

import argparse
import os

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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# 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
#
########################################################################

_UpperCAmelCase		:   str										=       16
_UpperCAmelCase		:   Any										=       32





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = 16      ):







							'''simple docstring'''






							snake_case_							=    AutoTokenizer.from_pretrained('bert-base-cased'      )
							snake_case_							=    load_dataset('glue'   ,							'mrpc'      )

							def tokenize_function(UpperCamelCase__      ):
														# max_length=None => use the model max length (it's actually the default)
														snake_case_							=    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():
														snake_case_							=    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
							snake_case_							=    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.
														snake_case_							=    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":
																					snake_case_							=    16
														elif accelerator.mixed_precision != "no":
																					snake_case_							=    8
														else:
																					snake_case_							=    None

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

							# Instantiate dataloaders.
							snake_case_							=    DataLoader(
							    tokenized_datasets['train']   ,							shuffle=UpperCamelCase__   ,							collate_fn=UpperCamelCase__   ,							batch_size=UpperCamelCase__      )
							snake_case_							=    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

					_UpperCAmelCase		:   Tuple										=       mocked_dataloaders  # noqa: F811





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,							UpperCamelCase__      ) == "1":
														snake_case_							=    2
							# Initialize Accelerator

							# New Code #
							# We pass in "all" to `log_with` to grab all available trackers in the environment
							# Note: If using a custom `Tracker` class, should be passed in here such as:
							# >>> log_with = ["all", MyCustomTrackerClassInstance()]
							if args.with_tracking:
														snake_case_							=    Accelerator(
														    cpu=args.cpu   ,							mixed_precision=args.mixed_precision   ,							log_with='all'   ,							project_dir=args.project_dir      )
							else:
														snake_case_							=    Accelerator(cpu=args.cpu   ,							mixed_precision=args.mixed_precision      )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							snake_case_							=    config['lr']
							snake_case_							=    int(config['num_epochs']      )
							snake_case_							=    int(config['seed']      )
							snake_case_							=    int(config['batch_size']      )
							set_seed(UpperCamelCase__      )

							snake_case_				,    snake_case_							=    get_dataloaders(UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    evaluate.load('glue'   ,							'mrpc'      )

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

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							snake_case_							=    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).
							snake_case_							=    model.to(accelerator.device      )

							# Instantiate optimizer
							snake_case_							=    AdamW(params=model.parameters()   ,							lr=UpperCamelCase__      )

							# Instantiate scheduler
							snake_case_							=    get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase__   ,							num_warmup_steps=100   ,							num_training_steps=(len(UpperCamelCase__      ) * 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.
							snake_case_				,    snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    accelerator.prepare(
							    UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							# New Code #
							# We need to initialize the trackers we use. Overall configurations can also be stored
							if args.with_tracking:
														snake_case_							=    os.path.split(UpperCamelCase__      )[-1].split('.'      )[0]
														accelerator.init_trackers(UpperCamelCase__   ,							UpperCamelCase__      )

							# Now we train the model
							for epoch in range(UpperCamelCase__      ):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					snake_case_							=    0
														for step, batch in enumerate(UpperCamelCase__      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device      )
																					snake_case_							=    model(**UpperCamelCase__      )
																					snake_case_							=    outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					snake_case_							=    loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase__      )
																					if step % gradient_accumulation_steps == 0:
																												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` (the default).
																					batch.to(accelerator.device      )
																					with torch.no_grad():
																												snake_case_							=    model(**UpperCamelCase__      )
																					snake_case_							=    outputs.logits.argmax(dim=-1      )
																					snake_case_				,    snake_case_							=    accelerator.gather_for_metrics((predictions, batch['labels'])      )
																					metric.add_batch(
																					    predictions=UpperCamelCase__   ,							references=UpperCamelCase__   ,							)

														snake_case_							=    metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''   ,							UpperCamelCase__      )

														# New Code #
														# To actually log, we call `Accelerator.log`
														# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        'accuracy': eval_metric['accuracy'],
																					        'f1': eval_metric['f1'],
																					        'train_loss': total_loss.item() / len(UpperCamelCase__      ),
																					        'epoch': epoch,
																					    }   ,							step=UpperCamelCase__   ,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
							if args.with_tracking:
														accelerator.end_training()





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    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.'      )
							parser.add_argument(
							    '--with_tracking'   ,							action='store_true'   ,							help='Whether to load in all available experiment trackers from the environment and use them for logging.'   ,							)
							parser.add_argument(
							    '--project_dir'   ,							type=UpperCamelCase__   ,							default='logs'   ,							help='Location on where to store experiment tracking logs` and relevent project information'   ,							)
							snake_case_							=    parser.parse_args()
							snake_case_							=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(UpperCamelCase__   ,							UpperCamelCase__      )


if __name__ == "__main__":
					main()







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			None
				__SCREAMING_SNAKE_CASE					: Any       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: int       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''tokenizer_file'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
											tokenizer.save_pretrained(self.tmpdirname       )




				def     a							(					self      ,   **snake_case       ):
											kwargs.update(self.special_tokens_map       )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()

											snake_case_							=    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											snake_case_							=    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

											snake_case_							=    tokenizer.batch_encode_plus(snake_case       )['input_ids']
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.batch_decode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case=6       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									snake_case_							=    self.rust_tokenizer_class.from_pretrained(snake_case      ,   **snake_case       )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									snake_case_							=    'This is a simple input'
																									snake_case_							=    ['This is a simple input 1', 'This is a simple input 2']
																									snake_case_							=    ('This is a simple input', 'This is a pair')
																									snake_case_							=    [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode_plus(snake_case      ,   max_length=snake_case       )

																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'       )

																									snake_case_							=    None  # Hotfixing padding = None
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    load_dataset('xnli'      ,   'all_languages'      ,   split='test'      ,   streaming=snake_case       )

											snake_case_							=    next(iter(snake_case       )       )['premise']  # pick up one data
											snake_case_							=    list(sample_data.values()       )

											snake_case_							=    list(map(tokenizer.encode      ,   snake_case       )       )
											snake_case_							=    [tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
											# any sequence length constraints. This test of the parent class will fail since it relies on the
											# maximum sequence length of the positoonal embeddings.
											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )      ,   1       )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )      ,   1       )




