





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_A					:				Any				=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")



@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		Optional[str]				=	field(
        default="""cifar10"""			,     metadata={"""help""": """Name of a dataset from the datasets package"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The column name of the images in the files."""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """A folder containing the training data."""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """A folder containing the validation data."""}    )
    lowerCamelCase__    :		Optional[float]				=	field(
        default=0.15			,     metadata={"""help""": """Percent to split off of train for validation."""}    )
    lowerCamelCase__    :		Optional[int]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }			,     )
    lowerCamelCase__    :		Optional[int]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }			,     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{}
        if self.train_dir is not None:
            SCREAMING_SNAKE_CASE__											=			self.train_dir
        if self.validation_dir is not None:
            SCREAMING_SNAKE_CASE__											=			self.validation_dir
        SCREAMING_SNAKE_CASE__											=			data_files if data_files else None





@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		str				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }			,     )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }			,     )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
    lowerCamelCase__    :		str				=	field(
        default="""main"""			,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}			,     )
    lowerCamelCase__    :		str				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Name or path of preprocessor config."""}    )
    lowerCamelCase__    :		bool				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }			,     )
    lowerCamelCase__    :		float				=	field(
        default=0.75			,     metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}    )
    lowerCamelCase__    :		bool				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )





@dataclass
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		float				=	field(
        default=1E-3			,     metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}    )





def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			torch.stack([example['''pixel_values'''] for example in examples]	)
    return {"pixel_values": pixel_values}


def 							__snake_case							(    )				->						Dict:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    SCREAMING_SNAKE_CASE__											=			HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)	)
    if len(sys.argv	) == 2 and sys.argv[1].endswith('''.json'''	):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
    else:
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry('''run_mae''' ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,							datefmt='''%m/%d/%Y %H:%M:%S''' ,							handlers=[logging.StreamHandler(sys.stdout	)] ,							)

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    SCREAMING_SNAKE_CASE__											=			training_args.get_process_log_level()
    logger.setLevel(lowerCAmelCase_	)
    transformers.utils.logging.set_verbosity(lowerCAmelCase_	)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
        + f'''distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}'''	)
    logger.info(f'''Training/evaluation parameters {training_args}'''	)

    # Detecting last checkpoint.
    SCREAMING_SNAKE_CASE__											=			None
    if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
        SCREAMING_SNAKE_CASE__											=			get_last_checkpoint(training_args.output_dir	)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                '''Use --overwrite_output_dir to overcome.'''	)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''	)

    # Initialize our dataset.
    SCREAMING_SNAKE_CASE__											=			load_dataset(
        data_args.dataset_name ,							data_args.dataset_config_name ,							data_files=data_args.data_files ,							cache_dir=model_args.cache_dir ,							use_auth_token=True if model_args.use_auth_token else None ,							)

    # If we don't have a validation split, split off a percentage of train as validation.
    SCREAMING_SNAKE_CASE__											=			None if '''validation''' in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split ,							lowerCAmelCase_	) and data_args.train_val_split > 0.0:
        SCREAMING_SNAKE_CASE__											=			ds['''train'''].train_test_split(data_args.train_val_split	)
        SCREAMING_SNAKE_CASE__											=			split['''train''']
        SCREAMING_SNAKE_CASE__											=			split['''test''']

    # Load pretrained model and image processor
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    SCREAMING_SNAKE_CASE__											=			{
        '''cache_dir''': model_args.cache_dir,
        '''revision''': model_args.model_revision,
        '''use_auth_token''': True if model_args.use_auth_token else None,
    }
    if model_args.config_name:
        SCREAMING_SNAKE_CASE__											=			ViTMAEConfig.from_pretrained(model_args.config_name ,							**lowerCAmelCase_	)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE__											=			ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,							**lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			ViTMAEConfig()
        logger.warning('''You are instantiating a new config instance from scratch.'''	)
        if model_args.config_overrides is not None:
            logger.info(f'''Overriding config: {model_args.config_overrides}'''	)
            config.update_from_string(model_args.config_overrides	)
            logger.info(f'''New config: {config}'''	)

    # adapt config
    config.update(
        {
            '''mask_ratio''': model_args.mask_ratio,
            '''norm_pix_loss''': model_args.norm_pix_loss,
        }	)

    # create image processor
    if model_args.image_processor_name:
        SCREAMING_SNAKE_CASE__											=			ViTImageProcessor.from_pretrained(model_args.image_processor_name ,							**lowerCAmelCase_	)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE__											=			ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,							**lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			ViTImageProcessor()

    # create model
    if model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE__											=			ViTMAEForPreTraining.from_pretrained(
            model_args.model_name_or_path ,							from_tf=bool('''.ckpt''' in model_args.model_name_or_path	) ,							config=lowerCAmelCase_ ,							cache_dir=model_args.cache_dir ,							revision=model_args.model_revision ,							use_auth_token=True if model_args.use_auth_token else None ,							)
    else:
        logger.info('''Training new model from scratch'''	)
        SCREAMING_SNAKE_CASE__											=			ViTMAEForPreTraining(lowerCAmelCase_	)

    if training_args.do_train:
        SCREAMING_SNAKE_CASE__											=			ds['''train'''].column_names
    else:
        SCREAMING_SNAKE_CASE__											=			ds['''validation'''].column_names

    if data_args.image_column_name is not None:
        SCREAMING_SNAKE_CASE__											=			data_args.image_column_name
    elif "image" in column_names:
        SCREAMING_SNAKE_CASE__											=			'''image'''
    elif "img" in column_names:
        SCREAMING_SNAKE_CASE__											=			'''img'''
    else:
        SCREAMING_SNAKE_CASE__											=			column_names[0]

    # transformations as done in original MAE paper
    # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
    if "shortest_edge" in image_processor.size:
        SCREAMING_SNAKE_CASE__											=			image_processor.size['''shortest_edge''']
    else:
        SCREAMING_SNAKE_CASE__											=			(image_processor.size['''height'''], image_processor.size['''width'''])
    SCREAMING_SNAKE_CASE__											=			Compose(
        [
            Lambda(lambda lowerCAmelCase_	: img.convert('''RGB'''	) if img.mode != "RGB" else img	),
            RandomResizedCrop(lowerCAmelCase_ ,							scale=(0.2, 1.0) ,							interpolation=InterpolationMode.BICUBIC	),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean ,							std=image_processor.image_std	),
        ]	)

    def preprocess_images(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			[transforms(lowerCAmelCase_	) for image in examples[image_column_name]]
        return examples

    if training_args.do_train:
        if "train" not in ds:
            raise ValueError('''--do_train requires a train dataset'''	)
        if data_args.max_train_samples is not None:
            SCREAMING_SNAKE_CASE__											=			ds['''train'''].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
        # Set the training transforms
        ds["train"].set_transform(lowerCAmelCase_	)

    if training_args.do_eval:
        if "validation" not in ds:
            raise ValueError('''--do_eval requires a validation dataset'''	)
        if data_args.max_eval_samples is not None:
            SCREAMING_SNAKE_CASE__											=			(
                ds['''validation'''].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
            )
        # Set the validation transforms
        ds["validation"].set_transform(lowerCAmelCase_	)

    # Compute absolute learning rate
    SCREAMING_SNAKE_CASE__											=			(
        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
    )
    if training_args.base_learning_rate is not None:
        SCREAMING_SNAKE_CASE__											=			training_args.base_learning_rate * total_train_batch_size / 2_5_6

    # Initialize our trainer
    SCREAMING_SNAKE_CASE__											=			Trainer(
        model=lowerCAmelCase_ ,							args=lowerCAmelCase_ ,							train_dataset=ds['''train'''] if training_args.do_train else None ,							eval_dataset=ds['''validation'''] if training_args.do_eval else None ,							tokenizer=lowerCAmelCase_ ,							data_collator=lowerCAmelCase_ ,							)

    # Training
    if training_args.do_train:
        SCREAMING_SNAKE_CASE__											=			None
        if training_args.resume_from_checkpoint is not None:
            SCREAMING_SNAKE_CASE__											=			training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            SCREAMING_SNAKE_CASE__											=			last_checkpoint
        SCREAMING_SNAKE_CASE__											=			trainer.train(resume_from_checkpoint=lowerCAmelCase_	)
        trainer.save_model()
        trainer.log_metrics('''train''' ,							train_result.metrics	)
        trainer.save_metrics('''train''' ,							train_result.metrics	)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        SCREAMING_SNAKE_CASE__											=			trainer.evaluate()
        trainer.log_metrics('''eval''' ,							lowerCAmelCase_	)
        trainer.save_metrics('''eval''' ,							lowerCAmelCase_	)

    # Write model card and (optionally) push to hub
    SCREAMING_SNAKE_CASE__											=			{
        '''tasks''': '''masked-auto-encoding''',
        '''dataset''': data_args.dataset_name,
        '''tags''': ['''masked-auto-encoding'''],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**lowerCAmelCase_	)
    else:
        trainer.create_model_card(**lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()








"""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


lowerCAmelCase__    						=			get_logger(__name__)


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







class _lowerCAmelCase						:







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







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    temperature







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







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = -float('Inf'							)   ,				lowerCAmelCase_ = 1							)		->							int:
										if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) 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(lowerCAmelCase_   ,				lowerCAmelCase_							) or (min_tokens_to_keep < 1):
														raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    top_p
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    filter_value
										_SCREAMING_SNAKE_CASE   :		Any       		=    min_tokens_to_keep







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    lax.top_k(lowerCAmelCase_   ,				scores.shape[-1]							)

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

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

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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.where(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    jax.lax.sort_key_val(lowerCAmelCase_   ,				lowerCAmelCase_							)[-1]

										return next_scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    max(lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    filter_value







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    scores.shape
										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.full(batch_size * vocab_size   ,				self.filter_value							)

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

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    next_scores_flat.at[topk_indices_flat].set(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    next_scores_flat.reshape(lowerCAmelCase_   ,				lowerCAmelCase_							)
										return next_scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    bos_token_id







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

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

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

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    max_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    eos_token_id







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.full(scores.shape   ,				-float('inf'							)							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    1 - jnp.bool_(cur_len - self.max_length + 1							)

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

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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

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

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    min_length
										_SCREAMING_SNAKE_CASE   :		str       		=    eos_token_id







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

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jnp.where(lowerCAmelCase_   ,				scores.at[:, self.eos_token_id].set(-float('inf'							)							)   ,				lowerCAmelCase_							)

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    list(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    begin_index







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Any       		=    1 - jnp.bool_(cur_len - self.begin_index							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.where(lowerCAmelCase_   ,				scores.at[:, self.begin_suppress_tokens].set(-float('inf'							)							)   ,				lowerCAmelCase_							)

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    scores.at[..., self.suppress_tokens].set(-float('inf'							)							)

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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







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

														_SCREAMING_SNAKE_CASE   :		Dict       		=    jnp.ones_like(lowerCAmelCase_   ,				dtype=scores.dtype							) * -float('inf'							)
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jnp.zeros((batch_size, 1)   ,				dtype=scores.dtype							)
														_SCREAMING_SNAKE_CASE   :		str       		=    lax.dynamic_update_slice(lowerCAmelCase_   ,				lowerCAmelCase_   ,				(0, current_token)							)
														return new_scores

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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(lowerCAmelCase_							)   ,				lambda: scores   ,				)   ,				)
										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    generate_config.eos_token_id
										_SCREAMING_SNAKE_CASE   :		str       		=    generate_config.no_timestamps_token_id
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    generate_config.no_timestamps_token_id + 1

										_SCREAMING_SNAKE_CASE   :		str       		=    decoder_input_length + 1

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







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

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

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

														return jnp.where(
														    lowerCAmelCase_   ,				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'							)							)   ,				)   ,				lowerCAmelCase_   ,				)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.vmap(lowerCAmelCase_							)(lowerCAmelCase_   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    jnp.where(cur_len == self.begin_index   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.where(
										    self.max_initial_timestamp_index is not None   ,				True and apply_max_initial_timestamp   ,				lowerCAmelCase_   ,				)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.timestamp_begin + self.max_initial_timestamp_index

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    jnp.where(
										    lowerCAmelCase_   ,				scores.at[:, last_allowed + 1 :].set(-float('inf'							)							)   ,				lowerCAmelCase_   ,				)

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

										def handle_cumulative_probs(lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		Any       		=    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   ,				axis=-1							)
														_SCREAMING_SNAKE_CASE   :		int       		=    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'							)							)   ,				lowerCAmelCase_   ,				)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    jax.vmap(lowerCAmelCase_							)(lowerCAmelCase_   ,				lowerCAmelCase_							)

										return scores

