


import argparse

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
#
# 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 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       :      Optional[int]												= 16
SCREAMING_SNAKE_CASE       :      List[Any]												= 32



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ = 16 )    ->						Optional[Any]:
    _lowercase       :							Optional[Any]      		=		AutoTokenizer.from_pretrained('bert-base-cased' )
    _lowercase       :							Any      		=		load_dataset('glue'							, 'mrpc' )

    def tokenize_function(lowerCamelCase_ ):
        # max_length=None => use the model max length (it's actually the default)
        _lowercase       :							Any      		=		tokenizer(examples['sentence1']							, examples['sentence2']							, truncation=lowerCamelCase_							, max_length=lowerCamelCase_ )
        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():
        _lowercase       :							Union[str, Any]      		=		datasets.map(
            lowerCamelCase_							, batched=lowerCamelCase_							, 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
    _lowercase       :							Tuple      		=		tokenized_datasets.rename_column('label'							, 'labels' )

    def collate_fn(lowerCamelCase_ ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _lowercase       :							str      		=		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":
            _lowercase       :							Union[str, Any]      		=		16
        elif accelerator.mixed_precision != "no":
            _lowercase       :							Any      		=		8
        else:
            _lowercase       :							List[Any]      		=		None

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

    # Instantiate dataloaders.
    _lowercase       :							str      		=		DataLoader(
        tokenized_datasets['train']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_							, drop_last=lowerCamelCase_ )
    _lowercase       :							str      		=		DataLoader(
        tokenized_datasets['validation']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_							, drop_last=(accelerator.mixed_precision == 'fp8')							, )

    return train_dataloader, eval_dataloader



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    # Initialize accelerator
    _lowercase       :							Union[str, Any]      		=		Accelerator(cpu=args.cpu							, mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase       :							Tuple      		=		config['lr']
    _lowercase       :							Any      		=		int(config['num_epochs'] )
    _lowercase       :							Optional[int]      		=		int(config['seed'] )
    _lowercase       :							List[str]      		=		int(config['batch_size'] )

    _lowercase       :							List[Any]      		=		evaluate.load('glue'							, 'mrpc' )

    # If the batch size is too big we use gradient accumulation
    _lowercase       :							Any      		=		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        _lowercase       :							List[str]      		=		batch_size // MAX_GPU_BATCH_SIZE
        _lowercase       :							Any      		=		MAX_GPU_BATCH_SIZE

    set_seed(lowerCamelCase_ )
    _lowercase		,				_lowercase       :							Optional[int]      		=		get_dataloaders(lowerCamelCase_							, lowerCamelCase_ )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase       :							List[Any]      		=		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							, return_dict=lowerCamelCase_ )

    # 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).
    _lowercase       :							Optional[Any]      		=		model.to(accelerator.device )
    # Instantiate optimizer
    _lowercase       :							str      		=		AdamW(params=model.parameters()							, lr=lowerCamelCase_ )

    # Instantiate scheduler
    _lowercase       :							Optional[Any]      		=		get_linear_schedule_with_warmup(
        optimizer=lowerCamelCase_							, num_warmup_steps=100							, num_training_steps=(len(lowerCamelCase_ ) * 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.

    _lowercase		,				_lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							int      		=		accelerator.prepare(
        lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

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

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

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



def 				UpperCamelCase_(      )    ->						Tuple:
    _lowercase       :							List[Any]      		=		argparse.ArgumentParser(description='Simple example of training script.' )
    parser.add_argument(
        '--mixed_precision'							, type=lowerCamelCase_							, default=lowerCamelCase_							, 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.' )
    _lowercase       :							Optional[int]      		=		parser.parse_args()
    _lowercase       :							Dict      		=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(lowerCamelCase_							, lowerCamelCase_ )


if __name__ == "__main__":
 main()






import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     SqueezeBertForMaskedLM,
     SqueezeBertForMultipleChoice,
     SqueezeBertForQuestionAnswering,
     SqueezeBertForSequenceClassification,
     SqueezeBertForTokenClassification,
     SqueezeBertModel,
 )






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase=13,      lowerCamelCase=7,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=False,      lowerCamelCase=True,      lowerCamelCase=99,      lowerCamelCase=32,      lowerCamelCase=5,      lowerCamelCase=4,      lowerCamelCase=64,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=5_12,      lowerCamelCase=16,      lowerCamelCase=2,      lowerCamelCase=0.0_2,      lowerCamelCase=3,      lowerCamelCase=4,      lowerCamelCase=None,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=4,      lowerCamelCase=1,      )       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		parent
           _lowercase       :							Optional[Any]      		=		batch_size
           _lowercase       :							Any      		=		seq_length
           _lowercase       :							Optional[Any]      		=		is_training
           _lowercase       :							Optional[Any]      		=		use_input_mask
           _lowercase       :							List[Any]      		=		use_token_type_ids
           _lowercase       :							List[str]      		=		use_labels
           _lowercase       :							str      		=		vocab_size
           _lowercase       :							List[str]      		=		hidden_size
           _lowercase       :							Dict      		=		num_hidden_layers
           _lowercase       :							List[str]      		=		num_attention_heads
           _lowercase       :							int      		=		intermediate_size
           _lowercase       :							Union[str, Any]      		=		hidden_act
           _lowercase       :							int      		=		hidden_dropout_prob
           _lowercase       :							List[Any]      		=		attention_probs_dropout_prob
           _lowercase       :							Dict      		=		max_position_embeddings
           _lowercase       :							Union[str, Any]      		=		type_vocab_size
           _lowercase       :							List[Any]      		=		type_sequence_label_size
           _lowercase       :							Any      		=		initializer_range
           _lowercase       :							List[str]      		=		num_labels
           _lowercase       :							Any      		=		num_choices
           _lowercase       :							Tuple      		=		scope
           _lowercase       :							Optional[Any]      		=		q_groups
           _lowercase       :							List[str]      		=		k_groups
           _lowercase       :							Optional[int]      		=		v_groups
           _lowercase       :							List[str]      		=		post_attention_groups
           _lowercase       :							Union[str, Any]      		=		intermediate_groups
           _lowercase       :							int      		=		output_groups
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		ids_tensor([self.batch_size, self.seq_length],      self.vocab_size)

           _lowercase       :							Any      		=		None
           if self.use_input_mask:
               _lowercase       :							Tuple      		=		random_attention_mask([self.batch_size, self.seq_length])

           _lowercase       :							Dict      		=		None
           _lowercase       :							int      		=		None
           _lowercase       :							List[Any]      		=		None
           if self.use_labels:
               _lowercase       :							List[Any]      		=		ids_tensor([self.batch_size],      self.type_sequence_label_size)
               _lowercase       :							int      		=		ids_tensor([self.batch_size, self.seq_length],      self.num_labels)
               _lowercase       :							Dict      		=		ids_tensor([self.batch_size],      self.num_choices)

           _lowercase       :							Optional[Any]      		=		self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           return SqueezeBertConfig(
               embedding_size=self.hidden_size,      vocab_size=self.vocab_size,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      intermediate_size=self.intermediate_size,      hidden_act=self.hidden_act,      attention_probs_dropout_prob=self.hidden_dropout_prob,      attention_dropout=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      initializer_range=self.initializer_range,      q_groups=self.q_groups,      k_groups=self.k_groups,      v_groups=self.v_groups,      post_attention_groups=self.post_attention_groups,      intermediate_groups=self.intermediate_groups,      output_groups=self.output_groups,      )
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		SqueezeBertModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Dict      		=		model(lowerCamelCase,      lowerCamelCase)
           _lowercase       :							Any      		=		model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		SqueezeBertForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[Any]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.vocab_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		SqueezeBertForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							List[Any]      		=		model(
               lowerCamelCase,      attention_mask=lowerCamelCase,      start_positions=lowerCamelCase,      end_positions=lowerCamelCase)
           self.parent.assertEqual(result.start_logits.shape,      (self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape,      (self.batch_size, self.seq_length))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.num_labels
           _lowercase       :							int      		=		SqueezeBertForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Any      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.num_labels
           _lowercase       :							List[str]      		=		SqueezeBertForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase       :							Union[str, Any]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.num_labels))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		self.num_choices
           _lowercase       :							str      		=		SqueezeBertForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Dict      		=		input_ids.unsqueeze(1).expand(-1,      self.num_choices,      -1).contiguous()
           _lowercase       :							int      		=		input_mask.unsqueeze(1).expand(-1,      self.num_choices,      -1).contiguous()
           _lowercase       :							Optional[Any]      		=		model(
               lowerCamelCase,      attention_mask=lowerCamelCase,      labels=lowerCamelCase,      )
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_choices))




       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.prepare_config_and_inputs()
           ((_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase)		,				(_lowercase))       :							Dict      		=		config_and_inputs
           _lowercase       :							Tuple      		=		{'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	Union[str, Any]     =			(
           (
               SqueezeBertModel,
               SqueezeBertForMaskedLM,
               SqueezeBertForMultipleChoice,
               SqueezeBertForQuestionAnswering,
               SqueezeBertForSequenceClassification,
               SqueezeBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       lowercase_ :	Optional[int]     =			(
           {
               """feature-extraction""": SqueezeBertModel,
               """fill-mask""": SqueezeBertForMaskedLM,
               """question-answering""": SqueezeBertForQuestionAnswering,
               """text-classification""": SqueezeBertForSequenceClassification,
               """token-classification""": SqueezeBertForTokenClassification,
               """zero-shot""": SqueezeBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowercase_ :	Tuple     =			False
       lowercase_ :	List[str]     =			True
       lowercase_ :	int     =			False
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							str      		=		SqueezeBertModelTester(self)
           _lowercase       :							Dict      		=		ConfigTester(self,      config_class=lowerCamelCase,      dim=37)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase)




       @slow
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase       :							List[Any]      		=		SqueezeBertModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_sentencepiece
@require_tokenizers
@require_torch
class _lowerCamelCase(						unittest.TestCase   ):
       @slow
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli')

           _lowercase       :							Optional[int]      		=		torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]])
           _lowercase       :							List[str]      		=		model(lowerCamelCase)[0]
           _lowercase       :							Union[str, Any]      		=		torch.Size((1, 3))
           self.assertEqual(output.shape,      lowerCamelCase)
           _lowercase       :							Tuple      		=		torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]])
           self.assertTrue(torch.allclose(lowerCamelCase,      lowerCamelCase,      atol=1E-4))


