






"""simple docstring"""
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowerCAmelCase__    						=			os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
			from run_translation import main  # noqa


set_seed(42)
lowerCAmelCase__    						=			'sshleifer/student_marian_en_ro_6_1'
lowerCAmelCase__    						=			'sshleifer/tiny-mbart'


@require_torch
class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def 			A      (    self   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=True   ,				)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.run_trainer(
										    eval_steps=1   ,				max_len=1_2   ,				model_name=lowerCAmelCase_   ,				num_train_epochs=1   ,				distributed=lowerCAmelCase_   ,				extra_args_str=lowerCAmelCase_   ,				predict_with_generate=lowerCAmelCase_   ,				do_train=lowerCAmelCase_   ,				do_eval=lowerCAmelCase_   ,				do_predict=lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    TrainerState.load_from_json(os.path.join(lowerCAmelCase_   ,				'trainer_state.json'							)							).log_history

										if not do_eval:
														return

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    [log for log in logs if 'eval_loss' in log.keys()]

										_SCREAMING_SNAKE_CASE   :		str       		=    eval_metrics[0]
										if predict_with_generate:
														assert "eval_bleu" in first_step_stats

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    eval_metrics[-1]
														assert isinstance(last_step_stats['eval_bleu']   ,				lowerCAmelCase_							)
														assert not math.isnan(float(last_step_stats['eval_loss']							)							), "eval_loss must not be `nan`"







						@require_torch_non_multi_gpu
						def 			A      (    self							)		->							Any:
										self.run_seqaseq_quick()







						@require_torch_multi_gpu
						def 			A      (    self							)		->							Optional[int]:
										self.run_seqaseq_quick(distributed=lowerCAmelCase_							)







						@require_torch_multi_gpu
						def 			A      (    self							)		->							List[Any]:
										self.run_seqaseq_quick(distributed=lowerCAmelCase_							)







						@unittest.skip('Requires an update of the env running those tests'							)
						@require_torch_multi_gpu
						@require_fairscale
						def 			A      (    self							)		->							List[Any]:
										self.run_seqaseq_quick(distributed=lowerCAmelCase_   ,				extra_args_str='--sharded_ddp simple'							)







						@unittest.skip('Requires an update of the env running those tests'							)
						@require_torch_multi_gpu
						@require_fairscale
						def 			A      (    self							)		->							Tuple:
										self.run_seqaseq_quick(distributed=lowerCAmelCase_   ,				extra_args_str='--sharded_ddp simple --fp16'							)







						@unittest.skip('Requires an update of the env running those tests'							)
						@require_torch_multi_gpu
						@require_fairscale
						def 			A      (    self							)		->							str:
										self.run_seqaseq_quick(distributed=lowerCAmelCase_   ,				extra_args_str='--sharded_ddp zero_dp_2'   ,				predict_with_generate=lowerCAmelCase_							)







						@unittest.skip('Requires an update of the env running those tests'							)
						@require_torch_multi_gpu
						@require_fairscale
						def 			A      (    self							)		->							str:
										self.run_seqaseq_quick(
										    distributed=lowerCAmelCase_   ,				extra_args_str='--sharded_ddp zero_dp_2 --fp16'   ,				predict_with_generate=lowerCAmelCase_							)







						@require_apex
						@require_torch_gpu
						def 			A      (    self							)		->							str:
										# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
										# program and it breaks other tests that run from the same pytest worker, therefore until this is
										# sorted out it must be run only in an external program, that is distributed=True in this
										# test and only under one or more gpus - if we want cpu will need to make a special test
										#
										# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
										# 2nd main() call it botches the future eval.
										#
										self.run_seqaseq_quick(distributed=lowerCAmelCase_   ,				extra_args_str='--fp16 --fp16_backend=apex'							)
										# test 2nd time - was getting eval_loss': nan'
										# to reproduce the problem set distributed=False
										self.run_seqaseq_quick(distributed=lowerCAmelCase_   ,				extra_args_str='--fp16 --fp16_backend=apex'							)







						@parameterized.expand(['base', 'low', 'high', 'mixed']							)
						@require_torch_multi_gpu
						def 			A      (    self   ,				lowerCAmelCase_							)		->							Dict:
										# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
										_SCREAMING_SNAKE_CASE   :		int       		=    {
										    # test with the default log_level - should be info and thus log info once
										    'base': {'extra_args_str': '', 'n_matches': 1},
										    # test with low log_level and log_level_replica - should be noisy on all processes
										    # now the info string should appear twice on 2 processes
										    'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
										    # test with high log_level and low log_level_replica
										    # now the info string should appear once only on the replica
										    'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
										    # test with high log_level and log_level_replica - should be quiet on all processes
										    'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
										}

										_SCREAMING_SNAKE_CASE   :		Any       		=    experiments[experiment_id]
										_SCREAMING_SNAKE_CASE   :		Any       		=    {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
										_SCREAMING_SNAKE_CASE   :		Any       		=    'Running training'
										with CaptureStderr() as cl:
														self.run_seqaseq_quick(**lowerCAmelCase_   ,				extra_args_str=data['extra_args_str']							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    len(re.findall(lowerCAmelCase_   ,				cl.err							)							)
										self.assertEqual(lowerCAmelCase_   ,				data['n_matches']							)







						@slow
						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.run_trainer(
										    eval_steps=2   ,				max_len=1_2_8   ,				model_name=lowerCAmelCase_   ,				learning_rate=3e-4   ,				num_train_epochs=1_0   ,				distributed=lowerCAmelCase_   ,				)

										# Check metrics
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    TrainerState.load_from_json(os.path.join(lowerCAmelCase_   ,				'trainer_state.json'							)							).log_history
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    [log for log in logs if 'eval_loss' in log.keys()]
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    eval_metrics[0]
										_SCREAMING_SNAKE_CASE   :		str       		=    eval_metrics[-1]

										assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
										assert isinstance(last_step_stats['eval_bleu']   ,				lowerCAmelCase_							)

										# test if do_predict saves generations and metrics
										_SCREAMING_SNAKE_CASE   :		int       		=    os.listdir(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    {os.path.basename(lowerCAmelCase_							) for p in contents}
										assert "generated_predictions.txt" in contents
										assert "predict_results.json" in contents







						@slow
						@require_bitsandbytes
						def 			A      (    self							)		->							Union[str, Any]:
										from transformers.training_args import OptimizerNames

										def train_and_return_metrics(lowerCAmelCase_							) -> Tuple[int, float]:
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    '--skip_memory_metrics 0'

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.run_trainer(
														    max_len=1_2_8   ,				model_name=lowerCAmelCase_   ,				learning_rate=3e-4   ,				num_train_epochs=1   ,				optim=lowerCAmelCase_   ,				distributed=lowerCAmelCase_   ,				extra_args_str=lowerCAmelCase_   ,				do_eval=lowerCAmelCase_   ,				do_predict=lowerCAmelCase_   ,				n_gpus_to_use=1   ,				)

														# Check metrics
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    TrainerState.load_from_json(Path(lowerCAmelCase_   ,				'trainer_state.json'							)							).log_history
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    int(logs[0]['train_mem_gpu_peaked_delta'] / 2**2_0							)
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    int(logs[0]['train_mem_gpu_alloc_delta'] / 2**2_0							)

														_SCREAMING_SNAKE_CASE   :		str       		=    logs[0]['train_loss']
														return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

										_SCREAMING_SNAKE_CASE   :		str       		=    gpu_alloc_mem_orig - gpu_alloc_mem_bnb

										_SCREAMING_SNAKE_CASE   :		Dict       		=    gpu_peak_mem_orig + gpu_alloc_mem_orig
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    gpu_peak_mem_bnb + gpu_alloc_mem_bnb
										_SCREAMING_SNAKE_CASE   :		str       		=    gpu_total_mem_orig - gpu_total_mem_bnb

										# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
										# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
										# in 2 bytes and the diff in optim memory usage is derived as so:
										#
										# - normal 25*8=~200MB (8 bytes per param)
										# - bnb    25*2= ~50MB (2 bytes per param)
										#
										# Thus we should expect ~150MB total memory saved.
										#
										# Peak memory should be the same - the total should be different by about that same margin
										#
										# After leaving a small margin to accommodate for differences between gpus let's check
										# that we have at least 120MB in savings
										_SCREAMING_SNAKE_CASE   :		Any       		=    1_2_0

										# uncomment the following if this test starts failing - requires py38 for a new print feature
										# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
										# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
										# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
										# print(f"{gpu_alloc_mem_diff=}MB")
										# print(f"{gpu_peak_mem_diff=}MB")
										# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
										# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

										self.assertGreater(
										    lowerCAmelCase_   ,				lowerCAmelCase_   ,				'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
										    F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
										    F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""   ,				)

										self.assertGreater(
										    lowerCAmelCase_   ,				lowerCAmelCase_   ,				'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
										    F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
										    F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""   ,				)

										self.assertEqual(
										    lowerCAmelCase_   ,				lowerCAmelCase_   ,				F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 3e-3   ,				lowerCAmelCase_ = "adafactor"   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = None   ,				)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.get_auto_remove_tmp_dir()
										_SCREAMING_SNAKE_CASE   :		str       		=    F"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(lowerCAmelCase_							)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCAmelCase_							)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    F"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(lowerCAmelCase_							)}
        """.split()

										_SCREAMING_SNAKE_CASE   :		Dict       		=    '\n            --do_predict\n        '.split()

										_SCREAMING_SNAKE_CASE   :		int       		=    []
										if do_train:
														args += args_train

										if do_eval:
														args += args_eval

										if do_predict:
														args += args_predict

										if predict_with_generate:
														args += "--predict_with_generate".split()

										if do_train:
														if optim == "adafactor":
																		args += "--adafactor".split()
														else:
																		args += F"""--optim {optim}""".split()

										if extra_args_str is not None:
														args += extra_args_str.split()

										if distributed:
														if n_gpus_to_use is None:
																		_SCREAMING_SNAKE_CASE   :		int       		=    get_gpu_count()
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    get_torch_dist_unique_port()
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    F"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
														_SCREAMING_SNAKE_CASE   :		Any       		=    [sys.executable] + distributed_args + args
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
														execute_subprocess_async(lowerCAmelCase_   ,				env=self.get_env()							)
										else:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ['run_translation.py'] + args
														with patch.object(lowerCAmelCase_   ,				'argv'   ,				lowerCAmelCase_							):
																		main()

										return output_dir









"""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

