



import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE__       =      1_6
SCREAMING_SNAKE_CASE__       =      3_2


def A						(       __UpperCamelCase ,	__UpperCamelCase = 16					)						->      List[str]:
			A__       =    AutoTokenizer.from_pretrained('bert-base-cased'					)
			A__       =    load_dataset('glue' ,	'mrpc'					)

			def tokenize_function(__UpperCamelCase					):
						# max_length=None => use the model max length (it's actually the default)
						A__       =    tokenizer(examples['sentence1'] ,	examples['sentence2'] ,	truncation=__UpperCamelCase ,	max_length=__UpperCamelCase					)
						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(
						    __UpperCamelCase ,	batched=__UpperCamelCase ,	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(__UpperCamelCase					):
						# 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(
						    __UpperCamelCase ,	padding='longest' ,	max_length=__UpperCamelCase ,	pad_to_multiple_of=__UpperCamelCase ,	return_tensors='pt' ,	)

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

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							SCREAMING_SNAKE_CASE__       =      mocked_dataloaders  # noqa: F811


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS' ,	__UpperCamelCase					) == "1":
						A__       =    2
			# 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'					)

			# New Code #
			# We now can define an inner training loop function. It should take a batch size as the only parameter,
			# and build the dataloaders in there.
			# It also gets our decorator
			@find_executable_batch_size(starting_batch_size=__UpperCamelCase					)
			def inner_training_loop(__UpperCamelCase					):
						# And now just move everything below under this function
						# We need to bring in the Accelerator object from earlier
						nonlocal accelerator
						# And reset all of its attributes that could hold onto any memory:
						accelerator.free_memory()

						# Then we can declare the model, optimizer, and everything else:
						set_seed(__UpperCamelCase					)

						# 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=__UpperCamelCase					)

						# 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=__UpperCamelCase					)
						A__     , A__       =    get_dataloaders(__UpperCamelCase ,	__UpperCamelCase					)

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

						# 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(
						    __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

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

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
			inner_training_loop()


def A						(       )						->      Tuple:
			A__       =    argparse.ArgumentParser(description='Simple example of training script.'					)
			parser.add_argument(
			    '--mixed_precision' ,	type=__UpperCamelCase ,	default=__UpperCamelCase ,	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.'					)
			A__       =    parser.parse_args()
			A__       =    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(__UpperCamelCase ,	__UpperCamelCase					)


if __name__ == "__main__":
							main()









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


SCREAMING_SNAKE_CASE__       =      get_logger(__name__)


SCREAMING_SNAKE_CASE__       =      r'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''


class      __lowerCAmelCase     :
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""simple docstring"""


								raise NotImplementedError(
								    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)







class      __lowerCAmelCase     :
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""simple docstring"""


								raise NotImplementedError(
								    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Any				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int				,					**_snake_case					:       Optional[int]					):






								"""simple docstring"""


								for processor in self:
											A__       =    inspect.signature(processor.__call__					).parameters
											if len(_snake_case					) > 3:
														if not all(arg in kwargs for arg in list(function_args.keys()					)[2:]					):
																	raise ValueError(
																	    F'''Make sure that all the required parameters: {list(function_args.keys()					)} for '''
																	    F'''{processor.__class__} are passed to the logits processor.'''					)
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case				,					**_snake_case					)
											else:
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case					)
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       float					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					) or not (temperature > 0):
											raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''					)

								A__       =    temperature



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    scores / self.temperature
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Optional[Any]				,					_snake_case					:       float				,					_snake_case					:       float = -float('Inf'					)				,					_snake_case					:       int = 1					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					) or (top_p < 0 or top_p > 1.0):
											raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''					)
								if not isinstance(_snake_case				,					_snake_case					) or (min_tokens_to_keep < 1):
											raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''					)

								A__       =    top_p
								A__       =    filter_value
								A__       =    min_tokens_to_keep



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    lax.top_k(_snake_case				,					scores.shape[-1]					)

								A__       =    jnp.full_like(_snake_case				,					self.filter_value					)
								A__       =    jax.nn.softmax(_snake_case				,					axis=-1					).cumsum(axis=-1					)
								A__       =    cumulative_probs < self.top_p

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

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

								A__       =    jnp.where(_snake_case				,					_snake_case				,					_snake_case					)
								A__       =    jax.lax.sort_key_val(_snake_case				,					_snake_case					)[-1]

								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       int				,					_snake_case					:       float = -float('Inf'					)				,					_snake_case					:       int = 1					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					) or top_k <= 0:
											raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''					)

								A__       =    max(_snake_case				,					_snake_case					)
								A__       =    filter_value



					def __call__(     self					:       Optional[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    scores.shape
								A__       =    jnp.full(batch_size * vocab_size				,					self.filter_value					)

								A__       =    min(self.top_k				,					scores.shape[-1]					)  # Safety check
								A__     , A__       =    lax.top_k(_snake_case				,					_snake_case					)
								A__       =    jnp.broadcast_to((jnp.arange(_snake_case					) * vocab_size)[:, None]				,					(batch_size, topk)					).flatten()
								A__       =    topk_scores.flatten()
								A__       =    topk_indices.flatten() + shift

								A__       =    next_scores_flat.at[topk_indices_flat].set(_snake_case					)
								A__       =    next_scores_flat.reshape(_snake_case				,					_snake_case					)
								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    bos_token_id



					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

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

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    max_length
								A__       =    eos_token_id



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

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

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Dict				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


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

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

								A__       =    min_length
								A__       =    eos_token_id



					def __call__(     self					:       int				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    1 - jnp.clip(cur_len - self.min_length				,					0				,					1					)

								A__       =    jnp.where(_snake_case				,					scores.at[:, self.eos_token_id].set(-float('inf'					)					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					_snake_case					:       Tuple				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    list(_snake_case					)
								A__       =    begin_index



					def __call__(     self					:       Union[str, Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       str				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    1 - jnp.bool_(cur_len - self.begin_index					)

								A__       =    jnp.where(_snake_case				,					scores.at[:, self.begin_suppress_tokens].set(-float('inf'					)					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					_snake_case					:       list					):






								"""simple docstring"""


								A__       =    list(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    scores.at[..., self.suppress_tokens].set(-float('inf'					)					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    dict(_snake_case					)
								# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
								# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
								# Indexes without forced tokens will have a negative value.
								A__       =    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:
														A__       =    force_token_array.at[index].set(_snake_case					)
								A__       =    jnp.intaa(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								def _force_token(_snake_case					:       Dict					):
											A__       =    scores.shape[0]
											A__       =    self.force_token_array[generation_idx]

											A__       =    jnp.ones_like(_snake_case				,					dtype=scores.dtype					) * -float('inf'					)
											A__       =    jnp.zeros((batch_size, 1)				,					dtype=scores.dtype					)
											A__       =    lax.dynamic_update_slice(_snake_case				,					_snake_case				,					(0, current_token)					)
											return new_scores

								A__       =    lax.cond(
								    cur_len >= self.force_token_array.shape[0]				,					lambda: scores				,					lambda: lax.cond(
								        self.force_token_array[cur_len] >= 0				,					lambda: _force_token(_snake_case					)				,					lambda: scores				,					)				,					)
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Dict				,					_snake_case					:       List[Any]					):






								"""simple docstring"""


								A__       =    generate_config.eos_token_id
								A__       =    generate_config.no_timestamps_token_id
								A__       =    generate_config.no_timestamps_token_id + 1

								A__       =    decoder_input_length + 1

								if generate_config.is_multilingual:
											# room for language token and task token
											self.begin_index += 2
								if hasattr(_snake_case				,					'max_initial_timestamp_index'					):
											A__       =    generate_config.max_initial_timestamp_index
								else:
											A__       =    model_config.vocab_size
								if self.max_initial_timestamp_index is None:
											A__       =    model_config.vocab_size



					def __call__(     self					:       Tuple				,					_snake_case					:       List[Any]				,					_snake_case					:       Dict				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    scores.at[:, self.no_timestamps_token_id].set(-float('inf'					)					)

								def handle_pairs(_snake_case					:       Dict				,					_snake_case					:       str					):
											A__       =    jnp.where((cur_len - self.begin_index) >= 1				,					_snake_case				,					_snake_case					)
											A__       =    jnp.where(
											    input_ids_k[cur_len - 1] >= self.timestamp_begin				,					True and last_was_timestamp				,					_snake_case				,					)

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

											return jnp.where(
											    _snake_case				,					jnp.where(
											        penultimate_was_timestamp > 0				,					scores_k.at[self.timestamp_begin :].set(-float('inf'					)					)				,					scores_k.at[: self.eos_token_id].set(-float('inf'					)					)				,					)				,					_snake_case				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

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

								A__       =    self.timestamp_begin + self.max_initial_timestamp_index

								A__       =    jnp.where(
								    _snake_case				,					scores.at[:, last_allowed + 1 :].set(-float('inf'					)					)				,					_snake_case				,					)

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

								def handle_cumulative_probs(_snake_case					:       List[Any]				,					_snake_case					:       Union[str, Any]					):
											A__       =    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,					axis=-1					)
											A__       =    jnp.max(logprobs_k[: self.timestamp_begin]					)
											return jnp.where(
											    timestamp_logprob > max_text_token_logprob				,					scores_k.at[: self.timestamp_begin].set(-float('inf'					)					)				,					_snake_case				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

								return scores




