




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCamelCase	:		List[str]							       =   16
_lowerCamelCase	:		int							       =   32







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ = 16						)   ->       List[str]:



       """simple docstring"""

       A__       =    AutoTokenizer.from_pretrained('''bert-base-cased'''						)
       A__       =    DatasetDict(
           {
               '''train''': dataset['''train'''].select(lowercase_						),
               '''validation''': dataset['''train'''].select(lowercase_						),
               '''test''': dataset['''validation'''],
           }						)

       def tokenize_function(lowercase_						):
              # max_length=None => use the model max length (it's actually the default)
              A__       =    tokenizer(examples['''sentence1'''] ,				examples['''sentence2'''] ,				truncation=lowercase_ ,				max_length=lowercase_						)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              A__       =    datasets.map(
                  lowercase_ ,				batched=lowercase_ ,				remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,				)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A__       =    tokenized_datasets.rename_column('''label''' ,				'''labels'''						)

       def collate_fn(lowercase_						):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              A__       =    128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     A__       =    16
              elif accelerator.mixed_precision != "no":
                     A__       =    8
              else:
                     A__       =    None

              return tokenizer.pad(
                  lowercase_ ,				padding='''longest''' ,				max_length=lowercase_ ,				pad_to_multiple_of=lowercase_ ,				return_tensors='''pt''' ,				)

       # Instantiate dataloaders.
       A__       =    DataLoader(
           tokenized_datasets['''train'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)
       A__       =    DataLoader(
           tokenized_datasets['''validation'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)

       A__       =    DataLoader(
           tokenized_datasets['''test'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)

       return train_dataloader, eval_dataloader, test_dataloader







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    []
       # Download the dataset
       A__       =    load_dataset('''glue''' ,				'''mrpc'''						)
       # Create our splits
       A__       =    StratifiedKFold(n_splits=int(args.num_folds						)						)
       # Initialize accelerator
       A__       =    Accelerator(cpu=args.cpu ,				mixed_precision=args.mixed_precision						)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A__       =    config['''lr''']
       A__       =    int(config['''num_epochs''']						)
       A__       =    int(config['''seed''']						)
       A__       =    int(config['''batch_size''']						)

       A__       =    evaluate.load('''glue''' ,				'''mrpc'''						)

       # If the batch size is too big we use gradient accumulation
       A__       =    1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              A__       =    batch_size // MAX_GPU_BATCH_SIZE
              A__       =    MAX_GPU_BATCH_SIZE

       set_seed(lowercase_						)

       # New Code #
       # Create our folds:
       A__       =    kfold.split(np.zeros(datasets['''train'''].num_rows						) ,				datasets['''train''']['''label''']						)
       A__       =    []
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(lowercase_						):
              A__ ,							A__ ,							A__       =    get_fold_dataloaders(
                  lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				)
              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              A__       =    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,				return_dict=lowercase_						)

              # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
              # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
              # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
              A__       =    model.to(accelerator.device						)

              # Instantiate optimizer
              A__       =    AdamW(params=model.parameters() ,				lr=lowercase_						)

              # Instantiate scheduler
              A__       =    get_linear_schedule_with_warmup(
                  optimizer=lowercase_ ,				num_warmup_steps=100 ,				num_training_steps=(len(lowercase_						) * num_epochs) // gradient_accumulation_steps ,				)

              # Prepare everything
              # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
              # prepare method.
              A__ ,							A__ ,							A__ ,							A__ ,							A__       =    accelerator.prepare(
                  lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)

              # Now we train the model
              for epoch in range(lowercase_						):
                     model.train()
                     for step, batch in enumerate(lowercase_						):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device						)
                            A__       =    model(**lowercase_						)
                            A__       =    outputs.loss
                            A__       =    loss / gradient_accumulation_steps
                            accelerator.backward(lowercase_						)
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     model.eval()
                     for step, batch in enumerate(lowercase_						):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device						)
                            with torch.no_grad():
                                   A__       =    model(**lowercase_						)
                            A__       =    outputs.logits.argmax(dim=-1						)
                            A__ ,							A__       =    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                            metric.add_batch(
                                predictions=lowercase_ ,				references=lowercase_ ,				)

                     A__       =    metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(f"""epoch {epoch}:""" ,				lowercase_						)

              # New Code #
              # We also run predictions on the test set at the very end
              A__       =    []
              for step, batch in enumerate(lowercase_						):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device						)
                     with torch.no_grad():
                            A__       =    model(**lowercase_						)
                     A__       =    outputs.logits
                     A__ ,							A__       =    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                     fold_predictions.append(predictions.cpu()						)
                     if i == 0:
                            # We need all of the test predictions
                            test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
              test_predictions.append(torch.cat(lowercase_ ,				dim=0						)						)
              # We now need to release all our memory and get rid of the current model, optimizer, etc
              accelerator.free_memory()
       # New Code #
       # Finally we check the accuracy of our folded results:
       A__       =    torch.cat(lowercase_ ,				dim=0						)
       A__       =    torch.stack(lowercase_ ,				dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
       A__       =    metric.compute(predictions=lowercase_ ,				references=lowercase_						)
       accelerator.print('''Average test metrics from all folds:''' ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       )   ->       Tuple:



       """simple docstring"""

       A__       =    argparse.ArgumentParser(description='''Simple example of training script.'''						)
       parser.add_argument(
           '''--mixed_precision''' ,				type=lowercase_ ,				default=lowercase_ ,				choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,				help='''Whether to use mixed precision. Choose'''
           '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
           '''and an Nvidia Ampere GPU.''' ,				)
       parser.add_argument('''--cpu''' ,				action='''store_true''' ,				help='''If passed, will train on the CPU.'''						)
       # New Code #
       parser.add_argument('''--num_folds''' ,				type=lowercase_ ,				default=3 ,				help='''The number of splits to perform across the dataset'''						)
       A__       =    parser.parse_args()
       A__       =    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_ ,				lowercase_						)


if __name__ == "__main__":
  main()





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.generation import (
						    FlaxForcedBOSTokenLogitsProcessor,
						    FlaxForcedEOSTokenLogitsProcessor,
						    FlaxLogitsProcessorList,
						    FlaxMinLengthLogitsProcessor,
						    FlaxTemperatureLogitsWarper,
						    FlaxTopKLogitsWarper,
						    FlaxTopPLogitsWarper,
						)



@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: str  		=       jnp.ones((batch_size, length)) / length
									return scores



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       None
									SCREAMING_SNAKE_CASE_: str  		=       20

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(batch_size=2   ,						length=lowerCAmelCase__)

									# tweak scores to not be uniform anymore
									SCREAMING_SNAKE_CASE_: List[str]  		=       scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
									SCREAMING_SNAKE_CASE_: Any  		=       scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

									# compute softmax
									SCREAMING_SNAKE_CASE_: Dict  		=       jax.nn.softmax(lowerCAmelCase__   ,						axis=-1)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FlaxTemperatureLogitsWarper(temperature=0.5)
									SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxTemperatureLogitsWarper(temperature=1.3)

									SCREAMING_SNAKE_CASE_: str  		=       jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase__   ,						scores.copy()   ,						cur_len=lowerCAmelCase__)   ,						axis=-1)
									SCREAMING_SNAKE_CASE_: int  		=       jax.nn.softmax(temp_dist_warper_smoother(lowerCAmelCase__   ,						scores.copy()   ,						cur_len=lowerCAmelCase__)   ,						axis=-1)

									# uniform distribution stays uniform
									self.assertTrue(jnp.allclose(probs[0, :]   ,						warped_prob_sharp[0, :]   ,						atol=1E-3))
									self.assertTrue(jnp.allclose(probs[0, :]   ,						warped_prob_smooth[0, :]   ,						atol=1E-3))

									# sharp peaks get higher, valleys get lower
									self.assertLess(probs[1, :].max()   ,						warped_prob_sharp[1, :].max())
									self.assertGreater(probs[1, :].min()   ,						warped_prob_sharp[1, :].min())

									# smooth peaks get lower, valleys get higher
									self.assertGreater(probs[1, :].max()   ,						warped_prob_smooth[1, :].max())
									self.assertLess(probs[1, :].min()   ,						warped_prob_smooth[1, :].min())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       None
									SCREAMING_SNAKE_CASE_: str  		=       10
									SCREAMING_SNAKE_CASE_: Tuple  		=       2

									# create ramp distribution
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.broadcast_to(np.arange(lowerCAmelCase__)[None, :]   ,						(batch_size, vocab_size)).copy()
									SCREAMING_SNAKE_CASE_: Dict  		=       ramp_logits[1:, : vocab_size // 2] + vocab_size

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxTopKLogitsWarper(3)

									SCREAMING_SNAKE_CASE_: Dict  		=       top_k_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# check that correct tokens are filtered
									self.assertListEqual(jnp.isinf(scores[0]).tolist()   ,						7 * [True] + 3 * [False])
									self.assertListEqual(jnp.isinf(scores[1]).tolist()   ,						2 * [True] + 3 * [False] + 5 * [True])

									# check special case
									SCREAMING_SNAKE_CASE_: Any  		=       5
									SCREAMING_SNAKE_CASE_: str  		=       FlaxTopKLogitsWarper(top_k=1   ,						filter_value=0.0   ,						min_tokens_to_keep=3)

									SCREAMING_SNAKE_CASE_: Any  		=       np.broadcast_to(np.arange(lowerCAmelCase__)[None, :]   ,						(batch_size, length)).copy()
									SCREAMING_SNAKE_CASE_: Any  		=       top_k_warp_safety_check(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
									self.assertListEqual((scores == 0.0).sum(axis=-1).tolist()   ,						[2, 2])



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Tuple  		=       None
									SCREAMING_SNAKE_CASE_: Dict  		=       10
									SCREAMING_SNAKE_CASE_: Dict  		=       2

									# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
									SCREAMING_SNAKE_CASE_: Tuple  		=       np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

									SCREAMING_SNAKE_CASE_: int  		=       FlaxTopPLogitsWarper(0.8)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.exp(top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__))

									# dist should be filtered to keep min num values so that sum is >= top_p
									# exp (-inf) => 0
									SCREAMING_SNAKE_CASE_: Dict  		=       np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
									self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))

									# check edge cases with negative and extreme logits
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.broadcast_to(np.arange(lowerCAmelCase__)[None, :]   ,						(batch_size, vocab_size)).copy() - (
									    vocab_size // 2
									)

									# make ramp_logits more extreme
									SCREAMING_SNAKE_CASE_: Dict  		=       ramp_logits[1] * 100.0

									# make sure at least 2 tokens are kept
									SCREAMING_SNAKE_CASE_: str  		=       FlaxTopPLogitsWarper(0.9   ,						min_tokens_to_keep=2   ,						filter_value=0.0)
									SCREAMING_SNAKE_CASE_: Any  		=       top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
									self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist()   ,						[3, 2])



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Tuple  		=       20
									SCREAMING_SNAKE_CASE_: List[str]  		=       4
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       0

									SCREAMING_SNAKE_CASE_: str  		=       FlaxMinLengthLogitsProcessor(min_length=10   ,						eos_token_id=lowerCAmelCase__)

									# check that min length is applied at length 5
									SCREAMING_SNAKE_CASE_: str  		=       ids_tensor((batch_size, 20)   ,						vocab_size=20)
									SCREAMING_SNAKE_CASE_: int  		=       5
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       min_dist_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()   ,						4 * [-float("inf")])

									# check that min length is not applied anymore at length 15
									SCREAMING_SNAKE_CASE_: List[str]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       15
									SCREAMING_SNAKE_CASE_: Any  		=       min_dist_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertFalse(jnp.isinf(lowerCAmelCase__).any())



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: int  		=       20
									SCREAMING_SNAKE_CASE_: str  		=       4
									SCREAMING_SNAKE_CASE_: List[Any]  		=       0

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__)

									# check that all scores are -inf except the bos_token_id score
									SCREAMING_SNAKE_CASE_: int  		=       ids_tensor((batch_size, 1)   ,						vocab_size=20)
									SCREAMING_SNAKE_CASE_: List[str]  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
									self.assertListEqual(scores[:, bos_token_id].tolist()   ,						4 * [0])  # score for bos_token_id shold be zero

									# check that bos_token_id is not forced if current length is greater than 1
									SCREAMING_SNAKE_CASE_: List[Any]  		=       3
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertFalse(jnp.isinf(lowerCAmelCase__).any())



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Any  		=       20
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       4
									SCREAMING_SNAKE_CASE_: Dict  		=       0
									SCREAMING_SNAKE_CASE_: List[Any]  		=       5

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__)

									# check that all scores are -inf except the eos_token_id when max_length is reached
									SCREAMING_SNAKE_CASE_: List[Any]  		=       ids_tensor((batch_size, 4)   ,						vocab_size=20)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       4
									SCREAMING_SNAKE_CASE_: Dict  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
									self.assertListEqual(scores[:, eos_token_id].tolist()   ,						4 * [0])  # score for eos_token_id should be zero

									# check that eos_token_id is not forced if max_length is not reached
									SCREAMING_SNAKE_CASE_: List[str]  		=       3
									SCREAMING_SNAKE_CASE_: str  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertFalse(jnp.isinf(lowerCAmelCase__).any())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: int  		=       4
									SCREAMING_SNAKE_CASE_: List[Any]  		=       10
									SCREAMING_SNAKE_CASE_: int  		=       15
									SCREAMING_SNAKE_CASE_: Dict  		=       2
									SCREAMING_SNAKE_CASE_: int  		=       1
									SCREAMING_SNAKE_CASE_: List[Any]  		=       15

									# dummy input_ids and scores
									SCREAMING_SNAKE_CASE_: int  		=       ids_tensor((batch_size, sequence_length)   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       input_ids.copy()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       scores.copy()

									# instantiate all dist processors
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxTemperatureLogitsWarper(temperature=0.5)
									SCREAMING_SNAKE_CASE_: Tuple  		=       FlaxTopKLogitsWarper(3)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxTopPLogitsWarper(0.8)

									# instantiate all logits processors
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxMinLengthLogitsProcessor(min_length=10   ,						eos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: str  		=       10

									# no processor list
									SCREAMING_SNAKE_CASE_: Dict  		=       temp_dist_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       top_k_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       min_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       bos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       eos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# with processor list
									SCREAMING_SNAKE_CASE_: str  		=       FlaxLogitsProcessorList(
									    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
									SCREAMING_SNAKE_CASE_: Tuple  		=       processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# scores should be equal
									self.assertTrue(jnp.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))

									# input_ids should never be changed
									self.assertListEqual(input_ids.tolist()   ,						input_ids_comp.tolist())




			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       4
									SCREAMING_SNAKE_CASE_: int  		=       10
									SCREAMING_SNAKE_CASE_: List[str]  		=       15
									SCREAMING_SNAKE_CASE_: List[Any]  		=       2
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       1
									SCREAMING_SNAKE_CASE_: str  		=       15

									# dummy input_ids and scores
									SCREAMING_SNAKE_CASE_: Tuple  		=       ids_tensor((batch_size, sequence_length)   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       input_ids.copy()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       scores.copy()

									# instantiate all dist processors
									SCREAMING_SNAKE_CASE_: Dict  		=       FlaxTemperatureLogitsWarper(temperature=0.5)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxTopKLogitsWarper(3)
									SCREAMING_SNAKE_CASE_: Dict  		=       FlaxTopPLogitsWarper(0.8)

									# instantiate all logits processors
									SCREAMING_SNAKE_CASE_: int  		=       FlaxMinLengthLogitsProcessor(min_length=10   ,						eos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: str  		=       10

									# no processor list
									def run_no_processor_list(lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Dict):
															SCREAMING_SNAKE_CASE_: Any  		=       temp_dist_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       top_k_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       min_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       bos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       eos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															return scores

									# with processor list
									def run_processor_list(lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
															SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxLogitsProcessorList(
															    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
															SCREAMING_SNAKE_CASE_: Dict  		=       processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															return scores

									SCREAMING_SNAKE_CASE_: str  		=       jax.jit(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       jax.jit(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       jitted_run_no_processor_list(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       jitted_run_processor_list(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									# scores should be equal
									self.assertTrue(jnp.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))

									# input_ids should never be changed
									self.assertListEqual(input_ids.tolist()   ,						input_ids_comp.tolist())






