name__ == "__main__":
			a			:    Tuple				 =		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default=None,
			    type=str,
			    help=(
			        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
			        """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default=None,
			    type=Path,
			    required=True,
			    help="""Path to the output PyTorch model directory.""",
			)
			parser.add_argument(
			    """--push_to_hub""",
			    default=True,
			    type=bool,
			    required=False,
			    help="""If True, push model and image processor to the hub.""",
			)

			a			:    int				 =		parser.parse_args()
			a			:    List[str]				 =		args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)














"""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






